DETAILED ACTION
Notice of AIA Status
The present application is being examined under the AIA the first inventor to file provisions.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/02/2024 and 12/03/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1, 8, 10, and 14-15 are objected to because of the following informalities:
In claim 1, Line 6 the term “diffraction in the thermal camera,” should be changed to “diffraction in the thermal camera;” for typographical/grammar issues.
In claim 8, Line 2 the term “a minimum and a mode” should be changed to “a minimum, and a mode” for typographical/grammar issues.
In claim 10, Line 2 the term “a weighted mean and a mode” should be changed to “a weighted mean, and a mode” for typographical/grammar issues.
In claim 14, Line 8 the term “diffraction in the thermal camera,” should be changed to “diffraction in the thermal camera;” for typographical/grammar issues.
In claim 15, Line 7 the term “diffraction in the thermal camera,” should be changed to “diffraction in the thermal camera;” for typographical/grammar issues.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Claims 14-15, recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 14; recites the limitation, “a processing device” [Line 2].
Claim 15; recites the limitation, “a processing device” [Line 1].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 14-15:
“a processing device” (Fig. 1, #28. Paragraph [0053]- The processing performed by the noise filter 26, the processing device 28 and the image processing pipeline 30 may be implemented in both hardware and software. In a hardware implementation, each of the method steps set out herein may be realized in dedicated circuitry. The circuitry may be in the form of one or more integrated circuits, such as one or more application specific integrated circuits (ASICs) or one or more field-programmable gate arrays (FGPAs). In a software implementation, the circuitry may instead be in the form of a processor, such as a central processing unit or a graphical processing unit, which in association with computer code instructions stored on a (non-transitory) computer-readable medium, such as a non-volatile memory, causes the processing device 28 to carry out the respective processing steps. (wherein processing device has structure associated with it of a dedicated circuitry such as a CPU, GPU, ASIC or FPGA.).).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5-7, and 13-15 are rejected under 35 U.S.C 103 as being unpatentable over Rennies et al. (US 20220187135 A1) hereafter referenced as Rennies in view of Tanaka et al. (US 20250124585 A1) hereafter referenced as Tanaka and Lin et al. (US 20200273152 A1) hereafter referenced as Lin.
Regarding claim 1, Rennies explicitly teaches a method for thermal image processing (Fig. 1, Paragraph [0027]- Rennies discloses it is thus possible to provide an apparatus and method that detects with improved accuracy pixels corresponding to a background of a scene in an image captured by a thermal detector device, for example a thermal imaging camera.),
the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera (Fig. 1, Paragraph [0036]- Rennies discloses a thermal detector device 100 of a thermal imaging apparatus comprises an array of thermal sensing pixels 102 operably coupled to signal processing circuitry 104. In this example, the array of thermal sensing pixels 102 is arranged as a rectangular matrix of M columns of pixels and N rows of pixels. An example of a suitable array of thermal sensing pixels is the MLX90640 far infrared thermal sensor array available from Melexis, Nev.),
wherein the thermal image depicts a scene comprising a set of objects (Fig. 3, Paragraph [0038]- Rennies discloses Infrared electromagnetic radiation emitted or reflected by objects in the scene are received by the array of thermal sensing pixels 102 within a field of view of the thermal detector device 100. In this example, the thermal image 300 captured contains a thermal representation of an object 302, for example a person, and a background 304.);
identifying a set of apparent object regions in the thermal image (Fig. 6, Paragraph [0053]- Rennies discloses once the pixels of the captured image 300 have been classified, it is possible for further processing of the captured image 300 taking into account the pixels classified as the background 600 and the pixels classified as the object 602.),
wherein each apparent object region includes a depiction of a respective one of the set of objects which is blurred due to diffraction in the thermal camera (Fig. 3, Paragraph [0038]- Rennies discloses the thermal detector device 100 is oriented towards a scene in the environment and a thermal image 300 (FIG. 3) is captured (Step 200). Infrared electromagnetic radiation emitted or reflected by objects in the scene are received by the array of thermal sensing pixels 102 within a field of view of the thermal detector device 100. In this example, the thermal image 300 captured contains a thermal representation of an object 302, for example a person, and a background 304 (wherein Fig. 3, shows the edge of the object 302 blurred due to diffraction).),
and wherein each apparent object region is identified as a contiguous region of pixels having pixel intensities differing from a representative background intensity of a thermal background of the scene by more than a threshold intensity (Fig. 2, paragraph [0048]- Rennies discloses once the expected background pixel temperature values, T.sub.Ei, have been calculated (Step 220), the pixel threshold calculator unit 115 then steps through the indices of the first vector and the third vector in the following manner in order to determine a first threshold value, th.sub.h, relating to temperatures greater than a temperature of the background 304 and a second threshold value, th.sub.I, relating to temperatures less than the temperature of the background 304. Further in Fig. 6, paragraph [0053]- Rennies discloses Once the pixels of the captured image 300 have been classified, it is possible for further processing of the captured image 300 taking into account the pixels classified as the background 600 and the pixels classified as the object 602 (wherein Fig. 6, Shows the object pixels 602 as contiguous).),
Rennies fails to explicitly teach and being of a size exceeding a threshold size such that the apparent object region includes an actual object region of at least one actual object pixel and a blurred edge region of blurred edge pixels surrounding the actual object region.
However, Tanaka explicitly teaches and being of a size exceeding a threshold size such that the apparent object region includes an actual object region of at least one actual object pixel and a blurred edge region of blurred edge pixels surrounding the actual object region (Fig. 6, Paragraph [0081]- Tanaka discloses a continuous region in which the variance or standard deviation of pixel values is equal to or less than a set upper limit value, and is a region in which the area included in the region, that is, the number of pixels, is equal to or more than a set threshold value.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies of having a method for thermal image processing, the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Tanaka being of a size exceeding a threshold size such that the apparent object region includes an actual object region of at least one actual object pixel and a blurred edge region of blurred edge pixels surrounding the actual object region.
Wherein having Rennies’ system for processing thermal image data wherein being of a size exceeding a threshold size such that the apparent object region includes an actual object region of at least one actual object pixel and a blurred edge region of blurred edge pixels surrounding the actual object region.
The motivation behind the modification would have been to allow for greater accuracy of separation between background and object pixels, since both Rennies and Tanaka are both systems that improve accuracy of determining background from object pixels. Wherein Rennies’s system wherein improved the accuracy and consistency of background pixel detection, while Tanaka’s system provides a way improve object segmentation accuracy. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Tanaka et al. (US 20250124585 A1) Paragraph [0008].
Rennies in view of Tanaka fails to explicitly teach and applying to each apparent object region a contrast enhancement step comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region; and setting pixels of the object edge region to a representative object intensity determined from one or more actual object pixels of the actual object region, and pixels of the background edge region to the representative background intensity.
However, Lin explicitly teaches and applying to each apparent object region a contrast enhancement step comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region (Fig. 3, Paragraph [0028]- Lin discloses therefore, the processor 240 may define the pixels in regions R2 to R3 in the image 300 as the edge pixels via the distance of two pixels extended outward from the edge of region R1 (i.e., the object) (wherein R2 is the object edge region and R3 is the background edge region).);
and setting pixels of the object edge region to a representative object intensity determined from one or more actual object pixels of the actual object region (Fig. 5, Paragraph [0047]- Lin discloses taking the F6 foreground edge pixel of the plurality of edge pixels as an example, when blurring processing is performed using a 3×3 mask, the background edge pixels B4, B7, and B10 adjacent thereto are first found, and then HSV difference calculation is performed using the foreground edge pixel F6 and the background edge pixels B4, B7, and B10, so that when the HSV difference is found for F2, F4, F6, F8, and F10 and the background edge pixels adjacent thereto, the H or S or V value (that is, the first difference) with the largest difference is taken, and the adjacent two pixels generating the H or S or V value with the largest difference are the aforementioned first pixel and second pixel.),
and pixels of the background edge region to the representative background intensity (Fig. 3, Paragraph [0030]- Lin discloses the mask acting on the edge regions may further include a mask (also referred to as a third mask) for performing blurring processing on the near-edge pixels and a mask (also known as a fourth mask) for performing blurring processing on the far-edge pixels. Further in Fig. 5, Paragraph [0047]- Lin discloses taking the F6 foreground edge pixel of the plurality of edge pixels as an example, when blurring processing is performed using a 3×3 mask, the background edge pixels B4, B7, and B10 adjacent thereto are first found, and then HSV difference calculation is performed using the foreground edge pixel F6 and the background edge pixels B4, B7, and B10, so that when the HSV difference is found for F2, F4, F6, F8, and F10 and the background edge pixels adjacent thereto, the H or S or V value (that is, the first difference) with the largest difference is taken, and the adjacent two pixels generating the H or S or V value with the largest difference are the aforementioned first pixel and second pixel.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies in view of Tanaka of having a method for thermal image processing, the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Lin applying to each apparent object region a contrast enhancement step comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region; and setting pixels of the object edge region to a representative object intensity determined from one or more actual object pixels of the actual object region, and pixels of the background edge region to the representative background intensity.
Wherein having Rennies’ system for processing thermal image data wherein applying to each apparent object region a contrast enhancement step comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region; and setting pixels of the object edge region to a representative object intensity determined from one or more actual object pixels of the actual object region, and pixels of the background edge region to the representative background intensity.
The motivation behind the modification would have been to allow for the creation of a clear edge of an object in a image, since both Rennies and Lin are both systems that improve contrast between object and background regions. Wherein Rennies’s system wherein improved the accuracy and consistency of background pixel detection, while Lin’s system provides a way improve how clear the edge of an object is. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Lin et al. (US 20200273152 A1) Paragraph [0051].
Regarding claim 5, Rennies in view of Tanaka and Lin teaches the method according to claim 1, Rennies further teaches wherein identifying the set of apparent object regions comprises: identifying a set of candidate object regions in the thermal image (Fig. 6, Paragraph [0053]- Rennies discloses once the pixels of the captured image 300 have been classified, it is possible for further processing of the captured image 300 taking into account the pixels classified as the background 600 and the pixels classified as the object 602.),
wherein a candidate object region is identified as a contiguous region of pixels having pixel intensities differing from the representative background intensity by more than the threshold intensity (Fig. 2, paragraph [0048]- Rennies discloses once the expected background pixel temperature values, T.sub.Ei, have been calculated (Step 220), the pixel threshold calculator unit 115 then steps through the indices of the first vector and the third vector in the following manner in order to determine a first threshold value, th.sub.h, relating to temperatures greater than a temperature of the background 304 and a second threshold value, th.sub.I, relating to temperatures less than the temperature of the background 304. Further in Fig. 6, paragraph [0053]- Rennies discloses Once the pixels of the captured image 300 have been classified, it is possible for further processing of the captured image 300 taking into account the pixels classified as the background 600 and the pixels classified as the object 602 (wherein Fig. 6, Shows the object pixels 602 as contiguous).);
Rennies fails to explicitly teach and determining a filtered set of candidate object regions by excluding, from the set of candidate object regions, candidate regions of a size not exceeding the threshold size, wherein the set of apparent object regions is identified as the filtered set of candidate object regions.
However, Tanaka explicitly teaches and determining a filtered set of candidate object regions by excluding, from the set of candidate object regions, candidate regions of a size not exceeding the threshold size (Fig. 6, Paragraph [0081]- Tanaka discloses a continuous region in which the variance or standard deviation of pixel values is equal to or less than a set upper limit value, and is a region in which the area included in the region, that is, the number of pixels, is equal to or more than a set threshold value.),
wherein the set of apparent object regions is identified as the filtered set of candidate object regions (Fig. 6, Paragraph [0081]- flat regions T1, T2, . . . , each of which is a continuous region in which the degree of variation in shading or color is equal to or less than an upper limit level and is a region having an area equal to or greater than a threshold value, are searched in the non-masked captured image P0. Each of the flat regions T1, T2, . . . is, for example, a continuous region in which the variance or standard deviation of pixel values is equal to or less than a set upper limit value, and is a region in which the area included in the region, that is, the number of pixels, is equal to or more than a set threshold value.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies of having a method for thermal image processing, the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Tanaka determining a filtered set of candidate object regions by excluding, from the set of candidate object regions, candidate regions of a size not exceeding the threshold size, wherein the set of apparent object regions is identified as the filtered set of candidate object regions.
Wherein having Rennies’ system for processing thermal image data wherein determining a filtered set of candidate object regions by excluding, from the set of candidate object regions, candidate regions of a size not exceeding the threshold size, wherein the set of apparent object regions is identified as the filtered set of candidate object regions.
The motivation behind the modification would have been to allow for greater accuracy of separation between background and object pixels, since both Rennies and Tanaka are both systems that improve accuracy of determining background from object pixels. Wherein Rennies’s system wherein improved the accuracy and consistency of background pixel detection, while Tanaka’s system provides a way improve object segmentation accuracy. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Tanaka et al. (US 20250124585 A1) Paragraph [0008].
Regarding claim 6, Rennies in view of Tanaka and Lin teaches the method according to claim 1, Rennies further teaches further comprising obtaining a noise level estimate for the thermal camera (Fig. 3, Paragraph [0052]- Rennies discloses the average noise margin can be adjusted by a predetermined amount. In this regard, the margin may be based upon a number of standard deviations from the mean noise margin, for example 6-sigma. The mean noise margin can be based upon a calculation of the overall noise of the apparatus.),
and determining the threshold intensity based on the noise level estimate (Fig. 3, paragraph [0048]- Rennies discloses the temperature with the noise margin taken into account is then recorded as the first threshold value, th.sub.h.).
Regarding claim 7, Rennies in view of Tanaka and Lin teaches the method according to claim 1, Rennies in view of Tanaka fails to explicitly teach wherein each respective pixel of the object edge region is set to a respective representative object intensity determined from one or more actual object pixels of the actual object region closest to the respective pixel of the object edge region.
However, Lin explicitly teaches wherein each respective pixel of the object edge region is set to a respective representative object intensity determined from one or more actual object pixels of the actual object region closest to the respective pixel of the object edge region (Fig. 5, Paragraph [0047]- Lin discloses taking the F6 foreground edge pixel of the plurality of edge pixels as an example, when blurring processing is performed using a 3×3 mask, the background edge pixels B4, B7, and B10 adjacent thereto are first found, and then HSV difference calculation is performed using the foreground edge pixel F6 and the background edge pixels B4, B7, and B10, so that when the HSV difference is found for F2, F4, F6, F8, and F10 and the background edge pixels adjacent thereto, the H or S or V value (that is, the first difference) with the largest difference is taken, and the adjacent two pixels generating the H or S or V value with the largest difference are the aforementioned first pixel and second pixel (wherein Fig. 5 shows the closest pixels being used).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies in view of Tanaka of having a method for thermal image processing, the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Lin wherein each respective pixel of the object edge region is set to a respective representative object intensity determined from one or more actual object pixels of the actual object region closest to the respective pixel of the object edge region.
Wherein having Rennies’ system for processing thermal image data wherein each respective pixel of the object edge region is set to a respective representative object intensity determined from one or more actual object pixels of the actual object region closest to the respective pixel of the object edge region
The motivation behind the modification would have been to allow for the creation of a clear edge of an object in a image, since both Rennies and Lin are both systems that improve contrast between object and background regions. Wherein Rennies’s system wherein improved the accuracy and consistency of background pixel detection, while Lin’s system provides a way improve how clear the edge of an object is. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Lin et al. (US 20200273152 A1) Paragraph [0051].
Regarding claim 13, Rennies in view of Tanaka and Lin teaches the method according to claim 1, Rennies further teaches wherein the thermal image comprises raw thermal image data (Fig. 1, paragraph [0039]- Rennies discloses using the raw intensity data received, the image capture module 106 generates temperature measurement data in respect of each individual pixel 118 of the array of thermal sensing pixels 102. The temperature measurement data generated is stored in the memory 108. As mentioned above, the array of thermal sensing pixels 102 is arranged as an M×N matrix of pixels 118, and in this example the temperature measurement data is stored in the memory 108 in a manner that is indexed by row and column of the array of thermal sensing pixels 102, for example T.sub.m,n, where T is the temperature of the pixel in the m.sup.th column and n.sup.th row.).
Regarding claim 14, Rennies explicitly teaches a computer program product comprising computer program code configured to perform a method when executed by a processing device (Fig. 1, Paragraph [0037]- Rennies discloses the signal processing circuitry 104 also comprises a data store, for example a memory 108, such as a Random Access Memory (RAM), a background identifier unit 110 and a pixel classifier unit 112.),
the method for thermal image processing comprising: obtaining a thermal image acquired by an image sensor of a thermal camera (Fig. 1, Paragraph [0036]- Rennies discloses a thermal detector device 100 of a thermal imaging apparatus comprises an array of thermal sensing pixels 102 operably coupled to signal processing circuitry 104. In this example, the array of thermal sensing pixels 102 is arranged as a rectangular matrix of M columns of pixels and N rows of pixels. An example of a suitable array of thermal sensing pixels is the MLX90640 far infrared thermal sensor array available from Melexis, Nev.),
wherein the thermal image depicts a scene comprising a set of objects (Fig. 3, Paragraph [0038]- Rennies discloses Infrared electromagnetic radiation emitted or reflected by objects in the scene are received by the array of thermal sensing pixels 102 within a field of view of the thermal detector device 100. In this example, the thermal image 300 captured contains a thermal representation of an object 302, for example a person, and a background 304.);
identifying a set of apparent object regions in the thermal image (Fig. 6, Paragraph [0053]- Rennies discloses once the pixels of the captured image 300 have been classified, it is possible for further processing of the captured image 300 taking into account the pixels classified as the background 600 and the pixels classified as the object 602.),
wherein each apparent object region includes a depiction of a respective one of the set of objects which is blurred due to diffraction in the thermal camera (Fig. 3, Paragraph [0038]- Rennies discloses the thermal detector device 100 is oriented towards a scene in the environment and a thermal image 300 (FIG. 3) is captured (Step 200). Infrared electromagnetic radiation emitted or reflected by objects in the scene are received by the array of thermal sensing pixels 102 within a field of view of the thermal detector device 100. In this example, the thermal image 300 captured contains a thermal representation of an object 302, for example a person, and a background 304 (wherein Fig. 3, shows the edge of the object 302 blurred due to diffraction).),
and wherein each apparent object region is identified as a contiguous region of pixels having pixel intensities differing from a representative background intensity of a thermal background of the scene by more than a threshold intensity (Fig. 2, paragraph [0048]- Rennies discloses once the expected background pixel temperature values, T.sub.Ei, have been calculated (Step 220), the pixel threshold calculator unit 115 then steps through the indices of the first vector and the third vector in the following manner in order to determine a first threshold value, th.sub.h, relating to temperatures greater than a temperature of the background 304 and a second threshold value, th.sub.I, relating to temperatures less than the temperature of the background 304. Further in Fig. 6, paragraph [0053]- Rennies discloses Once the pixels of the captured image 300 have been classified, it is possible for further processing of the captured image 300 taking into account the pixels classified as the background 600 and the pixels classified as the object 602 (wherein Fig. 6, Shows the object pixels 602 as contiguous).),
Rennies fails to explicitly teach and being of a size exceeding a threshold size such that the apparent object region includes an actual object region of at least one actual object pixel and a blurred edge region of blurred edge pixels surrounding the actual object region.
However, Tanaka explicitly teaches and being of a size exceeding a threshold size such that the apparent object region includes an actual object region of at least one actual object pixel and a blurred edge region of blurred edge pixels surrounding the actual object region (Fig. 6, Paragraph [0081]- Tanaka discloses a continuous region in which the variance or standard deviation of pixel values is equal to or less than a set upper limit value, and is a region in which the area included in the region, that is, the number of pixels, is equal to or more than a set threshold value.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies of having a computer program product comprising computer program code configured to perform a method when executed by a processing device, the method for thermal image processing comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Tanaka being of a size exceeding a threshold size such that the apparent object region includes an actual object region of at least one actual object pixel and a blurred edge region of blurred edge pixels surrounding the actual object region.
Wherein having Rennies’ system for processing thermal image data wherein being of a size exceeding a threshold size such that the apparent object region includes an actual object region of at least one actual object pixel and a blurred edge region of blurred edge pixels surrounding the actual object region.
The motivation behind the modification would have been to allow for greater accuracy of separation between background and object pixels, since both Rennies and Tanaka are both systems that improve accuracy of determining background from object pixels. Wherein Rennies’s system wherein improved the accuracy and consistency of background pixel detection, while Tanaka’s system provides a way improve object segmentation accuracy. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Tanaka et al. (US 20250124585 A1) Paragraph [0008].
Rennies in view of Tanaka fails to explicitly teach and applying to each apparent object region a contrast enhancement step comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region; and setting pixels of the object edge region to a representative object intensity determined from one or more actual object pixels of the actual object region, and pixels of the background edge region to the representative background intensity.
However, Lin explicitly teaches and applying to each apparent object region a contrast enhancement step comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region (Fig. 3, Paragraph [0028]- Lin discloses therefore, the processor 240 may define the pixels in regions R2 to R3 in the image 300 as the edge pixels via the distance of two pixels extended outward from the edge of region R1 (i.e., the object) (wherein R2 is the object edge region and R3 is the background edge region).);
and setting pixels of the object edge region to a representative object intensity determined from one or more actual object pixels of the actual object region (Fig. 5, Paragraph [0047]- Lin discloses taking the F6 foreground edge pixel of the plurality of edge pixels as an example, when blurring processing is performed using a 3×3 mask, the background edge pixels B4, B7, and B10 adjacent thereto are first found, and then HSV difference calculation is performed using the foreground edge pixel F6 and the background edge pixels B4, B7, and B10, so that when the HSV difference is found for F2, F4, F6, F8, and F10 and the background edge pixels adjacent thereto, the H or S or V value (that is, the first difference) with the largest difference is taken, and the adjacent two pixels generating the H or S or V value with the largest difference are the aforementioned first pixel and second pixel.),
and pixels of the background edge region to the representative background intensity (Fig. 3, Paragraph [0030]- Lin discloses the mask acting on the edge regions may further include a mask (also referred to as a third mask) for performing blurring processing on the near-edge pixels and a mask (also known as a fourth mask) for performing blurring processing on the far-edge pixels. Further in Fig. 5, Paragraph [0047]- Lin discloses taking the F6 foreground edge pixel of the plurality of edge pixels as an example, when blurring processing is performed using a 3×3 mask, the background edge pixels B4, B7, and B10 adjacent thereto are first found, and then HSV difference calculation is performed using the foreground edge pixel F6 and the background edge pixels B4, B7, and B10, so that when the HSV difference is found for F2, F4, F6, F8, and F10 and the background edge pixels adjacent thereto, the H or S or V value (that is, the first difference) with the largest difference is taken, and the adjacent two pixels generating the H or S or V value with the largest difference are the aforementioned first pixel and second pixel.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies in view of Tanaka of having a computer program product comprising computer program code configured to perform a method when executed by a processing device, the method for thermal image processing comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Lin applying to each apparent object region a contrast enhancement step comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region; and setting pixels of the object edge region to a representative object intensity determined from one or more actual object pixels of the actual object region, and pixels of the background edge region to the representative background intensity.
Wherein having Rennies’ system for processing thermal image data wherein applying to each apparent object region a contrast enhancement step comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region; and setting pixels of the object edge region to a representative object intensity determined from one or more actual object pixels of the actual object region, and pixels of the background edge region to the representative background intensity.
The motivation behind the modification would have been to allow for the creation of a clear edge of an object in a image, since both Rennies and Lin are both systems that improve contrast between object and background regions. Wherein Rennies’s system wherein improved the accuracy and consistency of background pixel detection, while Lin’s system provides a way improve how clear the edge of an object is. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Lin et al. (US 20200273152 A1) Paragraph [0051].
Regarding claim 15, Rennies explicitly teaches a thermal camera comprising a processing device configured to perform a method for thermal image processing (Fig. 1, Paragraph [0027]- Rennies discloses it is thus possible to provide an apparatus and method that detects with improved accuracy pixels corresponding to a background of a scene in an image captured by a thermal detector device, for example a thermal imaging camera.),
the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera (Fig. 1, Paragraph [0036]- Rennies discloses a thermal detector device 100 of a thermal imaging apparatus comprises an array of thermal sensing pixels 102 operably coupled to signal processing circuitry 104. In this example, the array of thermal sensing pixels 102 is arranged as a rectangular matrix of M columns of pixels and N rows of pixels. An example of a suitable array of thermal sensing pixels is the MLX90640 far infrared thermal sensor array available from Melexis, Nev.),
wherein the thermal image depicts a scene comprising a set of objects (Fig. 3, Paragraph [0038]- Rennies discloses Infrared electromagnetic radiation emitted or reflected by objects in the scene are received by the array of thermal sensing pixels 102 within a field of view of the thermal detector device 100. In this example, the thermal image 300 captured contains a thermal representation of an object 302, for example a person, and a background 304.);
identifying a set of apparent object regions in the thermal image(Fig. 6, Paragraph [0053]- Rennies discloses once the pixels of the captured image 300 have been classified, it is possible for further processing of the captured image 300 taking into account the pixels classified as the background 600 and the pixels classified as the object 602.),
wherein each apparent object region includes a depiction of a respective one of the set of objects which is blurred due to diffraction in the thermal camera (Fig. 3, Paragraph [0038]- Rennies discloses the thermal detector device 100 is oriented towards a scene in the environment and a thermal image 300 (FIG. 3) is captured (Step 200). Infrared electromagnetic radiation emitted or reflected by objects in the scene are received by the array of thermal sensing pixels 102 within a field of view of the thermal detector device 100. In this example, the thermal image 300 captured contains a thermal representation of an object 302, for example a person, and a background 304 (wherein Fig. 3, shows the edge of the object 302 blurred due to diffraction).),
and wherein each apparent object region is identified as a contiguous region of pixels having pixel intensities differing from a representative background intensity of a thermal background of the scene by more than a threshold intensity (Fig. 2, paragraph [0048]- Rennies discloses once the expected background pixel temperature values, T.sub.Ei, have been calculated (Step 220), the pixel threshold calculator unit 115 then steps through the indices of the first vector and the third vector in the following manner in order to determine a first threshold value, th.sub.h, relating to temperatures greater than a temperature of the background 304 and a second threshold value, th.sub.I, relating to temperatures less than the temperature of the background 304. Further in Fig. 6, paragraph [0053]- Rennies discloses Once the pixels of the captured image 300 have been classified, it is possible for further processing of the captured image 300 taking into account the pixels classified as the background 600 and the pixels classified as the object 602 (wherein Fig. 6, Shows the object pixels 602 as contiguous).),
Rennies fails to explicitly teach and being of a size exceeding a threshold size such that the apparent object region includes an actual object region of at least one actual object pixel and a blurred edge region of blurred edge pixels surrounding the actual object region.
However, Tanaka explicitly teaches and being of a size exceeding a threshold size such that the apparent object region includes an actual object region of at least one actual object pixel and a blurred edge region of blurred edge pixels surrounding the actual object region (Fig. 6, Paragraph [0081]- Tanaka discloses a continuous region in which the variance or standard deviation of pixel values is equal to or less than a set upper limit value, and is a region in which the area included in the region, that is, the number of pixels, is equal to or more than a set threshold value.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies of having a thermal camera comprising a processing device configured to perform a method for thermal image processing, the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Tanaka being of a size exceeding a threshold size such that the apparent object region includes an actual object region of at least one actual object pixel and a blurred edge region of blurred edge pixels surrounding the actual object region.
Wherein having Rennies’ system for processing thermal image data wherein being of a size exceeding a threshold size such that the apparent object region includes an actual object region of at least one actual object pixel and a blurred edge region of blurred edge pixels surrounding the actual object region.
The motivation behind the modification would have been to allow for greater accuracy of separation between background and object pixels, since both Rennies and Tanaka are both systems that improve accuracy of determining background from object pixels. Wherein Rennies’s system wherein improved the accuracy and consistency of background pixel detection, while Tanaka’s system provides a way improve object segmentation accuracy. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Tanaka et al. (US 20250124585 A1) Paragraph [0008].
Rennies in view of Tanaka fails to explicitly teach and applying to each apparent object region a contrast enhancement step comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region; and setting pixels of the object edge region to a representative object intensity determined from one or more actual object pixels of the actual object region, and pixels of the background edge region to the representative background intensity.
However, Lin explicitly teaches and applying to each apparent object region a contrast enhancement step comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region (Fig. 3, Paragraph [0028]- Lin discloses therefore, the processor 240 may define the pixels in regions R2 to R3 in the image 300 as the edge pixels via the distance of two pixels extended outward from the edge of region R1 (i.e., the object) (wherein R2 is the object edge region and R3 is the background edge region).);
and setting pixels of the object edge region to a representative object intensity determined from one or more actual object pixels of the actual object region (Fig. 5, Paragraph [0047]- Lin discloses taking the F6 foreground edge pixel of the plurality of edge pixels as an example, when blurring processing is performed using a 3×3 mask, the background edge pixels B4, B7, and B10 adjacent thereto are first found, and then HSV difference calculation is performed using the foreground edge pixel F6 and the background edge pixels B4, B7, and B10, so that when the HSV difference is found for F2, F4, F6, F8, and F10 and the background edge pixels adjacent thereto, the H or S or V value (that is, the first difference) with the largest difference is taken, and the adjacent two pixels generating the H or S or V value with the largest difference are the aforementioned first pixel and second pixel.),
and pixels of the background edge region to the representative background intensity (Fig. 3, Paragraph [0030]- Lin discloses the mask acting on the edge regions may further include a mask (also referred to as a third mask) for performing blurring processing on the near-edge pixels and a mask (also known as a fourth mask) for performing blurring processing on the far-edge pixels. Further in Fig. 5, Paragraph [0047]- Lin discloses taking the F6 foreground edge pixel of the plurality of edge pixels as an example, when blurring processing is performed using a 3×3 mask, the background edge pixels B4, B7, and B10 adjacent thereto are first found, and then HSV difference calculation is performed using the foreground edge pixel F6 and the background edge pixels B4, B7, and B10, so that when the HSV difference is found for F2, F4, F6, F8, and F10 and the background edge pixels adjacent thereto, the H or S or V value (that is, the first difference) with the largest difference is taken, and the adjacent two pixels generating the H or S or V value with the largest difference are the aforementioned first pixel and second pixel.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies in view of Tanaka of having a thermal camera comprising a processing device configured to perform a method for thermal image processing, the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Lin applying to each apparent object region a contrast enhancement step comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region; and setting pixels of the object edge region to a representative object intensity determined from one or more actual object pixels of the actual object region, and pixels of the background edge region to the representative background intensity.
Wherein having Rennies’ system for processing thermal image data wherein applying to each apparent object region a contrast enhancement step comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region; and setting pixels of the object edge region to a representative object intensity determined from one or more actual object pixels of the actual object region, and pixels of the background edge region to the representative background intensity.
The motivation behind the modification would have been to allow for the creation of a clear edge of an object in a image, since both Rennies and Lin are both systems that improve contrast between object and background regions. Wherein Rennies’s system wherein improved the accuracy and consistency of background pixel detection, while Lin’s system provides a way improve how clear the edge of an object is. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Lin et al. (US 20200273152 A1) Paragraph [0051].
Claim 8 is rejected under 35 U.S.C 103 as being unpatentable over Rennies et al. (US 20220187135 A1) hereafter referenced as Rennies in view of Tanaka et al. (US 20250124585 A1) hereafter referenced as Tanaka, Lin et al. (US 20200273152 A1) hereafter referenced as Lin, and Miyamoto et al. (US 20110115785 A1) hereafter referenced as Miyamoto.
Regarding claim 8, Rennies in view of Tanaka and Lin explicitly teaches the method according to claim 1, Rennies in view of Tanaka and Lin fails to explicitly teach wherein the representative object intensity is determined as one of: a mean, a median, a maximum, a minimum and a mode of the one or more actual object pixels.
However, Miyamoto explicitly teaches wherein the representative object intensity is determined as one of: a mean(Fig. 1, Paragraph [0026]- Miyamoto discloses here, specific examples of the "value calculated by the predetermined method" for the replacement may include maximum value, minimum value, average value, median value, and mode value of pixel values of pixels in the intended setting area, class value of the highest frequency class in a pixel value histogram of each pixel in the intended setting area, maximum value, minimum value, average value, and median value of pixel values of adjacent pixels in the intended setting area adjacent to the search point with respect to the adjacent pixel in the unintended setting area, and the like.),
a median(Fig. 1, Paragraph [0026]- Miyamoto discloses here, specific examples of the "value calculated by the predetermined method" for the replacement may include maximum value, minimum value, average value, median value, and mode value of pixel values of pixels in the intended setting area, class value of the highest frequency class in a pixel value histogram of each pixel in the intended setting area, maximum value, minimum value, average value, and median value of pixel values of adjacent pixels in the intended setting area adjacent to the search point with respect to the adjacent pixel in the unintended setting area, and the like.),
a maximum(Fig. 1, Paragraph [0026]- Miyamoto discloses here, specific examples of the "value calculated by the predetermined method" for the replacement may include maximum value, minimum value, average value, median value, and mode value of pixel values of pixels in the intended setting area, class value of the highest frequency class in a pixel value histogram of each pixel in the intended setting area, maximum value, minimum value, average value, and median value of pixel values of adjacent pixels in the intended setting area adjacent to the search point with respect to the adjacent pixel in the unintended setting area, and the like.),
a minimum(Fig. 1, Paragraph [0026]- Miyamoto discloses here, specific examples of the "value calculated by the predetermined method" for the replacement may include maximum value, minimum value, average value, median value, and mode value of pixel values of pixels in the intended setting area, class value of the highest frequency class in a pixel value histogram of each pixel in the intended setting area, maximum value, minimum value, average value, and median value of pixel values of adjacent pixels in the intended setting area adjacent to the search point with respect to the adjacent pixel in the unintended setting area, and the like.)
and a mode of the one or more actual object pixels (Fig. 1, Paragraph [0026]- Miyamoto discloses here, specific examples of the "value calculated by the predetermined method" for the replacement may include maximum value, minimum value, average value, median value, and mode value of pixel values of pixels in the intended setting area, class value of the highest frequency class in a pixel value histogram of each pixel in the intended setting area, maximum value, minimum value, average value, and median value of pixel values of adjacent pixels in the intended setting area adjacent to the search point with respect to the adjacent pixel in the unintended setting area, and the like.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies in view of Tanaka and Lin of having a method for thermal image processing, the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Miyamoto wherein the representative object intensity is determined as one of: a mean, a median, a maximum, a minimum and a mode of the one or more actual object pixels.
Wherein having Rennies’ system for processing thermal image data wherein the representative object intensity is determined as one of: a mean, a median, a maximum, a minimum and a mode of the one or more actual object pixels.
The motivation behind the modification would have been to allow for improved artifact prevention, since both Rennies and Miyamoto are both systems that manipulate the value of pixels. Wherein Rennies’s system wherein improved the accuracy and consistency of background pixel detection, while Miyamoto’s system provides a way reduce the number of artifacts. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Miyamoto et al. (US 20110115785 A1) Paragraph [0028-29].
Claims 9 and 11-12 are rejected under 35 U.S.C 103 as being unpatentable over Rennies et al. (US 20220187135 A1) hereafter referenced as Rennies in view of Tanaka et al. (US 20250124585 A1) hereafter referenced as Tanaka, Lin et al. (US 20200273152 A1) hereafter referenced as Lin, and Paul et al. (US 20220094896 A1) hereafter referenced as Paul.
Regarding claim 9, Rennies in view of Tanaka and Lin explicitly teaches the method according to claim 1,
Rennies in view of Tanaka and Lin fails to explicitly teach further comprising obtaining a frequency distribution of pixel values of the thermal image, wherein the representative background intensity is determined as a representative pixel intensity of at least a portion of the frequency distribution.
However, Paul explicitly teaches further comprising obtaining a frequency distribution of pixel values of the thermal image (Fig. 3, Paragraph [0033]- Paul discloses the thermal image may be received from thermal imaging subsystem 202, for example. At 404, method 400 comprises generating a histogram via binning pixels by intensity level. In some examples, at 406, the method comprises applying a local contrast enhancement algorithm on the thermal image thereby modifying intensity levels of the pixels, and creating the histogram via binning the pixels by modified intensity level.),
wherein the representative background intensity is determined as a representative pixel intensity of at least a portion of the frequency distribution (Fig. 3, Paragraph [0024]- Paul discloses for example, a global maximum 308 may be identified in the histogram and used to set threshold intensity level 306.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies in view of Tanaka, Lin, and Paul of having a method for thermal image processing, the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Paul further comprising obtaining a frequency distribution of pixel values of the thermal image, wherein the representative background intensity is determined as a representative pixel intensity of at least a portion of the frequency distribution.
Wherein having Rennies’ system for processing thermal image data wherein further comprising obtaining a frequency distribution of pixel values of the thermal image, wherein the representative background intensity is determined as a representative pixel intensity of at least a portion of the frequency distribution.
The motivation behind the modification would have been to allow for improved visualization of the thermal data, since both Rennies and Paul are both systems that process thermal images. Wherein Rennies’ system wherein improved the accuracy and consistency of background pixel detection, while Pauls’s system improved visualization of the thermal data obtained. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Paul et al. (US 20220094896 A1) Paragraph [0016].
Regarding claim 11, Rennies in view of Tanaka, Lin, and Paul explicitly teaches the method according to claim 9, Rennies in view of Tanaka and Lin fails to explicitly teach further comprising identifying at least a first peak region in the frequency distribution, wherein the representative pixel intensity is determined from pixel values within the first peak region.
However, Paul explicitly teaches further comprising identifying at least a first peak region in the frequency distribution (Fig. 3, Paragraph [0023]- Paul discloses FIG. 3 illustrates a threshold applied to a histogram which allows for relatively “hotter” pixels to be selectively colorized. In this example, a subset of pixels 304 to be colorized is determined based on the pixels being above threshold intensity level 306 (wherein Fig. 3 Shows a first peak region).),
wherein the representative pixel intensity is determined from pixel values within the first peak region (Fig. 3, Paragraph [0024]- Paul discloses additionally or alternatively, threshold intensity level 306 may be chosen based on pixel count, e.g., a global or local maximum of histogram 300. For example, a global maximum 308 may be identified in the histogram and used to set threshold intensity level 306.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies in view of Tanaka, Lin, and Paul of having a method for thermal image processing, the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Paul further comprising identifying at least a first peak region in the frequency distribution, wherein the representative pixel intensity is determined from pixel values within the first peak region.
Wherein having Rennies’ system for processing thermal image data wherein further comprising identifying at least a first peak region in the frequency distribution, wherein the representative pixel intensity is determined from pixel values within the first peak region.
The motivation behind the modification would have been to allow for improved visualization of the thermal data, since both Rennies and Paul are both systems that process thermal images. Wherein Rennies’ system wherein improved the accuracy and consistency of background pixel detection, while Pauls’s system improved visualization of the thermal data obtained. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Paul et al. (US 20220094896 A1) Paragraph [0016].
Regarding claim 12, Rennies in view of Tanaka, Lin, and Paul explicitly teaches
The method according to claim 11,
Rennies in view of Tanaka and Lin fails to explicitly teach further comprising identifying a second peak region in the frequency distribution, wherein the representative pixel intensity is determined from the pixel values within the first peak region but not pixel values within the second peak region.
However, Paul further teaches further comprising identifying a second peak region in the frequency distribution (Fig. 3, Paragraph [0023]- Paul discloses FIG. 3 illustrates a threshold applied to a histogram which allows for relatively “hotter” pixels to be selectively colorized. In this example, a subset of pixels 304 to be colorized is determined based on the pixels being above threshold intensity level 306 (wherein Fig. 3 Shows a second peak region).),
wherein the representative pixel intensity is determined from the pixel values within the first peak region but not pixel values within the second peak region (Fig. 3, Paragraph [0024]- Paul discloses threshold intensity level 306 is a lower-bound threshold condition, and the subset of pixels 304 corresponds to pixels with intensity levels equal to or greater than threshold intensity level 304 (wherein Fig. 3, Shows section 304 in only the second peak region).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies in view of Tanaka, Lin, and Paul of having a method for thermal image processing, the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Paul further comprising identifying a second peak region in the frequency distribution, wherein the representative pixel intensity is determined from the pixel values within the first peak region but not pixel values within the second peak region.
Wherein having Rennies’ system for processing thermal image data wherein further comprising identifying a second peak region in the frequency distribution, wherein the representative pixel intensity is determined from the pixel values within the first peak region but not pixel values within the second peak region.
The motivation behind the modification would have been to allow for improved visualization of the thermal data, since both Rennies and Paul are both systems that process thermal images. Wherein Rennies’ system wherein improved the accuracy and consistency of background pixel detection, while Pauls’s system improved visualization of the thermal data obtained. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Paul et al. (US 20220094896 A1) Paragraph [0016].
Claim 10 is rejected under 35 U.S.C 103 as being unpatentable over Rennies et al. (US 20220187135 A1) hereafter referenced as Rennies in view of Tanaka et al. (US 20250124585 A1) hereafter referenced as Tanaka, Lin et al. (US 20200273152 A1) hereafter referenced as Lin, Paul et al. (US 20220094896 A1) hereafter referenced as Paul, and Miyamoto et al. (US 20110115785 A1) hereafter referenced as Miyamoto.
Regarding claim 10, Rennies in view of Tanaka, Lin, and Paul teaches the method according to claim 9,
Rennies in view of Tanaka, Lin, and Paul teaches fails to explicitly teach wherein the representative pixel intensity is one of: a mean, a median, a weighted mean and a mode of the at least a portion of the frequency distribution.
However, Miyamoto explicitly teaches wherein the representative pixel intensity is one of: a mean (Fig. 1, Paragraph [0026]- Miyamoto discloses here, specific examples of the "value calculated by the predetermined method" for the replacement may include maximum value, minimum value, average value, median value, and mode value of pixel values of pixels in the intended setting area, class value of the highest frequency class in a pixel value histogram of each pixel in the intended setting area, maximum value, minimum value, average value, and median value of pixel values of adjacent pixels in the intended setting area adjacent to the search point with respect to the adjacent pixel in the unintended setting area, and the like.),
a median (Fig. 1, Paragraph [0026]- Miyamoto discloses here, specific examples of the "value calculated by the predetermined method" for the replacement may include maximum value, minimum value, average value, median value, and mode value of pixel values of pixels in the intended setting area, class value of the highest frequency class in a pixel value histogram of each pixel in the intended setting area, maximum value, minimum value, average value, and median value of pixel values of adjacent pixels in the intended setting area adjacent to the search point with respect to the adjacent pixel in the unintended setting area, and the like.),
a weighted mean (Fig. 1, Paragraph [0026]- Miyamoto discloses here, specific examples of the "value calculated by the predetermined method" for the replacement may include maximum value, minimum value, average value, median value, and mode value of pixel values of pixels in the intended setting area, class value of the highest frequency class in a pixel value histogram of each pixel in the intended setting area, maximum value, minimum value, average value, and median value of pixel values of adjacent pixels in the intended setting area adjacent to the search point with respect to the adjacent pixel in the unintended setting area, and the like.)
and a mode of the at least a portion of the frequency distribution (Fig. 1, Paragraph [0026]- Miyamoto discloses here, specific examples of the "value calculated by the predetermined method" for the replacement may include maximum value, minimum value, average value, median value, and mode value of pixel values of pixels in the intended setting area, class value of the highest frequency class in a pixel value histogram of each pixel in the intended setting area, maximum value, minimum value, average value, and median value of pixel values of adjacent pixels in the intended setting area adjacent to the search point with respect to the adjacent pixel in the unintended setting area, and the like.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Rennies in view of Tanaka, Lin, and Paul of having a method for thermal image processing, the method comprising: obtaining a thermal image acquired by an image sensor of a thermal camera, wherein the thermal image depicts a scene comprising a set of objects with the teachings of Miyamoto wherein the representative pixel intensity is one of: a mean, a median, a weighted mean and a mode of the at least a portion of the frequency distribution.
Wherein having Rennies’ system for processing thermal image data wherein the representative pixel intensity is one of: a mean, a median, a weighted mean and a mode of the at least a portion of the frequency distribution.
The motivation behind the modification would have been to allow for improved artifact prevention, since both Rennies and Miyamoto are both systems that manipulate the value of pixels. Wherein Rennies’s system wherein improved the accuracy and consistency of background pixel detection, while Miyamoto’s system provides a way reduce the number of artifacts. Please see Rennies et al. (US 20220187135 A1), Paragraph [0027] and Miyamoto et al. (US 20110115785 A1) Paragraph [0028-29].
Allowable Subject Matter
Claim 2 along with its dependent claims 3-4 respectively, are therefrom objected to as being dependent upon rejected base claim, claims 1, respectively but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 2, the prior arts fail to explicitly teach, designating pixels of the blurred edge region located within a predetermined fraction of said distance from the boundary of the actual object region as pixels of the object edge region, and pixels of the blurred edge region located outside the predetermined fraction of said distance from the boundary of the actual object region as pixels of the background edge region, as claimed in claim 2.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered
pertinent to applicant`s disclosure.
YACHIDA et al. (US 20220036046 A1)- An image processing device according to one aspect of the present disclosure includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: receive a visible image of a face; receive a near-infrared image of the face; adjust brightness of the visible image based on a frequency distribution of pixel values of the visible image and a frequency distribution of pixel values of the near-infrared image; specify a relative position at which the visible image is related to the near-infrared image; invert adjusted brightness of the visible image; detect a region of a pupil from a synthetic image obtained by adding up the visible image the brightness of which is inverted and the near-infrared image based on the relative position; and output information on the detected pupil....................Please see Fig. 1. Abstract.
Ebenstein et al. (US 20080197284 A1)- An object detection system is disclosed in at least one embodiment. The system includes a far IR sensor operable to sense thermal radiation of objects and surroundings in a field of view and to generate a far IR image in response thereto, and an image processing device operable to receive and process the far IR image to detect the presence of one or more objects in the field of view. The image processing device can be configured to process the far IR image by generating an initial threshold image based on the far IR image and an initial threshold value, iteratively obtaining a number of successive threshold images based on the far IR image and a number of successively increased threshold values, and determining the presence of one or more objects in the field of view based on the threshold images and threshold values.....................Please see Fig. 1. Abstract.
Taguchi et al. (US 20170091944 A1)- A memory stores a first image of an object in an image-capturing target region and a second image, the first image being captured by a first imaging device, and the second image being captured by a second imaging device by use of a reflected electromagnetic wave from the image-capturing target region, using an electromagnetic source that radiates an electromagnetic wave onto the image-capturing target region. When a position of a strongly reflective region in the second image corresponds to a prescribed position in the second image, a processor estimates a position of the object on the basis of the first image and complementary information that complements an image of the strongly reflective region.......................Please see Fig. 1. Abstract.
Feng et al. (US 20220385873 A1)- A multi-point measurement of a scene captured in an image frame may be used to process the image frame, such as by applying white balance corrections to the image frame. In some examples, the image frame may be segmented into portions that are illuminated by different illumination sources. Different portions of the image frame may be white balanced differently based on the color temperature of the illumination source for the corresponding portion. Infrared measurements of multiple points in the scene may be used to determine a characteristic of the illumination source of different portions of the scene. For example, a picture that includes indoor and outdoor portions may be illuminated by at least two illumination sources that produce different infrared measurements values. White balancing may be applied differently to these two portions to correct for color temperature of the different sources........................Please see Fig. 1. Abstract.
Usikov et al. (US 20190279371 A1)- Various aspects of an image-processing apparatus and method for object boundary stabilization in an image of a sequence of image frames are disclosed. The image-processing apparatus includes an image processor that receives a depth image of a scene from a first-type of sensor and a color image of the scene from the second-type of sensor. The scene may comprise at least an object-of-interest. A first object mask of the object-of-interest is generated by a depth thresholding operation on the received depth image. Dangling-pixels artifact present on a first object boundary of the first object mask, are removed. The first object boundary is smoothened using a moving-template filter on the color image. A second object mask having a second object boundary is generated based on the smoothening of the first object boundary. The object-of-interest from the color image is extracted based on the generated second object mask........................Please see Fig. 1. Abstract.
Pelz et al. (US 7324143 B1)- A method for reducing noise in a digital image includes providing a digital image comprising a plurality of channels with each of the channels comprising a set of pixel data signals and applying a filter to each of the sets of pixel data signals, where the filter applied to at least one of the sets of pixel data signals is different from the filter applied to another one of the sets of pixel data signals. An imaging system which reduce noise in a digital image includes an image sensor apparatus and a filter system. The image sensor apparatus captures a digital image comprising a plurality of channels with each of the channels comprising a set of pixel data signals. The filter system comprises at least two different filters with each of the filters filtering at least one of the sets of pixel data signals for one of the channels. The filter applied to at least one of the sets of pixel data signals is different from the filter applied to another one of the sets of pixel data signals.........................Please see Fig. 1. Abstract.
Fitzpatrick et al. (US 7860344 B1)- Improved apparatus and methodology for image processing and object tracking that, inter alia, reduces noise. In one embodiment, the methodology is applied to moving targets such as missiles in flight, and comprises processing sequences of images that have been corrupted by one or more noise sources (e.g., sensor noise, medium noise, and/or target reflection noise). In this embodiment, a multi-dimensional image is acquired for a first time step t; the acquired image is normalized and sampled, and then segmented into target and background pixel sets. Intensity statistics of the pixel sets are determined, and a prior probability image from a previous time step smoothed. The smoothed prior image is then shifted to produce an updated prior image, and a posterior probability image calculated using the updated prior probability. Finally, the position of the target is extracted using the posterior probability image. A tracking system and controller utilizing this methodology are also disclosed........................Please see Fig. 1. Abstract
Schulte et al. (US 20180300884 A1)- An infrared (IR) imaging module may capture a background image in response to receiving IR radiation from a background of a scene and determine background calibration terms using the background image. The determined background calibration terms may be scale factors and/or offsets that equalize the pixel values of the background image to a baseline, value. IR imaging device may use the background calibration terms to capture images that have the baseline value for pixels corresponding to IR radiation received from the background and higher values (or lower values) for pixels corresponding to IR radiation received from a foreground. Such images may be used to count people and generate a heat map. The background calibration terms may be updated periodically, with the update period being increased at least for some pixels or a pixel area when a person is detected........................Please see Fig. 1. Abstract
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/LUCIUS CAMERON GREEN ALLEN/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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