Method of matching stereo images and method of measuring disparity between these items
First Claim
1. A method of matching stereo images, comprising the steps of:
- inputting first and second images IL and IR;
developing said images IL and IR into a plurality of frequency component images FL1, FL2, FL3 . . . , FLk, FLk+1, . . . , FLn and a plurality of frequency component images FR1, FR2, FR3, . . . , FRk, FRk+1, . . . , FRn, respectively;
applying a secondary differential processing to each of said frequency component images;
converting each frequency component image, after being applied the secondary differential processing, into ternary values pixel by pixel, thereby obtaining ternary-valued frequency component images TL1, TL2, TL3, . . . , TLk, TLk+1, . . . , TLn and ternary-valued frequency component images TR1, TR2, TR3 . . . TRk, TRk+1, . . . , TRn; and
performing a matching operation between said first and second images based on said ternary-valued frequency component images,wherein pixels in a one-dimensional window of the ternary-valued frequency component image TLk of said first image IL are compared in a one-to-one manner with pixels in a designated region of the ternary-valued frequency component image TRk of said second image IR, when said ternary-valued frequency component images TLk and TRk are identical in their frequency components,an evaluation raesult "P" is obtained when corresponding two pixels are both "p" or "m", while an evaluation result "Z" is obtained when the corresponding two pixels are both "z", anda similarity between two ternary-valued frequency component images TLk and TRk is evaluated by using the following equation;
space="preserve" listing-type="equation">Eall=Σ
β
k(PN)k+Σ
γ
k(ZN)kwhere PN represents a total number of pixels having the evaluation result "P", ZN represents a total number of pixels having the evaluation result "Z", and β
k and γ
k represent weighing factors.
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Abstract
In the image pickup phase (A), right and left images are taken in through two image-pickup devices (S101, S102). Then, in the next feature extraction phase (B), right and left images are respectively subjected to feature extraction (S103, S104). Thereafter, in the succeeding matching phase (C), the extracted features of right and left images are compared to check how they match with each other (step S105). More specifically, in the matching phase (C), a one-dimensional window is set, this one-dimensional window is shifted along the left image in accordance with a predetermined scanning rule so as to successively set overlapped one-dimensional windows, and a matching operation is performed by comparing the image features within one window and corresponding image features on the right image. Subsequently, in the disparity determination phase (D), the left image is dissected or divided into plural blocks each having a predetermined size, a histogram in each block is created from disparities obtained by the matching operation based on one-dimensional windows involving pixels of a concerned block, and a specific disparity just corresponding to the peak of thus obtained histogram is identified as a valid disparity representing the concerned block (S106).
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Citations
16 Claims
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1. A method of matching stereo images, comprising the steps of:
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inputting first and second images IL and IR; developing said images IL and IR into a plurality of frequency component images FL1, FL2, FL3 . . . , FLk, FLk+1, . . . , FLn and a plurality of frequency component images FR1, FR2, FR3, . . . , FRk, FRk+1, . . . , FRn, respectively; applying a secondary differential processing to each of said frequency component images; converting each frequency component image, after being applied the secondary differential processing, into ternary values pixel by pixel, thereby obtaining ternary-valued frequency component images TL1, TL2, TL3, . . . , TLk, TLk+1, . . . , TLn and ternary-valued frequency component images TR1, TR2, TR3 . . . TRk, TRk+1, . . . , TRn; and performing a matching operation between said first and second images based on said ternary-valued frequency component images, wherein pixels in a one-dimensional window of the ternary-valued frequency component image TLk of said first image IL are compared in a one-to-one manner with pixels in a designated region of the ternary-valued frequency component image TRk of said second image IR, when said ternary-valued frequency component images TLk and TRk are identical in their frequency components, an evaluation raesult "P" is obtained when corresponding two pixels are both "p" or "m", while an evaluation result "Z" is obtained when the corresponding two pixels are both "z", and a similarity between two ternary-valued frequency component images TLk and TRk is evaluated by using the following equation;
space="preserve" listing-type="equation">Eall=Σ
β
k(PN)k+Σ
γ
k(ZN)kwhere PN represents a total number of pixels having the evaluation result "P", ZN represents a total number of pixels having the evaluation result "Z", and β
k and γ
k represent weighing factors. - View Dependent Claims (2, 3, 4)
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5. A method of matching stereo images, comprising the steps of:
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inputting first and second images IL and IR; developing said images IL and IR into a plurality of frequency component images FL1, FL2, FL3, . . . , FLk, FLk+1, . . . , FLn and a plurality of frequency component images FR1, FR2, FR3, . . . , FRk, FRk+1, . . . , FRn, respectively; applying a secondary differential processing to each of said frequency component images; converting each frequency component image, after being applied the secondary differential processing, into ternary values pixel by pixel by using a positive threshold TH1(>
0) and a negative threshold TH2(<
0) in such a manner that a pixel larger than TH1 is designated to "p", a pixel in a range between TH1 and TH2 is designated to "z", and a pixel smaller than TH2 is designated to "m", thereby obtaining ternary-valued frequency component images TL1, TL2, TL3, . . . , TLk, TLk+1, . . . , TLn and ternary-valued frequency component images TR1, TR2, TR3 . . . , TRk, TRk+1, . . . , TRn; andperforming a matching operation between said first and second images based on said ternary-valued frequency component images, wherein pixels in a one-dimensional window of the ternary-valued frequency component image TLk of said first image IL are compared in a one-to-one manner with pixels in a designated region of the ternary-valued frequency component image TRk of said second image IR, when said ternary-valued frequency component images TLk and TRk are identical in their frequency components, an evaluation raesult "P" is obtained when corresponding two pixels are both "p" or "m", while an evaluation result "Z" is obtained when the corresponding two pixels are both "z", and a similarity between two ternary-valued frequency component images TLk and TRk is evaluated by using the following equation;
space="preserve" listing-type="equation">Eall=Σ
β
k(PN)k+Σ
γ
k(ZN)kwhere PN represents a total number of pixels having the evaluation result "P", ZN represents a total number of pixels having the evaluation result "Z", and β
k and γ
k represent weighing factors. - View Dependent Claims (6, 7, 8)
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9. A method of matching stereo images, comprising the steps of:
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inputting first and second images IL and IR; developing said images IL and IR into a plurality of frequency component images FL1, FL2, FL3 . . . FLk, FLk+1, . . . , FLn and a plurality of frequency component images FR1, FR2, FR3 . . . FRk, FRk+1, . . . , FRn, respectively; applying a secondary differential processing to each of said frequency component images; converting each frequency component image, after being applied the secondary differential processing, into ternary values pixel by pixel in such a manner that a pixel not related to a zero-crossing point is designated to "z", a pixel related to a zero-crossing point and having a positive gradient is designated to "p", and a pixel related to a zero-crossing point and having a negative gradient is designated to "m", thereby obtaining ternary-valued frequency component images TL1, TL2, TL3, . . . , TLk, TLk+1, . . . , TLn and ternary-valued frequency component images TR1, TR2, TR3, . . . , TRk, TRk+1, . . . , TRn; and performing a matching operation between said first and second images based an said ternary-valued frequency component images, wherein pixels in a one-dimensional window of the ternary-valued frequency component image TLk of said first image IL are compared in a one-to-one manner with pixels in a designated region of the ternary-valued frequency component image TRk of said second image IR, when said ternary-valued frequency component images TLk and TRk are identical in their frequency components, an evaluation raesult "P" is obtained when corresponding two pixels are both "p" or "m", while an evaluation result "Z" is obtained when the corresponding two pixels are both "z", and a similarity between two ternary-valued frequency component images TLk and TRk is evaluated by using the following equation;
space="preserve" listing-type="equation">Eall=Σ
β
k(PN)k+Σ
γ
k(ZN)kwhere PN represents a total number of pixels having the evaluation result "P", ZN represents a total number of pixels having the evaluation result "Z", and β
k and γ
k represent weighing factors. - View Dependent Claims (10, 11, 12)
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13. A method of matching stereo images, comprising the steps of:
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inputting first and second images IL and IR; developing said images IL and IR into a plurality of frequency component images FL1, FL2, FL3, . . . , FLk, FLk+1, . . . , FLn and a plurality of frequency component images FR1, FR2, FR3, . . . , FRk, FRk+1, . . . , FRn, respectively; applying a secondary differential processing to each of said frequency component images; converting each low frequency component image of said frequency component images, after being applied the secondary differential processing, into ternary values pixel by pixel by using a positive threshold TH1(>
0) and a negative threshold TH2(<
0) in such a manner that a pixel larger than TH1 is designated to "p" a pixel in a range between TH1 and TH2 is designated to "z", and a pixel smaller than TH2 is designated to "m",and converting each high frequency component image of said frequency component images, after being applied the secondary differential processing, into ternary values pixel by pixel in such a manner that a pixel not related to a zero-crossing point is designated to "z", a pixel related to a zero-crossing point and having a positive gradient is designated to "p", and a pixel related to a zero-crossing point and having a negative gradient is designated to "m", thereby obtaining ternary-valued frequency component images TL1, TL2, TL3, . . . , TLk, TLk+1, . . . , TLn and ternary-valued frequency component images TR1, TR2, TR3, . . . , TRk, TRk+1, . . . , TRn; and performing a matching operation between said first and second images based on said ternary-valued frequency component images, wherein pixels in a one-dimensional window of the ternary-valued frequency component image TLk of said first image IL are compared in a one-to-one manner with pixels in a designated region of the ternary-valued frequency component image TRk of said second image IR, when said ternary-valued frequency component images TLk and TRk are identical in their frequency components, an evaluation raesult "P" is obtained when corresponding two pixels are both "p" or "m", while an evaluation result "Z" is obtained when the corresponding two pixels are both "z", and a similarity between two ternary-valued frequency component images TLk and TRk is evaluated by using the following equation;
space="preserve" listing-type="equation">Eall=Σ
β
k(PN)k+Σ
γ
k(ZN)kwhere PN represents a total number of pixels having the evaluation result "P", ZN represents a total number of pixels having the evaluation result "Z", and β
k and γ
k represent weighing factors. - View Dependent Claims (14, 15, 16)
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Specification