| Project/Area Number |
09680364
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Mie University |
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Principal Investigator |
NOMURA Yoshihiko Mie University, Faculty of Engineering, Professor, 工学部, 教授 (00228371)
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| Co-Investigator(Kenkyū-buntansha) |
MITSUYA Yasunaga Nagoya University, Graduate School of Engineering, Professor, 工学研究科, 教授 (10200065)
KATO Norihiko Mie University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (70185859)
MATSUI Hirokazu Mie University, Faculty of Engineering, Research Associate (10303752)
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| Project Period (FY) |
1997 – 1998
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| Project Status |
Completed (Fiscal Year 1998)
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| Budget Amount *help |
¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 1998: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Keywords | Hough Transform / Affine Transform / Gabor Transform / Image / Matching |
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| Research Abstract |
Searching objects within an image is an important technology in computer vision. In model-based matching system, the appearance of the target-object in an image can be greatly different with the aspect and scale of the model, because of the rotation and translation between the views of the model and the real image. Furthermore if the background of the input image is unknown or with clutters, the matching process will be further complicated. This work presents an efficient method to search a target-object in an image with unknown scene by using the Hough Transform and the Gabor transform. In the latter, spectra information of the input image can be obtained by applying the Gabor transform to the input image. The spectra information has the following characteristics.
(1)Each Gabor function is expanded in the frequency domain. Therefore, few Gabor functions could cover whole the frequency spectrum of the original image. This mean it provides very few numbers of features, resulting in the highly efficient calculation.
(2) Responding to the rotations of model, the spectra plane rotates.
(3) Responding to the scale changes of model, the spectra plane changes its scale while changing its magnitude with constant rate. Using the second and the third characteristics, target-object can be correctly detected and the pose is also calculated by spectra matching. The approach consists of four steps.
(1) Amplitude spectra of Gabor-expansion coefficients are calculated at a central pixel on the model image and at all pixels on the scene image.
(2) Amplitude spectra are deformed by a log-polar sampling.
(3) Considering the effect of rotation and scale change, as an affine-invariant concentric feature, amplitude spectra of Fourier-expansion coefficients are calculated for all the log-polar sampled spectra.
(4) A specific pixel having the same Fourier amplitude spectrum as the model image is found out among the pixels on the scene image. The target object is considered to be at the pixel.
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