Classification of Rotated and Scaled Textured Images
| Project/Area Number |
08680398
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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 | Kyoto Institute of Technology |
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Principal Investigator |
YOSHIDA Yasuo Kyoto Inst.of Tech., Faculty of Engineering and Design, Prof., 工芸学部, 教授 (80026046)
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| Co-Investigator(Kenkyū-buntansha) |
FUJITA Kazuhiro Kyoto Inst.of Tech., Faculty of Engineering and Design, Assist Prof., 工芸学部, 助手 (90209049)
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| Project Period (FY) |
1996 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥2,400,000 (Direct Cost: ¥2,400,000)
Fiscal Year 1997: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 1996: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | spectral moments / rotation invariants / scale invariants / texture classification / defect detection / rotation angle / scaling factor / 拡大不変量 / 布地欠陥検出 / テクスチャ面傾斜角 / 微小石灰質検出 / 回転・拡大画像識別 |
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| Research Abstract |
Most of texture classification approaches pose classification problems under the assumption that a test image possesses the same orientation and scale as training images. According1y, they perform poorly even though the orientation of the scale of the test image differs a little from those of the training ones.
This project studies a new technique for texture classification based on rotation and scale (RS) invariants obtained from spectral moments. Rotation invariants derived from moments have been studied by many researchers. However, successful results for pattern recognition using them are limited only to binary images with a finite-sized object. Application of the invariant moments to gray-scale images is difficult because the images have non-zero values on the image boundaries.
Instead of the gray-scale moments of the image, we use the moments of its spectral density function (SDF) to obtain rotation invariants because the SDF has advantageous properties. When an image is rotated and scaled, its SDF rotates by the same ang1e and is inversely scaled ; moreover, the spectral powers of most textured images are concentrated around the origin and decay rapidly toward high frequencies. In addition, we scheme to obtain stable rotation moments ; we define as a circle the integral region for moment calculation and adjust its radius to sca1e change by setting a constant rate of the image total power in the circle. Since spectral moments have a simple property with respect to scale, we can define RS invariants by a ratio of rotation invariants.
We confirmed the validity of the proposed method in experiments using 16 textures from Brodatz album with very high classification rates. We applied this technique to detection of (1) the rotation ang1e and the scaling factor of textured images, (2) the defect of c1othes and (3) the micro calcifications in mammograms and obtained prosperous results.
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Report
(3 results)
Research Products
(4 results)
