| Project/Area Number |
05680299
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | The University of Tokushima |
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Principal Investigator |
OE Shunichiro The University of Tokushima, Information Processing Center, Associate Professor, 総合情報処理センター, 助教授 (10035636)
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| Project Period (FY) |
1993 – 1994
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| Project Status |
Completed (Fiscal Year 1994)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1994: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1993: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | Texture analysis / Segmentation / Texture feature / Neural network / Two-dimensional AR model / Fractal dimension / Kohonen's self-organizing neural network / Decision based neural network / テクスチャ画像 |
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| Research Abstract |
A segmentation method of a texture image composed of several kinds of texture areas can be divided into two problems. They are to extract the features from random texture field exactly and to segment an image to homogeneous texture areas. In order to segment the texture image it was necessary to get a discriminant function and its threshold value for the decision of homogeneity of textures in many methods developed until now. But it is difficult to formulate the function and to decide the threshold value theoretically. So I aimed at the development of texture segmentation method which did not need a discriminant function and its threshold value by using neural networks.
In this research I proposed the segmentation method of a texture image by applying Kohonen's self-organizing neural network, neural network based on backpropagation method, and decision based neural network (DBNN) to the texture features extracted by two-dimensional AR model and fractal dimension. It was obviously that the best segmentation result was obtained by using Kohonen's self-organizing neural network and DBNN.As the actual texture image has strictly non-stationarity, good segmentation result was not obtained by applying this method to such an image. So I propose a new method which pre-processes a original image by using wavelet transform.
Until now it is difficult to obtain the optimum segmentation number contained in a texture image automatically. So I proposed a new method to solve the problem.
Furthermore, it was obviously that the proposed method could apply to the segmentation problem of a color texture image by using the features for monochrome texture image and some kinds of color features.
From the above-mentioned researches I think that a texture segmentation method with generality and good segmentation ability is completed.
I will contribute the results of this research to jounal soon.
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