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12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00)
Texture image segmentation method based on multilayer CNN
Vancouver, British Columbia, Canada
November 13-November 15
ISBN: 0-7695-0909-6
| ASCII Text | x | ||
| G. Liu, S. Oe, "Texture image segmentation method based on multilayer CNN," 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, pp. 0147, 12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00), 2000. | |||
| BibTex | x | ||
| @article{ 10.1109/TAI.2000.889860, author = {G. Liu and S. Oe}, title = {Texture image segmentation method based on multilayer CNN}, journal ={2012 IEEE 24th International Conference on Tools with Artificial Intelligence}, volume = {0}, year = {2000}, isbn = {0-7695-0909-6}, pages = {0147}, doi = {http://doi.ieeecomputersociety.org/10.1109/TAI.2000.889860}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 IEEE 24th International Conference on Tools with Artificial Intelligence TI - Texture image segmentation method based on multilayer CNN SN - 0-7695-0909-6 SP EP A1 - G. Liu, A1 - S. Oe, PY - 2000 KW - image texture; image segmentation; feature extraction; cellular neural nets; multilayer perceptrons; texture image segmentation method; texture feature extraction method; simple texel scale feature; scale; binary image processing; pixel gray value; texel frequency; texel orientation features; texture edge integration problem; binary value line processing problems; hole filling; line thinning; line shortening; multilayer cellular neural network VL - 0 JA - 2012 IEEE 24th International Conference on Tools with Artificial Intelligence ER - | |||
Abstract: The paper presents a new texture feature extraction method called simple texel scale feature (STSF) based on the scale and orientation information of texels, and a new texture image segmentation method based on binary image processing is introduced. The scale information of texels is extracted by comparing the gray value of two pixels. The relation of the positions of these two pixels shows the frequency and orientation features of texels. Texel scale features can be extracted by using different position relations (distance and orientation). After obtaining texture feature images, we consider the texture image segmentation problem not as a pattern classification problem but several texture edge integration problems, which are simple binary value line processing problems such as hole filling, line thinning and shortening. A new kind of multilayer cellular neural network (CNN) called MLCNN is proposed, and some MLCNNs are designed for these problems.
Index Terms:
image texture; image segmentation; feature extraction; cellular neural nets; multilayer perceptrons; texture image segmentation method; texture feature extraction method; simple texel scale feature; scale; binary image processing; pixel gray value; texel frequency; texel orientation features; texture edge integration problem; binary value line processing problems; hole filling; line thinning; line shortening; multilayer cellular neural network
Citation:
G. Liu, S. Oe, "Texture image segmentation method based on multilayer CNN," ictai, pp.0147, 12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00), 2000
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