11 October 2000 Color image segmentation algorithm based on neural networks
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Proceedings Volume 4224, Biomedical Photonics and Optoelectronic Imaging; (2000) https://doi.org/10.1117/12.403953
Event: Optics and Optoelectronic Inspection and Control: Techniques, Applications, and Instruments, 2000, Beijing, China
This paper presents a color image segmentation method with Self-Organize Feature Map and General Learning Vector Quantity which, in the uniform color space, divides color into clusters based on the least sum of squares criterion. At the first step of this method, SOFM is employed to make a preliminary classification on the original image, and then GLVQ is used to segment it. Both of their advantages can be fully taken of to improve the precision and velocity of color image segmentation.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qizhi Teng, Xiaohai He, Li Jiang, Zhouyu Deng, Xiaoqiang Wu, Deyuan Tao, "Color image segmentation algorithm based on neural networks", Proc. SPIE 4224, Biomedical Photonics and Optoelectronic Imaging, (11 October 2000); doi: 10.1117/12.403953; https://doi.org/10.1117/12.403953

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