10 April 2018 Super-pixel extraction based on multi-channel pulse coupled neural network
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106154P (2018) https://doi.org/10.1117/12.2303366
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
Super-pixel extraction techniques group pixels to form over-segmented image blocks according to the similarity among pixels. Compared with the traditional pixel-based methods, the image descripting method based on super-pixel has advantages of less calculation, being easy to perceive, and has been widely used in image processing and computer vision applications. Pulse coupled neural network (PCNN) is a biologically inspired model, which stems from the phenomenon of synchronous pulse release in the visual cortex of cats. Each PCNN neuron can correspond to a pixel of an input image, and the dynamic firing pattern of each neuron contains both the pixel feature information and its context spatial structural information. In this paper, a new color super-pixel extraction algorithm based on multi-channel pulse coupled neural network (MPCNN) was proposed. The algorithm adopted the block dividing idea of SLIC algorithm, and the image was divided into blocks with same size first. Then, for each image block, the adjacent pixels of each seed with similar color were classified as a group, named a super-pixel. At last, post-processing was adopted for those pixels or pixel blocks which had not been grouped. Experiments show that the proposed method can adjust the number of superpixel and segmentation precision by setting parameters, and has good potential for super-pixel extraction.
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GuangZhu Xu, GuangZhu Xu, Song Hu, Song Hu, Liu Zhang, Liu Zhang, JingJing Zhao, JingJing Zhao, YunXia Fu, YunXia Fu, BangJun Lei, BangJun Lei, } "Super-pixel extraction based on multi-channel pulse coupled neural network", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106154P (10 April 2018); doi: 10.1117/12.2303366; https://doi.org/10.1117/12.2303366
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