Abstract
We propose a dynamic region generation algorithm for image segmentation based on spiking neural network inspired by human visual cortex that shows the tremendous capacity of processing image. The network structure generated by the proposed algorithm is automatically and dynamically. An image can be decomposed into several different shape and size of regions that look like superpixels. Merging these regions based on the color space similarity can extract contour. Dynamic network architecture brings stronger computing power. Dynamic generation method leads to more flexible network. Experimental results on BCDS300 dataset confirm that our approach achieves satisfactory segmentation results for different images compared with SLIC.
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Acknowledgments
This work was supported in part by the National Science Foundation of China under Grants 61573081, 61273308 and the Fundamental Research Funds for Central Universities under Grant ZYGX2015J062.
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Zuo, L., Ma, L., Xiao, Y., Zhang, M., Qu, H. (2017). A Dynamic Region Generation Algorithm for Image Segmentation Based on Spiking Neural Network. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_83
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DOI: https://doi.org/10.1007/978-3-319-70090-8_83
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