Abstract
In this paper, a bio-inspired neural network was constructed. It could represent images effectively and provide a processing method for image understanding. Our model adopted the retinal ganglion cells (GCs) and their non-classical receptive field (nCRF) can dynamic self-adjusts according to the characteristics of the image. Extensive experimental evaluations to demonstrate that this kind of representation method was able to make for SIFT detector for focus on foreground object more correctly, and promote the result of image segmentation significantly.
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Wei, H., Lang, B., Zuo, QS. (2012). An Image Representation Method Based on Retina Mechanism for the Promotion of SIFT and Segmentation. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_45
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DOI: https://doi.org/10.1007/978-3-642-34500-5_45
Publisher Name: Springer, Berlin, Heidelberg
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