Abstract Considering the superiority of visual nervous system in contour perception, a new method of image edge detection based on the function of photoreceptor in visual system was proposed in this paper. Firstly, the neural network was constructed with leaky integrateandfire (LIF) neuronal electrophysiological model. Secondly, each neuron would be classified as excitation (ON) type or inhibition (OFF) type according to the neural firing pattern. And then the weak edges were highlighted by using centersurround antagonistic receptive field feature and feedback enhancing mode of neuronal excitation. Meanwhile the image movement in multidirection and multiscale was applied to overcome the adaptability of photoreceptor and highlight the contrast of weak details. Finally the edge image was acquired by fusing the variance information, such as photosensitive neural network’s firing rates. In this paper, twenty colony images having rich details in the edge were selected as experimental samples. The results of multiintensity edge detection were assessed by the confidence of edge and reconstruction similarity. It was proved that the new method can effectively detect the intact multiintensity edge image, especially the means of reconstruction similarity in the weak edge detection is improved significantly, which is higher than 08 (P<005). The method of edge detection discussed in this paper provides a brandnew idea for multiintensity edge details image processing based on the feature of physiological visual.
LUO Jia Jun WU Wei* FAN Ying Le GAO Yun Yuan. Multiintensity Edge Detection for Colony Images Based on the Function of Photoreceptor in Visual System #br#[J]. journal1, 2014, 33(6): 677-686.
［1］Wenshuo G, Xiaoguang Z, Lei Y, et al. An improved Sobel edge detection ［C］// 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT). Chengdu: IEEE, 2010(5): 67-71.
［2］Bing W, Shaosheng F. An improved CANNY edge detection algorithm ［C］ // Qingling L, Fei Y, Yun L， et al. eds. Second IEEE International Workshop on Computer Science and Engineering. Qingdao: IEEE, 2009: 497-500.
［3］Zhan S, Jinglu H. Image edge detection method based on a simplified PCNN model with anisotropic linking mechanism ［C］ // Aboul EH, Ajith A, Francesco M, et al. eds. 10th IEEE International Conference on Intelligent Systems Design and Applications (ISDA). Cairo: IEEE, 2010:330-335.
［4］Yiming J, Chang HK, ChiCheng H. Efficient edge detection and object segmentation using Gabor filters ［C］//Proceedings of the 42nd Annual Southeast Regional Conference. Huntsville: ACM, 2004: 454-459.
［5］Lee BB, Martin PR, Grünert U. Retinal connectivity and primate vision ［J］. Prog Retin Eye Res, 2010, 29(6): 622-639.
［6］Mohemmed A, Schliebs S, Matsuda S, et al. Training spiking neural networks to associate spatiotemporal input-output spike patterns ［J］. Neurocomputing, 2013, 107: 3-10.
［7］Chouhan AS. An analytical study of leaky integrate-andfire neuron model using MATLAB simulation ［J］. Int J Eng Res Technol, 2013, 2(4): 2242-2245.
［8］Pillow JW, Shlens J, Paninski L, et al. Spatiotemporal correlations and visual signaling in a complete neuronal population ［J］. Nature, 2008, 454(7207): 995-999.
［9］Tanabe S. Population codes in the visual cortex ［J］. Neurosci Res, 2013, 76(3): 101-105.
［10］MartinezConde S, Macknik SL, Troncoso XG, et al. Microsaccades counteract visual fading during fixation ［J］. Neuron, 2006, 49(2): 297-305.
［11］Kuang X, Poletti M, Victor JD, et al. Temporal encoding of spatial information during active visual fixation ［J］. Curr Biol, 2012, 22(6): 510-514.
［12］Meer P, Georgescu B. Edge detection with embedded confidence ［J］. IEEE Trans Pattern Anal Mach Intell, 2001, 23(12): 1351-1365.
［13］Wang Z, Bovik AC, Sheikh HR, et al. Image quality assessment: from error visibility to structural similarity ［J］. IEEE Trans Image Process, 2004, 13(4): 600-612.
［14］Govindarajani B, Panetta KA, Agaian S. Image reconstruction for quality assessment of edge detectors ［C］// IEEE International Conference on Systems, Man and Cybernetics. Singapore: IEEE, 2008: 691-696.
