基于视觉感光层功能的菌落图像多强度边缘检测研究

doi:10.3969/j.issn.0258-8021. 2014. 06.07

Abstract
Figure/Table
References (0)
Related Citation (2)

Download: PDF (2179 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

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 integrateandfire (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 centersurround antagonistic receptive field feature and feedback enhancing mode of neuronal excitation. Meanwhile the image movement in multidirection and multiscale 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 multiintensity edge detection were assessed by the confidence of edge and reconstruction similarity. It was proved that the new method can effectively detect the intact multiintensity edge image, especially the means of reconstruction similarity in the weak edge detection is improved significantly, which is higher than 08 (P<005). The method of edge detection discussed in this paper provides a brandnew idea for multiintensity edge details image processing based on the feature of physiological visual.

Key words： edge detection photoreceptor receptive field leaky integrate\ and\ fire (LIF) neuronal model

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LUO Jia Jun WU Wei^* FAN Ying Le GAO Yun Yuan

Cite this article:

LUO Jia Jun WU Wei^* FAN Ying Le GAO Yun Yuan. Multiintensity Edge Detection for Colony Images Based on the Function of Photoreceptor in Visual System #br#[J]. journal1, 2014, 33(6): 677-686.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2014. 06.07 OR http://cjbme.csbme.org/EN/Y2014/V33/I6/677

［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, ChiCheng 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 spatiotemporal input-output spike patterns ［J］. Neurocomputing, 2013, 107: 3-10.
［7］Chouhan AS. An analytical study of leaky integrate-andfire 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. Spatiotemporal 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］MartinezConde 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.