Abstract The accuracy of edge detection is of great significance for improving the performance of artificial visual perception systems. In this work, a neural network with long and short-term synaptic complementarity was constructed. First, the dominant color antagonistic characteristics of cone cells were introduced, and the color antagonistic channels of the image to be tested were weighted to obtain the primary edge perception of the image to be tested. the synchronous firing characteristics of the neuron group were simulated, the synapse dynamically connected neuron action window was defined, and the group discharge time coding of the primary edge perception was realized; then a long- and short-term synaptic complementary module was built based on the synchronous firing of the neuron group in the short-term characteristics and the temporal and spatial dependence of neuron firing activity in the long-term, to achieve long- and short-term synaptic plasticity coding and complementary fusion. At last, the edge response by encoding the temporal information stream was obtained. The 20 pairs of colony images collected by the laboratory according to the needs of routine microbiological experiments were used as experimental materials, and the reconstructed similarity MSSIM, edge confidence BIdx, and comprehensive index EIdx were used as evaluation indicators. Results showed that, compared with the three mainstream methods of VSC, NIS and MSP, the detection results of the algorithm of this study had accurate edges and a low missed detection rate, which was consistent with the results of artificial subjective observations; meanwhile, the mean and standard deviation of the three indicators of MSSIM, BIdx and EIdx was 0.909 6±0.037 7, 0.671 5±0.105 7, and 0.804 8±0.052 1 respectively, and the overall performance was better than the above three mainstream methods. The method provided a new idea for realizing the construction of visual perception computing model and its application in image processing by simulating the long- and short-term synaptic complementarity of the neuron population.
Yu Xiang,Fan Yingle,Fang Tao, et al. Edge Detection Method Based on the Long- and Short-Term Synaptic Complementary Networks[J]. Chinese Journal of Biomedical Engineering, 2020, 39(6): 641-651.
[1] Iqbal MTB, Abdullah-Al-Wadud M, Ryu B, et al. Facial expression recognition with neighborhood-aware edge directional pattern (NEDP)[J]. IEEE Transactions on Affective Computing, 2020, 11(1): 125-137. [2] Mittal M, Verma A, Kaur I, et al. An efficient edge detection approach to provide better edge connectivity for image analysis[J]. IEEE Access, 2019: 33240-33255. [3] Lee C, Srinivasan G, Panda P, et al. Deep spiking convolutional neural network trained with unsupervised spike-timing-dependent plasticity[J]. IEEE Transactions on Cognitive and Developmental Systems, 2018, 11(3): 384-394. [4] Li Jing, Liu Bo, Gao Weixin, et al. Spiking neural network with synaptic plasticity for recognition[C]//2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). Chongqing: IEEE, 2018: 1728-1732. [5] Ostapenko O, Puscas M, Klein T, et al. Learning to remember: A synaptic plasticity driven framework for continual learning[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE, 2019: 11313-11321. [6] Ahmed A, Byun Y, Byun SY, et al. Edge detection in roof images using transfer learning in CNN[C]// 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS). Beijing: IEEE 2019: 92-98. [7] Wang Y, Zhao X, Huang K. Iterative residual network for structured edge detection[C]// IEEE International Conference on Image Processing (ICIP). Athens: IEEE, 2018: 4183-4187. [8] 潘晴, 严国萍, 张玉宽. 基于人眼视觉注视机制下突触短时可塑性的图像边缘检测算法[J]. 中国图象图形学报, 2019, 13(7):1316-1321. [9] 王典, 范影乐, 张梦楠, 等. 基于突触连接视通路方位敏感的图像分级边缘检测方法[J]. 中国生物医学工程学报, 2015, 34(5): 522-532. [10] 廖进文, 范影乐, 武薇, 等. 基于抑制性突触多层神经元群放电编码的图像边缘检测[J]. 中国生物医学工程学报, 2014, 33(5):513-524.. [11] Zhang Xianshi, Gao Shaobing, Li Ruoxuan, et al. A retinal mechanism inspired color constancy model[J]. IEEE Transactions on Image Processing, 2016, 25(3): 1219-1232. [12] Gao Shaobing, Yang Kaifu, Li Chaoyi, et al. Color constancy using double-opponency[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(10): 1973-1985. [13] Niu Yuzhen, Zhang Haifeng, Guo Wenzhong, et al. Image quality assessment for color correction based on color contrast similarity and color value difference[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 28(4): 849-862. [14] 胡钧皓, 范影乐, 李康群, 等. 基于神经元颜色拮抗与动态编码的轮廓检测方法[J]. 中国生物医学工程学报, 2017, 36(5): 520-528. [15] Yoon YC. LIF and simplified SRM neurons encode signals into spikes via a form of asynchronous pulse sigma-delta modulation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(5): 1192-1205. [16] Kasabov N, Zhou L, Doborjeh MG, et al. New algorithms for encoding, learning and classification of fMRI data in a spiking neural network architecture: a case on modeling and understanding of dynamic cognitive processes[J]. IEEE Transactions on Cognitive and Developmental Systems, 2016, 9(4): 293-303. [17] Dedola F, Severino FPU, Meneghetti N, et al. Ultrasound Stimulations Induce Prolonged Depolarization and Fast Action Potentials in Leech Neurons[J]. IEEE Open Journal of Engineering in Medicine and Biology. 2020, 1: 23-32. [18] Yang Rui, Huang Heming, Hong Qinghui, et al. Synaptic suppression triplet-STDP learning rule realized in second-order Memristors[J]. Advanced Functional Materials, 2018, 28(5): 1704455-1704455. [19] 方芳, 范影乐, 廖进文, 等. 基于神经元突触可塑性机制图像边缘检测方法[J]. 华中科技大学学报(自然科学版),2015,43(S1):200-202,206. [20] Wei Wei, Wang Xiaojing. Downstream effect of ramping neuronal activity through synapses with short-term plasticity[J]. Neural Computation, 2016, 28(4): 652-666. [21] Antonietti A, Casellato C, D’Angelo E, et al. Model-driven analysis of eyeblink classical conditioning reveals the underlying structure of cerebellar plasticity and neuronal activity[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(11): 2748-2762. [22] 魏春玲. 突触后神经元兴奋性的活动依赖的长时程变化[D]. 西安:陕西师范大学, 2013. [23] 磨少清. 边缘检测及其评价方法的研究[D]. 天津: 天津大学, 2011. [24] Govindarajan B, Panetta KA, Agaian S. Image reconstruction for quality assessment of edge detectors[C]// IEEE International Conference on Systems, Man and Cybernetics. Singapore City: IEEE, 2008: 691-696. [25] Wang Z, Bovik AC, Sheikh HR, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612. [26] Meer P, Georgescu B. Edge detection with embedded confidence[J]. IEEE Transactions on pattern analysis and machine intelligence, 2001, 23(12): 1351-1365. [27] Berberian N, Ross M, Chartier S, et al. Synergy between short-term and long-term plasticity explains direction-selectivity in visual cortex[C]//2017 IEEE Symposium Series on Computational Intelligence (SSCI). Honolulu: IEEE, 2017:1-8.
