摘要
目的提出一种主视通路信息流层级传递和响应的新模型用于检测图像轮廓的新方法。方法以Ru G图库40幅图片为实验对象,利用非下采样轮廓波变换模拟外侧膝状体(lateral geniculate nucleu,LGN)对视觉信息的频域分离作用;构建LIF神经元网络模型来表达视觉神经系统中的电生理活动,通过CRF机制整合空间信息;同时,利用局部半波整流的高斯差函数来模拟n CRF的全局调节机制。有朝向性地将多个LGN细胞感受野进行关联。同时构建皮层下视通路来模拟它对于主视通路进行视觉信息处理的协同作用。然后,经过非极大值抑制和阈值处理,得到本文轮廓检测结果。最后将本文检测结果与3种经典方法(Noninh,SSC,ISO)的检测结果进行对比。结果本方法的检测结果与基准轮廓图的平均P指标为0.46,大于经典的3种检测方法(P指标分别为0.36、0.40、0.42)。结论本文算法不仅对纹理和背景具有抑制的作用,而且能有效区分纹理强边缘和主体轮廓,获得较佳的效果。
Objective To propose a new model of hierarchical transfer and response of the information flow in the cortical visual pathway to detect the image contour. Methods Forty images in the Ru G library were selected for processing. The non-subsampled contourlet transform was used to simulate the frequency-domain separation of visual information in lateral geniculate nucleu( LGN). The LIF neural network model was constructed to express the electrophysiological activities in the visual nervous system,and then the spatial information was integrated via CRF mechanism. At the same time,in order to simulate the global adjustment mechanism of the n CRF,the Gaussian difference function of the local half-wave rectification was utilized. After that,multiple LGN cells were associated into the field. Meanwhile,subcortical visual pathways were constructed to simulate the synergistic effect of visual information on the cortical pathways. The non-maximum suppression and threshold processing were used for processing and the contour detection results were obtained. In the end,the results were compared with that of other three classical methods( Noninh,SSC,ISO). Results The mean value of P between the detected result and the ground truth was 0. 46,which was better than the other three classical methods( P values were 0. 36,0. 40,0. 42 respectively). Conclusion The method not only has a deterrent effort on texture and background,but also can effectively distinguish the edge of the texture and the main contour,which can get better results.
引文
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