采用基本ICA模拟视觉感知机制对自然图像分解得到的图像基函数在空间排列上是混乱的,这与视觉生理机制相互矛盾.模拟视皮层感受野间的信息整合机制,建立了新的计算模型.针对基于内容的图像故障区域检测问题,提出了相应的高效率少样本检测算法. 首先,以列车正常和故障图像序列作为训练数据,利用拓扑ICA方法学习图像基函数,由此得到的独立分量系数作为神经元响应,然后模拟同步振荡机制选择响应强烈的神经元,输出其对应的内容,最后通过自动对比实现图像故障区域的快速定位.实验结果表明,与传统方法相比较,引入视觉信息整合机制的新模型及其算法能够提高故障检测率.

The visual information in the brain is passed layer by layer. Almost all the visual signals from the retina go through the receptive field of the primary visual cortex (V1 area) and pass on to a more advanced visual cortex after processing. The receptive field of V1 is mainly responsible for extracting the image shape, direction, color and other information, with the spatial domain of locality, time and frequency domain direction and choice, as well as sparse response characteristics. From the view of natural image statistics, Independent Component Analysis (ICA) is one of the main methods to model early computational vision. However, the space arrangement of basic functions (independent components of natural image) decomposed by basic ICA is chaotic and their amplitudes are uncertainty. This decomposition result is contradicted with physiological mechanisms of vision. So, a new computational model is proposed to simulate two important mechanisms of vision which are visual cortex receptive field topology construct and synchronous oscillation among neuron group. To solve the problem of train image fault detection, a new algorithm was proposed based on above compute model. The experiment results show that, the new algorithm can increase fault detection rate effectively compared with traditional methods which absence of above two important mechanisms of vision.