基于时空脉冲编码的图像主体轮廓提取方法

图像主体轮廓包含图像非常重要的信息, 精准有效地提取图像主体轮廓不仅能减少信息冗余, 而且能降低后续图像分析和处理的时间复杂度. 本文基于视觉神经元信息处理机理, 提出了一种基于时空脉冲编码的图像主体轮廓提取方法. 首先, 利用Gabor函数模拟神经节细胞感受野对图像进行多尺度多方向的信息提取; 其次, 模拟视网膜非经...

Image Principal Contour Extraction Method Based on Spatio-temporal Spike Coding

The image principal contour contains important image features, and an accurate and effective extraction method of the image principal contour can not only reduce information redundancy but also reduce the time complexity of subsequent image analysis and processing. Depending on the information processing mechanism of visual neurons, this paper proposes an image principal contour extraction method based on spatio-temporal spike coding. First, the Gabor function is used to simulate the multi-scale and multi-directional information extraction from the image by the receptive field of ganglion cells. Second, the non-classical receptive field of the retina is simulated to construct an anisotropic suppression model to suppress the edge of the image background texture. Then, as for the visual images obtained by the receptive field of different scales, the small scale of the visual receptive field can help extract most of the texture information of the image, and the extraction under the large scale can make most of the texture of the image disappear, with only some characteristics of the principal contour retained for the adaptive adjustment of weights to perform spatio-temporal spike coding. Finally, the leaky integrate-and-fire neuron model is used to extract the principal contour of the image, and the principal contour of the final image is obtained by non-maximum suppression and hysteresis threshold binarization. From both subjective and objective aspects, the method proposed in this paper is simulated and verified on the RUG40 database, and its performance is compared with that of the existing mainstream image contour extraction methods. The experimental results show that the proposed method can reduce the redundant information of the principal contour of the image while effectively improving the accuracy of principal contour extraction.