基于改进边缘检测的视觉注意计算模型

本文主要针对传统Itti视觉注意计算模型，引入边缘特征信息，优化视觉注意计算模型.在引入边缘特征信息的过程中，本文对Canny边缘检测算法进行改进：以改进后的双边滤波算法代替高斯滤波从而更好的保持边缘，借鉴Sobel算子从4个方向计算梯度幅值来代替原方法中的从2个方向来计算的过程，利用改进的OTSU算法选取双阈值代替手动设置阈值，从而降低图像分割时的误检、漏检现象.在实验中，我们发现，该方法相较于建立在普通Canny算法基础上的Itti视觉注意计算模型，对显著区域的提取效果上有很大的提高.

Visual Attention Calculation Model Based on Improved Edge Detection

This study mainly focuses on the traditional Itti visual attention calculation model, introduces edge feature information, and optimizes the visual attention calculation model. In the process of introducing edge feature information, this study improves the Canny edge detection algorithm: replace the Gaussian filter with an improved bilateral filtering algorithm to better maintain the edge, use the Sobel operator to calculate the gradient magnitude from four directions instead of two directions in the original method, the improved OTSU algorithm is used to select the double threshold instead of manually setting the threshold, thus reducing false detection and missed detection during image segmentation. In the experiment, we found that the proposed method is much better than the Itti visual attention calculation model based on the ordinary Canny algorithm.