一种基于视觉预注意机制的选择性成像机器视觉系统

摘要

在大背景中微小目标的视觉检测中,传统的机器视觉系统按照预先设定的采样频率与固定的分辨率采集和处理图像,存在大量的冗余数据。为了有效去除冗余数据,基于人类视觉预注意机制设计了一种选择性成像机器视觉系统,将视觉处理过程划分为并行的预注意初级处理阶段与串行的高级处理阶段;同时模仿人类视觉注意机制,提出了一种基于视觉熵的视觉预注意算法;系统并行地获取并判断图像是否可疑,然后仅将少量可疑图像传送至主机串行的进行精细目标识别。试验结果表明,本系统显著地减少了图像处理的数据量,可疑图像识别算法快速有效,提高了系统检测效率和精度。为大背景中微小目标的机器视觉检测提供了一条新途径。%In the detection of small defects in a large background,since the existing machine vision systems sample images at a fixed resolution and a preset speed in one inspection,usually there is a large amount of redundancy.To solve this problem,a new machine vision system with parallel and selective imaging function based on visual attention was presented,which divided the image processing into two stages of paralleling pre-attention stage and serial attention stage.Following the mechanism of human visual attention,to remove most of the redundancy image data,an algorithm for identification of suspicious images in the pre-attention based on visual entropy was developed.The experiment results of detecting contaminants in wool indicated that the new machine vision system can efficiently reduce the total image data and the algorithm based on visual entropy is fast and valid.It provided a new approach for improvement of the speed and accuracy of real-time detections of the machine vision systems significantly.