A Review of Gradient-Based and Edge-Based Feature Extraction Methods for Object Detection

In computer vision research, object detection based on image processing is the task of identifying a designated object on a static image or a sequence of video frames. Projects based on such research works have been widely adopted to various industrial and social applications. The fields to which those applications applies includes but not limited to, security surveillance, intelligent transportation system, automated manufacturing, quality control and supply chain management. In this paper, we are going to review a few most popular computer vision methods based on image processing and pattern recognition. Those methods have been extensively studied in various research papers and their significance to computer vision research have been proven by subsequent research works. In general, we categorize those methods into to gradient-based and edge-based feature extraction methods, depending on the low level features they use. In this paper, the definitions for gradient and edge are extended. Because an image can also be considered as a grid of image patches, it is therefore reasonable to incorporate the concept of granules to gradient for a review. The definition for granules can be found in [1].