Planar object recognition based on local feature prediction agreements

School of Mechanical Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China

^a Corresponding author: mojinqiu@sjtu.edu.cn

Abstract

Object recognition has drawn great attention in industrial application especially in automated feeding and assembling, for it can greatly improve the line flexibility and save cost. In this paper, a simple but effective method for planar object recognition is presented. This method can deal with objects under complex conditions like occlusion and clutter. The method generates object pose hypothesis from the prediction agreements of different local features in the object. There are two stages contained in our method, offline stage and online stage. At offline stage, the representative parts in the object are chosen as its local features and the recognition template is made. Next at online stage, the matches of different local features are found in the input image. Then the prediction agreements are searched among them in order to generate the final object pose hypothesis. A thin planar object recognition experiment has been conducted under occluded conditions and an improved result is presented compared with the traditional overall matching method.