Research On Recognition And Location Of Work-Pieces Based On Binocular Stereo Vision

With the development of machine vision, stereo vision especially binocular stereo vision is applied widely in many fields such as objection recognition, virtual reality, industrial inspection, navigator of robotics, aviation and spaceflight. One important task of binocular stereo vision is to get 3D information of objects under various conditions. Binocular stereo vision has incomparable advantages over monocular vision and it is a preceding research area of machine vision. This paper discusses the main search on location of work-piece by binocular stereo vision in semi-structural environment.Camera calibration is necessary when 3D information is obtained from 2D image in machine vision. Considering conventional calibration methods and self-calibration method and combining concrete experiment environment, binocular linear camera calibration method based on a transmission projection model without distortion is used and the method which image coordinate mapped to world coordinate directly is summarized. This method avoids the error when the intrinsic and extrinsic parameters of the cameras are calculated and makes the calibration experiment much easier with high precision.Images which are photographed by two cameras are matched in order to get depth information of work-pieces. latest achievement in image processing field—algorithm of Scale Invariant Feature Transform (SIFT) is used to match images based on scale space theory and this algorithm is improved. It is proved by experiments that this improved algorithm can solve stereo matching problems of work-piece images including five aspects: rotation,scale,translation,occlusion and noise and improve processing speed compared to original algorithm.Scale Invariant Feature Transform (SIFT) is used to search objects as template and calculation method of shape center based on edge is adopted to locate work-pieces combining binocular linear camera calibration. The algorithm in this paper is much more practical than conventional template matching methods because of its high precision and stability.