Multi-stereo Vision, Image Matching Techniques

Image matching in multi-stereo vision is studied in this thesis. To improve the precision and reliability of matching of corresponding points between different images taken from different orientation by one single camera, a set of refrence points is applied as an assistant utility to measure the object. The main contents and contributions of this thesis are as follows:(1) An improved algorithm is put forward based on the OFMM. Owning to the great computational complexity of the OFMM algorithm, Canny operator is used to detect initial edge locations before using this algorithm. The templates of OFMM are improved, the condition have changed when we judge which pixel is edge. (2) A novel algorithm of automatic reference point detection is put forward and implemented. Firstly, the Canny operator is utilized to detect initial edge locations in pixel. Secondly, useless edges are filtered by using inherent attributes of the ellipse, such as length, roundness, convex-concave and enclosure. Thirdly, accurate edges in sub-pixel are obtained by using the improved orthogonal Fourier-Mellin moment operator. Fourthly, reference points are classified by using the improved method. At last, sub-pixel center coordinates are identified. (3) A matching algorithm based on camera poses is put forward. Firstly, poses of the camera are obtained based on coded targets which are matched. Secondly, uncoded targets are matched by using epipolar constraint based on camera poses obtained. At last, refined matching is performed based on initial matching by means of symmetrical distance constraint to increase number of corresponding points and to improve the accuracy of the matching between corresponding points.