Recognition of panorama parts using OpenCV

The main difficulty of automated classification is to work out an algorithm which would give highly differential results on image comparison. There are some standard methods of analyzing images: template matching, image stitching and optical flow, but the last one is hardy suitable for this problem. The idea is to use OpenCV library where these methods are already implemented and to estimate the quality of such preliminary classification. Then starts the main part of work: to adjust preprocessing algorithms and parameters according to the current problem. For the first step we tried to use the template matching approach. For each pair of images in the set we searched the segments of target image on the source image. To achieve the best result several target-into-segments splitting strategies were developed. For the second step we tried image stitching approach. Specifically, we used the SURF algorithm to find the key points for each image and then found matching key points for each pair if images in the set. The last step was combination of two implemented approaches in their best configurations.