Group-wise sparse correspondences between images based on a common labelling approach
International Conference on Computer Vision Theory and Applications (VISAPP)
Finding sparse correspondences between two images is a usual process needed for several higher-level computer vision tasks. For instance, in robot positioning, it is frequent to make use of images that the robot captures from their cameras to guide the localisation or reduce the intrinsic ambiguity of a specific localisation obtained by other methods. Nevertheless, obtaining good correspondence between two images with a high degree of dissimilarity is a complex task that may lead to important positioning errors. With the aim of increasing the accuracy with respect to the pair-wise image matching approaches, we present a new method to compute group-wise correspondences among a set of images. Thus, pair-wise errors are compensated and better correspondences between images are obtained. These correspondences can be used as a less-noisy input for the localisation process. Group-wise correspondences are computed by finding the common labelling of a set of salient points obtained from the images. Results show a clear increase in effectiveness with respect to methods that use only two images.
computer vision, image matching, pattern recognition.
multiple point set alignment, group wise point set alignment
A. Solé, G. Sanroma, F. Serratosa i Casanelles and R. Alquézar Mancho. Group-wise sparse correspondences between images based on a common labelling approach, 7th International Conference on Computer Vision Theory and Applications, 2012, Rome, Italy, pp. 269-278, INSTICC.