A stereo matching method based on adaptive morphological correlation is presented. The point correspondences of an input pair of stereo images are determined by matching locally adaptive image windows using the suggested morphological correlation that is optimal with respect to an introduced binary dissimilarity-to-matching ratio criterion. The proposed method is capable of determining the point correspondences in homogeneous image regions and at the edges of scene objects of input stereo images with high accuracy. Furthermore, unknown correspondences of occluded and not matched points in the scene can be successfully recovered using a simple proposed post-processing. The performance of the proposed method is exhaustively tested for stereo matching in terms of objective measures using known database images. In addition, the obtained results are discussed and compared with those of two similar state-of-the-art methods.
Camera pose estimation is an essential task in many computer vision applications. A widely used approach for this task is given by the specification of several corresponding points in a pair of captured input and reference images. The effectiveness of these methods depends on the accuracy of the specified points and is very sensitive to outliers. This work presents an iterative method for camera pose estimation based on local image correlation. The pose of the camera is estimated by finding a homography matrix that produces the best match between local fragments of the reference image constructed around the specified points and their corresponding projective transformed fragments of the input image when using the estimated homography. The performance of the proposed method is tested by processing synthetic and experimental images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.