Understanding the human mind is the key to social robotics, and researchers describe what we can expect from this field in the future.

We present an extensive evaluation of 17 confidence measures for stereo matching that compares the most widely used measures as well as several novel techniques proposed here. We begin by categorizing these methods according to which aspects of stereo cost estimation they take into account and, then, assess their strengths and weaknesses. The evaluation is conducted using a winner-take-all framework on binocular and multi-baseline datasets with ground truth. It measures the capability of each confidence method to rank depth estimates according to their likelihood for being correct, to detect occluded pixels and to generate low-error depth maps by selecting among multiple hypotheses for each pixel. Our work was motivated by the observation that such an evaluation is missing from the rapidly maturing stereo literature and that our findings would be helpful to researchers binocular and multi-view stereo.

The path following algorithm was proposed recently to solve the matching problems on undirected graph models, and exhibited a state-of-art performance on matching accuracy. In this paper we extend the path following algorithm to the matching problems on directed graph models, by proposing a concave relaxation for the problem. Based on the concave and convex relaxations, a series of objective functions are constructed, and the Frank-Wolfe algorithm is then utilized to minimize them. Several experiments on synthetic and real data witness the validity of the extended path following algorithm.

In this paper we present an efficient new approach for addressing two-view minimal-case problems in camera motion estimation, most notably the so-called five-point relative orientation, and the six-point focal-length problem. Our approach is based on the hidden variable technique for solving multivariate polynomial systems. The resulting algorithm is conceptually simple, which involves a relaxation which replaces monomials in all but one of the variables to reduce the problem to the solution of sets of linear equations, and finding the solution of a polynomial eigenvalue problem. To actually solve the polynomial eigenvalues efficiently, we make novel use of several numeric techniques, which include quotient-free Gaussian elimination, Levinson-Durbin iteration, as well as a dedicated root-polishing procedure. We have tested the approach on different minimal cases and extensions, with very satisfactory results obtained. Both executables and source codes of the proposed algorithms are made online and freely downloadable.

Object appearance modeling is crucial for tracking objects especially in videos captured by non-stationary cameras and for reasoning about occlusions between multiple moving objects. Based on the log-Euclidean Riemannian metric on symmetric positive definite matrices, we propose an incremental log-Euclidean Riemannian subspace learning algorithm in which covariance matrices of image features are mapped into a vector space with the log-Euclidean Riemannian metric. Based on the subspace learning algorithm, we develop a log-Euclidean block-division appearance model which captures both the global and local spatial layout information about object appearances. Single object tracking and multi-object tracking with occlusion reasoning are then achieved by particle filtering-based Bayesian state inference. During tracking, incremental updating of the log-Euclidean block-division appearance model captures changes in object appearance. For multi-object tracking, the appearance models of the objects can be updated even in the presence of occlusions. Experimental results demonstrate that the proposed tracking algorithm obtains more accurate results than six state-of-the-art tracking algorithms.