Ivan Cabezas

A measure for accuracy disparity maps evaluation

The quantitative evaluation of disparity maps is based on error measures. Among the existing measures, the percentage of Bad Matched Pixels (BMP) is widely adopted. Nevertheless, the BMP does not consider the magnitude of the errors and the inherent error of stereo systems, in regard to the inverse relation between depth and disparity. Consequently, different disparity maps, with quite similar percentages of BMP, may produce 3D reconstructions of largely different qualities. In this paper, a ground-truth based measure of errors in estimated disparity maps is presented. It offers advantages over the BMP, since it takes into account the magnitude of the errors and the inverse relation between depth and disparity. Experimental validations of the proposed measure are conducted by using two state-of-the-art quantitative evaluation methodologies. Obtained results show that the proposed measure is more suited than BMP to evaluate the depth accuracy of the estimated disparity map.

A Method for Reducing the Cardinality of the Pareto Front

Multi-objective problems are characterised by the presence of a set of optimal trade-off solutions –a Pareto front–, from which a solution has to be selected by a decision maker. However, selecting a solution from a Pareto front depends on large quantities of solutions to select from and dimensional complexity due to many involved objectives, among others. Commonly, the selection of a solution is based on preferences specified by a decision maker. Nevertheless a decision maker may have not preferences at all. Thus, an informed decision making process has to be done, which is difficult to achieve. In this paper, selecting a solution from a Pareto front is addressed as a multi-objective problem using two utility functions and operating in the objective space. A quantitative comparison of stereo correspondence algorithms performance is used as an application domain.

An EA-Based Method for Estimating the Fundamental Matrix

The camera calibration problem consists in estimating intrinsic and extrinsic parameters. It can be solved by computing a 3x3 matrix enclosing such parameters - the fundamental matrix -, which can be obtained from a set of corresponding points. Nevertheless, in practice, corresponding points may be falsely matched or badly located, due to occlusion and ambiguity. Moreover, if the set of corresponding points does not include information on existing scene depth, the estimated fundamental matrix may not be able to correctly recover the epipolar geometry. In this paper, an EA-based method for accurately selecting estimated corresponding points is introduced. It considers geometric issues that were ignored in previous EA-based approaches. Two selection operators were evaluated and obtained similar results. Additionally, a mutation operator is designed to tackle bad located points by shifting disparity vectors. An inter-technique comparison is performed against a standard camera calibration method. The qualitative evaluation is conducted by analysing obtained epipolar lines, regarding expected appearance, based on a-priori knowledge of camera systems during the capturing process. The quantitative evaluation of the proposed method is based on residuals. Experimental results shown the proposed method is able to correctly reconstruct the epipolar geometry.

Evaluating Robustness of Template Matching Algorithms as a Multi-objective Optimisation Problem

Template matching has multiple applications on different problems in computer vision. Image distortions remain as the main challenge that template matching algorithms have to overcome. Thus, measuring robustness of algorithms against distortion conditions is an important task. Moreover, a comparison among template matching algorithms is difficult to achieve due to the lack of a standard evaluation methodology. In this paper, a measurement for quantifying the robustness of template matching algorithms against a single distortion is introduced. In addition, a procedure for comparing template matching algorithms is presented, aiming to become an evaluation standard. The comparison of template matching algorithms is formulated as a Multi-objective Optimisation problem. Experimental evaluation of the proposed procedure, using the robustness coefficient, is conducted by comparing algorithms based on full-search and different similarity measurements.