Search

An efficient unambiguous stereo matching technique is presented in this paper. Our main contribution is to introduce a new reliability measure to dynamic programming approaches in general. For stereo vision application, the reliability of a proposed match on a scanline is defined as the cost difference between the globally best disparity assignment that includes the match and the globally best assignment that does not include the match. A reliability-based dynamic programming algorithm is derived accordingly, which can selectively assign disparities to pixels when the corresponding reliabilities exceed a given threshold. The experimental results show that the new approach can produce dense (>70 percent of the unoccluded pixels) and reliable (error rate <0.5 percent) matches efficiently (<0.2 sec on a 2GHz P4) for the four Middlebury stereo data sets. [ABSTRACT FROM AUTHOR]

Deals with a study which discussed the theoretical shading of a Lambertian sphere of known size in a multiple distant light source environment and derived its intensity function. Information on multiple illuminant direction analysis; Performance evaluation; Comparison with other techniques in determining single light source; Conclusions.

Match cost functions are common elements of every stereopsis algorithm that are used to provide a dissimilarity measure between pixels in different images. Global stereopsis algorithms incorporate assumptions about the smoothness of the resulting distance map that can interact with match cost functions in unpredictable ways. In this paper, we present a large-scale study on the relative performance of a structured set of match cost functions within several global stereopsis frameworks. We compare 272 match cost functions that are built from component parts in the context of four global stereopsis frameworks with a data set consisting of 57 stereo image pairs at three different variances of synthetic sensor noise. From our analysis, we infer a set of general rules that can be used to guide derivation of match cost functions for use in global stereopsis algorithms. [ABSTRACT FROM AUTHOR]