Visible and infrared sensors fusion by matching feature points of foreground blobs
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
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Discerning depth from IR stereopsis is difficult because in general IR imagery does not contain sufficient features for left-right correspondence. We investigate the production of sparse disparity maps from un-calibrated infrared stereo images and argue that a dense depth field may not be attained directly from IR stereo images, but perhaps a sparse depth field may be obtained that can be interpolated to produce a dense depth field. In our proposed technique the sparse disparity map is produced by matching the stable features, extracted from the phase congruency model. A set of Log-Gabor wavelet coefficients is used to analyze and describe the extracted features for matching. The resulted sparse disparity map is then refined by triangular and epipolar geometrical constraints. In densifying the sparse disparity map, a watershed transformation is performed on the discontinuity map of the reference image to divide the image into several segments and finally the surface of each segment is reconstructed independently by fitting a thin-plate spline to its known disparities. Experiments on a set of IR stereo pairs lend credibility to the robustness of our IR stereo matching and surface reconstruction technique.