Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
A Cooperative Algorithm for Stereo Matching and Occlusion Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse Disparity Map from Uncalibrated Infrared Stereo Images
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Dense Surface from Infrared Stereo
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
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Foreground blobs in a mixed stereo pair of videos (visible and infrared sensors) allow a coarse evaluation of the distances between each blob and the uncalibrated cameras. The main goals of this work are to find common feature points in each type of image and to create pairs of corresponding points in order to obtain coarse positionning of blobs in space. Feature points are found by two methods: the skeleton and the Discrete Curve Evolution (DCE) algorithm. For each method, a feature-based algorithm creates the pairs of points. Blob pairing can help to create those pairs of points. Finally, a RANSAC algorithm filters all pairs of points in order to respect the epipolar geometrical constraints. The median horizontal disparities for each pair of blobs are evaluated with two different ground truths. In most cases, the nearest blob is detected and disparities are as accurate as the background subtraction allows.