Papers by Swarna Ravindran
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017
Traditional point tracking algorithms such as the KLT use local 2D information aggregation for fe... more Traditional point tracking algorithms such as the KLT use local 2D information aggregation for feature detection and tracking, due to which their performance degrades at the object boundaries that separate multiple objects. Recently, CoMaL Features have been proposed that handle such a case. However, they proposed a simple tracking framework where the points are re-detected in each frame and matched. This is inefficient and may also lose many points that are not re-detected in the next frame. We propose a novel tracking algorithm to accurately and efficiently track CoMaL points. For this, the level line segment associated with the CoMaL points is matched to MSER segments in the next frame using shape-based matching and the matches are further filtered using texture-based matching. Experiments show improvements over a simple redetect-and-match framework as well as KLT in terms of speed/accuracy on different real-world applications, especially at the object boundaries.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
Traditional Feature Detectors and Trackers use information aggregation in 2D patches to detect an... more Traditional Feature Detectors and Trackers use information aggregation in 2D patches to detect and match discriminative patches. However, this information does not remain the same at object boundaries when there is object motion against a significantly varying background. In this paper, we propose a new approach for feature detection, tracking and re-detection that gives significantly improved results at the object boundaries. We utilize level lines or iso-intensity curves that often remain stable and can be reliably detected even at the object boundaries, which they often trace. Stable portions of long level lines are detected and points of high curvature are detected on such curves for corner detection. Further, this level line is used to separate the portions belonging to the two objects, which is then used for robust matching of such points. While such CoMaL (Corners on Maximally-stable Level Line Segments) points were found to be much more reliable at the object boundary regions, they perform comparably at the interior regions as well. This is illustrated in exhaustive experiments on realworld datasets.
Feature or interest points typically use information aggregation in 2D patches which does not rem... more Feature or interest points typically use information aggregation in 2D patches which does not remain stable at object boundaries when there is object motion against a significantly varying background. Level or iso-intensity curves are much more stable under such conditions, especially the longer ones. In this paper, we identify stable portions on long iso-curves and detect corners on them. Further, the iso-curve associated with a corner is used to discard portions from the background and improve matching. Such CoMIC (Corners on Maximally-stable Iso-intensity Curves) points yield superior results at the object boundary regions compared to state-of-the-art detectors while performing comparably at the interior regions as well. This is illustrated in exhaustive matching experiments for both boundary and non-boundary regions in applications such as stereo and point tracking for structure from motion in video sequences.
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Papers by Swarna Ravindran