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Author

Bimal Kumar Ray

Other affiliations: Indian Statistical Institute
Bio: Bimal Kumar Ray is an academic researcher from VIT University. The author has contributed to research in topics: Polygon & Line segment. The author has an hindex of 7, co-authored 32 publications receiving 360 citations. Previous affiliations of Bimal Kumar Ray include Indian Statistical Institute.

Papers
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Journal ArticleDOI
TL;DR: A technique to determine optimal polygons of digital curves is proposed that determines the longest possible line segments with the minimum possible error using the L 1 norm.

89 citations

Journal ArticleDOI
TL;DR: The present technique introduces the concept of an asymmetric region of support and k - l -cosine, which needs no input parameter and remains reliable even when features of multiple size are present.

76 citations

Journal ArticleDOI
TL;DR: The initial segmentation is done by introducing the concept of rank of a point and the procedure generates polygons that are insensitive to rotation and scales and remains reliable in presence of noise.

69 citations

Journal ArticleDOI
TL;DR: A technique for smoothing a curve adaptively based on the roughness present in the curve is suggested, which has been applied on a number of digital curves and the results have been compared with those of the recent work.

44 citations

Journal ArticleDOI
TL;DR: A scale-space map showing the location of the maxima of absolute curvature over iterations is proposed and the corner detector has been applied successfully on different digital curves even in presence of additive white Gaussian noise and at varying orientations.

29 citations


Cited by
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Journal ArticleDOI
TL;DR: A new heuristic for feature detection is presented and, using machine learning, a feature detector is derived from this which can fully process live PAL video using less than 5 percent of the available processing time.
Abstract: The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is important because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection and, using machine learning, we derive a feature detector from this which can fully process live PAL video using less than 5 percent of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115 percent, SIFT 195 percent). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that, despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and of very high quality.

1,847 citations

Journal ArticleDOI
TL;DR: A measure which combines the relative fidelity and efficiency of a curve segmentation is described, and this measure is used to compare the application of 23 algorithms to a curve first used by Teh and Chin (1989).
Abstract: Given the enormous number of available methods for finding polygonal approximations to curves techniques are required to assess different algorithms. Some of the standard approaches are shown to be unsuitable if the approximations contain varying numbers of lines. Instead, we suggest assessing an algorithm's results relative to an optimal polygon, and describe a measure which combines the relative fidelity and efficiency of a curve segmentation. We use this measure to compare the application of 23 algorithms to a curve first used by Teh and Chin (1989); their integral square errors (ISEs) are assessed relative to the optimal ISE. In addition, using an example of pose estimation, it is shown how goal-directed evaluation can be used to select an appropriate assessment criterion.

263 citations

Journal ArticleDOI
TL;DR: A curvature-based corner detector that detects both fine and coarse features accurately at low computational cost and forms extremely well in both fields is proposed.
Abstract: This paper proposes a curvature-based corner detector that detects both fine and coarse features accurately at low computational cost. First, it extracts contours from a Canny edge map. Second, it com- putes the absolute value of curvature of each point on a contour at a low scale and regards local maxima of absolute curvature as initial corner candidates. Third, it uses an adaptive curvature threshold to remove round corners from the initial list. Finally, false corners due to quantiza- tion noise and trivial details are eliminated by evaluating the angles of corner candidates in a dynamic region of support. The proposed detector was compared with popular corner detectors on planar curves and gray- level images, respectively, in a subjective manner as well as with a fea- ture correspondence test. Results reveal that the proposed detector per- forms extremely well in both fields. © 2008 Society of Photo-Optical

246 citations

Journal ArticleDOI
TL;DR: This work proposes an efficient and deterministic method, TopDom, to identify TDs, along with a set of statistical methods for evaluating their quality, and reveals that the locations of housekeeping genes are closely associated with cross-tissue conserved TDs.
Abstract: Genome-wide proximity ligation assays allow the identification of chromatin contacts at unprecedented resolution. Several studies reveal that mammalian chromosomes are composed of topological domains (TDs) in sub-mega base resolution, which appear to be conserved across cell types and to some extent even between organisms. Identifying topological domains is now an important step toward understanding the structure and functions of spatial genome organization. However, current methods for TD identification demand extensive computational resources, require careful tuning and/or encounter inconsistencies in results. In this work, we propose an efficient and deterministic method, TopDom, to identify TDs, along with a set of statistical methods for evaluating their quality. TopDom is much more efficient than existing methods and depends on just one intuitive parameter, a window size, for which we provide easy-to-implement optimization guidelines. TopDom also identifies more and higher quality TDs than the popular directional index algorithm. The TDs identified by TopDom provide strong support for the cross-tissue TD conservation. Finally, our analysis reveals that the locations of housekeeping genes are closely associated with cross-tissue conserved TDs. The software package and source codes of TopDom are available athttp://zhoulab.usc.edu/TopDom/.

222 citations

Journal ArticleDOI
TL;DR: A complete corner detection technique based on the chord-to-point distance accumulation (CPDA) for the discrete curvature estimation and shows that the proposed technique performs better than the existing CSS-based and other related methods in terms of both average repeatability and localization error.
Abstract: Many contour-based image corner detectors are based on the curvature scale-space (CSS). We identify the weaknesses of the CSS-based detectors. First, the ldquocurvaturerdquo itself by its ldquodefinitionrdquo is very much sensitive to the local variation and noise on the curve, unless an appropriate smoothing is carried out beforehand. In addition, the calculation of curvature involves derivatives of up to second order, which may cause instability and errors in the result. Second, the Gaussian smoothing causes changes to the curve and it is difficult to select an appropriate smoothing-scale, resulting in poor performance of the CSS corner detection technique. We propose a complete corner detection technique based on the chord-to-point distance accumulation (CPDA) for the discrete curvature estimation. The CPDA discrete curvature estimation technique is less sensitive to the local variation and noise on the curve. Moreover, it does not have the undesirable effect of the Gaussian smoothing. We provide a comprehensive performance study. Our experiments showed that the proposed technique performs better than the existing CSS-based and other related methods in terms of both average repeatability and localization error.

155 citations