The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Jayanthi Sivaswamy - One of the best experts on this subject based on the ideXlab platform.
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moving object detection by multi view geometric techniques from a single camera mounted robot
Intelligent Robots and Systems, 2009Co-Authors: Abhijit Kundu, Madhava K Krishna, Jayanthi SivaswamyAbstract:The ability to detect, and track multiple moving objects like person and other robots, is an important prerequisite for mobile robots working in dynamic indoor environments. We approach this problem by detecting independently moving objects in image sequence from a monocular camera mounted on a robot. We use multi-view geometric constraints to classify a pixel as moving or static. The first constraint, we use, is the epipolar constraint which requires images of static points to lie on the corresponding epipolar lines in subsequent images. In the second constraint, we use the knowledge of the robot motion to estimate a bound in the position of image pixel along the epipolar line. This is capable of detecting moving objects followed by a moving camera in the same direction, a so-Called Degenerate configuration where the epipolar constraint fails. To classify the moving pixels robustly, a Bayesian framework is used to assign a probability that the pixel is stationary or dynamic based on the above geometric properties and the probabilities are updated when the pixels are tracked in subsequent images. The same framework also accounts for the error in estimation of camera motion. Successful and repeatable detection and pursuit of people and other moving objects in realtime with a monocular camera mounted on the Pioneer 3DX, in a cluttered environment confirms the efficacy of the method.
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IROS - Moving object detection by multi-view geometric techniques from a single camera mounted robot
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Abhijit Kundu, K. Madhava Krishna, Jayanthi SivaswamyAbstract:The ability to detect, and track multiple moving objects like person and other robots, is an important prerequisite for mobile robots working in dynamic indoor environments. We approach this problem by detecting independently moving objects in image sequence from a monocular camera mounted on a robot. We use multi-view geometric constraints to classify a pixel as moving or static. The first constraint, we use, is the epipolar constraint which requires images of static points to lie on the corresponding epipolar lines in subsequent images. In the second constraint, we use the knowledge of the robot motion to estimate a bound in the position of image pixel along the epipolar line. This is capable of detecting moving objects followed by a moving camera in the same direction, a so-Called Degenerate configuration where the epipolar constraint fails. To classify the moving pixels robustly, a Bayesian framework is used to assign a probability that the pixel is stationary or dynamic based on the above geometric properties and the probabilities are updated when the pixels are tracked in subsequent images. The same framework also accounts for the error in estimation of camera motion. Successful and repeatable detection and pursuit of people and other moving objects in realtime with a monocular camera mounted on the Pioneer 3DX, in a cluttered environment confirms the efficacy of the method.
Abhijit Kundu - One of the best experts on this subject based on the ideXlab platform.
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moving object detection by multi view geometric techniques from a single camera mounted robot
Intelligent Robots and Systems, 2009Co-Authors: Abhijit Kundu, Madhava K Krishna, Jayanthi SivaswamyAbstract:The ability to detect, and track multiple moving objects like person and other robots, is an important prerequisite for mobile robots working in dynamic indoor environments. We approach this problem by detecting independently moving objects in image sequence from a monocular camera mounted on a robot. We use multi-view geometric constraints to classify a pixel as moving or static. The first constraint, we use, is the epipolar constraint which requires images of static points to lie on the corresponding epipolar lines in subsequent images. In the second constraint, we use the knowledge of the robot motion to estimate a bound in the position of image pixel along the epipolar line. This is capable of detecting moving objects followed by a moving camera in the same direction, a so-Called Degenerate configuration where the epipolar constraint fails. To classify the moving pixels robustly, a Bayesian framework is used to assign a probability that the pixel is stationary or dynamic based on the above geometric properties and the probabilities are updated when the pixels are tracked in subsequent images. The same framework also accounts for the error in estimation of camera motion. Successful and repeatable detection and pursuit of people and other moving objects in realtime with a monocular camera mounted on the Pioneer 3DX, in a cluttered environment confirms the efficacy of the method.
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IROS - Moving object detection by multi-view geometric techniques from a single camera mounted robot
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Abhijit Kundu, K. Madhava Krishna, Jayanthi SivaswamyAbstract:The ability to detect, and track multiple moving objects like person and other robots, is an important prerequisite for mobile robots working in dynamic indoor environments. We approach this problem by detecting independently moving objects in image sequence from a monocular camera mounted on a robot. We use multi-view geometric constraints to classify a pixel as moving or static. The first constraint, we use, is the epipolar constraint which requires images of static points to lie on the corresponding epipolar lines in subsequent images. In the second constraint, we use the knowledge of the robot motion to estimate a bound in the position of image pixel along the epipolar line. This is capable of detecting moving objects followed by a moving camera in the same direction, a so-Called Degenerate configuration where the epipolar constraint fails. To classify the moving pixels robustly, a Bayesian framework is used to assign a probability that the pixel is stationary or dynamic based on the above geometric properties and the probabilities are updated when the pixels are tracked in subsequent images. The same framework also accounts for the error in estimation of camera motion. Successful and repeatable detection and pursuit of people and other moving objects in realtime with a monocular camera mounted on the Pioneer 3DX, in a cluttered environment confirms the efficacy of the method.
K. Madhava Krishna - One of the best experts on this subject based on the ideXlab platform.
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IROS - Moving object detection by multi-view geometric techniques from a single camera mounted robot
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Abhijit Kundu, K. Madhava Krishna, Jayanthi SivaswamyAbstract:The ability to detect, and track multiple moving objects like person and other robots, is an important prerequisite for mobile robots working in dynamic indoor environments. We approach this problem by detecting independently moving objects in image sequence from a monocular camera mounted on a robot. We use multi-view geometric constraints to classify a pixel as moving or static. The first constraint, we use, is the epipolar constraint which requires images of static points to lie on the corresponding epipolar lines in subsequent images. In the second constraint, we use the knowledge of the robot motion to estimate a bound in the position of image pixel along the epipolar line. This is capable of detecting moving objects followed by a moving camera in the same direction, a so-Called Degenerate configuration where the epipolar constraint fails. To classify the moving pixels robustly, a Bayesian framework is used to assign a probability that the pixel is stationary or dynamic based on the above geometric properties and the probabilities are updated when the pixels are tracked in subsequent images. The same framework also accounts for the error in estimation of camera motion. Successful and repeatable detection and pursuit of people and other moving objects in realtime with a monocular camera mounted on the Pioneer 3DX, in a cluttered environment confirms the efficacy of the method.
Alexey P. Vinogradov - One of the best experts on this subject based on the ideXlab platform.
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Formation of Degenerate Band Gaps in Layered Systems
Materials, 2012Co-Authors: Anton I. Ignatov, Miguel Levy, A. M. Merzlikin, Alexey P. VinogradovAbstract:In the review, peculiarities of spectra of one-dimensional photonic crystals made of anisotropic and/or magnetooptic materials are considered. The attention is focused on band gaps of a special type—the so Called Degenerate band gaps which are Degenerate with respect to polarization. Mechanisms of formation and properties of these band gaps are analyzed. Peculiarities of spectra of photonic crystals that arise due to the linkage between band gaps are discussed. Particularly, it is shown that formation of a frozen mode is caused by linkage between Brillouin and Degenerate band gaps. Also, existence of the optical Borrmann effect at the boundaries of Degenerate band gaps and optical Tamm states at the frequencies of Degenerate band gaps are analyzed.
Ron Shamir - One of the best experts on this subject based on the ideXlab platform.
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Degenerate Primer Design
Methods in molecular biology (Clifton N.J.), 2007Co-Authors: Chaim Linhart, Ron ShamirAbstract:A polymerase chain reaction (PCR) primer sequence is Called Degenerate if some of its positions have several possible bases. The degeneracy of the primer is the number of unique sequence combinations it contains. We study the problem of designing a pair of primers with prescribed degeneracy that match a maximum number of given input sequences. Such problems occur, for example, when studying a family of genes that is known only in part or is known in a related species. We discuss the complexity of several versions of the problem and give approximation algorithms for one simplified variant. On the basis of these algorithms, we developed a program Called HYDEN for designing highly Degenerate primers for a set of genomic sequences. We describe HYDEN, and report on its success in several applications for identifying olfactory receptor genes in mammals.
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The Degenerate primer design problem: theory and applications.
Journal of computational biology : a journal of computational molecular cell biology, 2005Co-Authors: Chaim Linhart, Ron ShamirAbstract:A PCR primer sequence is Called Degenerate if some of its positions have several possible bases. The degeneracy of the primer is the number of unique sequence combinations it contains. We study the problem of designing a pair of primers with prescribed degeneracy that match a maximum number of given input sequences. Such problems occur when studying a family of genes that is known only in part, or is known in a related species. We prove that various simplified versions of the problem are hard, show the polynomiality of some restricted cases, and develop approximation algorithms for one variant. Based on these algorithms, we implemented a program Called HYDEN for designing highly Degenerate primers for a set of genomic sequences. We report on the success of the program in several applications, one of which is an experimental scheme for identifying all human olfactory receptor (OR) genes. In that project, HYDEN was used to design primers with degeneracies up to 10(10) that amplified with high specificity many novel genes of that family, tripling the number of OR genes known at the time.
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ISMB - The Degenerate primer design problem.
2002Co-Authors: Chaim Linhart, Ron ShamirAbstract:A PCR primer sequence is Called Degenerate if some of its positions have several possible bases. The degeneracy of the primer is the number of unique sequence combinations it contains. We study the problem of designing a pair of primers with prescribed degeneracy that match a maximum number of given input sequences. Such problems occur when studying a family of genes that is known only in part, or is known in a related species. We prove that various simplified versions of the problem are hard, show the polynomiality of some restricted cases, and develop approximation algorithms for one variant. Based on these algorithms, we implemented a program Called HYDEN for designing highly-Degenerate primers for a set of genomic sequences. We report on the success of the program in an experimental scheme for identifying all human olfactory receptor (OR) genes. In that project, HYDEN was used to design primers with degeneracies up to 10(10) that amplified with high specificity many novel genes of that family, tripling the number of OR genes known at the time.
