The Experts below are selected from a list of 2982 Experts worldwide ranked by ideXlab platform
Bhargab B. Bhattacharya - One of the best experts on this subject based on the ideXlab platform.
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Thinning-free Polygonal Approximation of Thick Digital Curves Using Cellular Envelope
Electronic Letters on Computer Vision and Image Analysis, 2008Co-Authors: Partha Bhowmick, Arindam Biswas, Bhargab B. BhattacharyaAbstract:Since the inception of successful rasterization of curves and objects in the digital space, several algorithms have been proposed for approximating a given digital curve. All these algorithms, however, resort to thinning as preprocessing before approximating a digital curve with changing thickness. Described in this paper is a novel thinning-free algorithm for Polygonal Approximation of an arbitrarily thick digital curve, using the concept of “cellular envelope”, which is newly introduced in this paper. The cellular envelope, defined as the smallest set of cells containing the given curve, and hence bounded by two tightest (inner and outer) isothetic polygons, is constructed using a combinatorial technique. This envelope, in turn, is analyzed to determine a Polygonal Approximation of the curve as a sequence of cells using certain attributes of digital straightness. Since a real-world curve=curve-shaped object with varying thickness, unexpected disconnectedness, noisy information, etc., is unsuitable for the existing algorithms on Polygonal Approximation, the curve is encapsulated by the cellular envelope to enable the Polygonal Approximation. Owing to the implicit Euclidean-free metrics and combinatorial properties prevailing in the cellular plane, implementation of the proposed algorithm involves primitive integer operations only, leading to fast execution of the algorithm. Experimental results that include output polygons for different values of the Approximation parameter corresponding to several real-world digital curves, a couple of measures on the quality of Approximation, comparative results related with two other well-referred algorithms, and CPU times, have been presented to demonstrate the elegance and efficacy of the proposed algorithm.
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fast Polygonal Approximation of digital curves using relaxed straightness properties
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007Co-Authors: Partha Bhowmick, Bhargab B. BhattacharyaAbstract:Several existing digital straight line segment (DSS) recognition algorithms can be used to determine the digital straightness of a given one-pixel-thick digital curve. Because of the inherent geometric constraints of digital straightness, these algorithms often produce a large number of segments to cover a given digital curve representing a real-life object/image. Thus, a curve segment, which is not exactly digitally straight but appears to be visually straight, is fragmented into multiple DSS when these algorithms are run. In this paper, a new concept of approximate straightness is introduced by relaxing certain conditions of DSS, and an algorithm is described to extract those segments from a digital curve. The number of such segments required to cover the curve is found to be significantly fewer than that of the exact DSS cover. As a result, the data set required for representing a curve also reduces to a large extent. The extracted set of segments can further be combined to determine a compact Polygonal Approximation of a digital curve based on certain Approximation criteria and a specified error tolerance. The proposed algorithm involves only primitive integer operations and, thus, runs very fast compared to those based on exact DSS. The overall time complexity becomes linear in the number of points present in the representative set. Experimental results on several digital curves demonstrate the speed, elegance, and efficacy of the proposed method.
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ICVGIP - PACE: Polygonal Approximation of thick digital curves using cellular envelope
Computer Vision Graphics and Image Processing, 2006Co-Authors: Partha Bhowmick, Arindam Biswas, Bhargab B. BhattacharyaAbstract:A novel algorithm to derive an approximate cellular envelope of an arbitrarily thick digital curve on a 2D grid is proposed in this paper. The concept of “cellular envelope” is newly introduced in this paper, which is defined as the smallest set of cells containing the given curve, and hence bounded by two tightest (inner and outer) isothetic polygons on the grid. Contrary to the existing algorithms that use thinning as preprocessing for a digital curve with changing thickness, in our work, an optimal cellular envelope (smallest in the number of constituent cells) that entirely contains the given curve is constructed based on a combinatorial technique. The envelope, in turn, is further analyzed to determine Polygonal Approximation of the curve as a sequence of cells using certain attributes of digital straightness. Since a real-world curve/curve-shaped object with varying thickness and unexpected disconnectedness is unsuitable for the existing algorithms on Polygonal Approximation, the curve is encapsulated by the cellular envelope to enable the Polygonal Approximation. Owing to the implicit Euclidean-free metrics and combinatorial properties prevailing in the cellular plane, implementation of the proposed algorithm involves primitive integer operations only, leading to fast execution of the algorithm. Experimental results including CPU time reinforce the elegance and efficacy of the proposed algorithm.
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pace Polygonal Approximation of thick digital curves using cellular envelope
Lecture Notes in Computer Science, 2006Co-Authors: Partha Bhowmick, Arindam Biswas, Bhargab B. BhattacharyaAbstract:A novel algorithm to derive an approximate cellular envelope of an arbitrarily thick digital curve on a 2D grid is proposed in this paper. The concept of cellular envelope is newly introduced in this paper, which is defined as the smallest set of cells containing the given curve, and hence bounded by two tightest (inner and outer) isothetic polygons on the grid. Contrary to the existing algorithms that use thinning as preprocessing for a digital curve with changing thickness, in our work, an optimal cellular envelope (smallest in the number of constituent cells) that entirely contains the given curve is constructed based on a combinatorial technique. The envelope, in turn, is further analyzed to determine Polygonal Approximation of the curve as a sequence of cells using certain attributes of digital straightness. Since a real-world curve/curve-shaped object with varying thickness and unexpected disconnectedness is unsuitable for the existing algorithms on Polygonal Approximation, the curve is encapsulated by the cellular envelope to enable the Polygonal Approximation. Owing to the implicit Euclidean-free metrics and combinatorial properties prevailing in the cellular plane, implementation of the proposed algorithm involves primitive integer operations only, leading to fast execution of the algorithm. Experimental results including CPU time reinforce the elegance and efficacy of the proposed algorithm.
Yalan Zhou - One of the best experts on this subject based on the ideXlab platform.
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Improved stochastic competitive Hopfield network for Polygonal Approximation
Expert Systems With Applications, 2011Co-Authors: Jiahai Wang, Zhanghui Kuang, Yalan Zhou, Rong-long WangAbstract:The Polygonal Approximation is an important topic in the area of pattern recognition, computer graphics and computer vision. This paper firstly proposes a new computational energy function to properly express the objective of the Polygonal Approximation problem based on competitive Hopfield neural network (CHNN), and then proposes a stochastic CHNN (SCHNN) by introducing stochastic dynamics into the CHNN to help the network escape from local minima. In order to further improve the performance of the SCHNN, a multi-start strategy or re-start mechanism is introduced. The multi-start strategy or re-start mechanism super-imposed on the SCHNN is characterized by alternating phases of cooling and reheating the stochastic dynamics, thus provides a means to achieve an effective dynamic or oscillating balance between intensification and diversification during the search. The proposed multi-start SCHNN (MS-SCHNN) is tested on a set of benchmark problems and several large size test instances. Simulation results show that the proposed MS-SCHNN is better than or competitive with several typical neural network algorithms such as CHNN and transiently chaotic neural network, metaheuristic algorithms such as genetic algorithms, and 12 commonly referred state-of-the-art algorithms specifically developed for the Polygonal Approximation. Furthermore, the chain codes and results of the proposed algorithm for the large size curves are also provided.
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discrete particle swarm optimization based on estimation of distribution for Polygonal Approximation problems
Expert Systems With Applications, 2009Co-Authors: Jiahai Wang, Zhanghui Kuang, Xinshun Xu, Yalan ZhouAbstract:The Polygonal Approximation is an important topic in the area of pattern recognition, computer graphics and computer vision. This paper presents a novel discrete particle swarm optimization algorithm based on estimation of distribution (DPSO-EDA), for two types of Polygonal Approximation problems. Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The DPSO-EDA incorporates the global statistical information collected from local best solution of all particles into the particle swarm optimization and therefore each particle has comprehensive learning and search ability. Further, constraint handling methods based on the split-and-merge local search is introduced to satisfy the constraints of the two types of problems. Simulation results on several benchmark problems show that the DPSO-EDA is better than previous methods such as genetic algorithm, tabu search, particle swarm optimization, and ant colony optimization.
Eliang Chen - One of the best experts on this subject based on the ideXlab platform.
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Polygonal Approximation using a competitive hopfield neural network
Pattern Recognition, 1994Co-Authors: Pauchoo Chung, Chingtsorng Tsai, Eliang ChenAbstract:Abstract Polygonal Approximation plays an important role in pattern recognition and computer vision. In this paper, a parallel method using a Competitive Hopfield Neural Network (CHNN) is proposed for Polygonal Approximation. Based on the CHNN, the Polygonal Approximation is regarded as a minimization of a criterion function which is defined as the arc-to-chord deviation between the curve and the polygon. The CHNN differs from the original Hopfield network in that a competitive winner-take-all mechanism is imposed. The winner-take-all mechanism adeptly precludes the necessity of determining the values for the weighting factors in the energy function in maintaining a feasible result. The proposed method is compared to several existing methods by the Approximation error norms L2 and L∞ with the result that promising Approximation polygons are obtained.
Jiahai Wang - One of the best experts on this subject based on the ideXlab platform.
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Improved stochastic competitive Hopfield network for Polygonal Approximation
Expert Systems With Applications, 2011Co-Authors: Jiahai Wang, Zhanghui Kuang, Yalan Zhou, Rong-long WangAbstract:The Polygonal Approximation is an important topic in the area of pattern recognition, computer graphics and computer vision. This paper firstly proposes a new computational energy function to properly express the objective of the Polygonal Approximation problem based on competitive Hopfield neural network (CHNN), and then proposes a stochastic CHNN (SCHNN) by introducing stochastic dynamics into the CHNN to help the network escape from local minima. In order to further improve the performance of the SCHNN, a multi-start strategy or re-start mechanism is introduced. The multi-start strategy or re-start mechanism super-imposed on the SCHNN is characterized by alternating phases of cooling and reheating the stochastic dynamics, thus provides a means to achieve an effective dynamic or oscillating balance between intensification and diversification during the search. The proposed multi-start SCHNN (MS-SCHNN) is tested on a set of benchmark problems and several large size test instances. Simulation results show that the proposed MS-SCHNN is better than or competitive with several typical neural network algorithms such as CHNN and transiently chaotic neural network, metaheuristic algorithms such as genetic algorithms, and 12 commonly referred state-of-the-art algorithms specifically developed for the Polygonal Approximation. Furthermore, the chain codes and results of the proposed algorithm for the large size curves are also provided.
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discrete particle swarm optimization based on estimation of distribution for Polygonal Approximation problems
Expert Systems With Applications, 2009Co-Authors: Jiahai Wang, Zhanghui Kuang, Xinshun Xu, Yalan ZhouAbstract:The Polygonal Approximation is an important topic in the area of pattern recognition, computer graphics and computer vision. This paper presents a novel discrete particle swarm optimization algorithm based on estimation of distribution (DPSO-EDA), for two types of Polygonal Approximation problems. Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The DPSO-EDA incorporates the global statistical information collected from local best solution of all particles into the particle swarm optimization and therefore each particle has comprehensive learning and search ability. Further, constraint handling methods based on the split-and-merge local search is introduced to satisfy the constraints of the two types of problems. Simulation results on several benchmark problems show that the DPSO-EDA is better than previous methods such as genetic algorithm, tabu search, particle swarm optimization, and ant colony optimization.
Partha Bhowmick - One of the best experts on this subject based on the ideXlab platform.
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Thinning-free Polygonal Approximation of Thick Digital Curves Using Cellular Envelope
Electronic Letters on Computer Vision and Image Analysis, 2008Co-Authors: Partha Bhowmick, Arindam Biswas, Bhargab B. BhattacharyaAbstract:Since the inception of successful rasterization of curves and objects in the digital space, several algorithms have been proposed for approximating a given digital curve. All these algorithms, however, resort to thinning as preprocessing before approximating a digital curve with changing thickness. Described in this paper is a novel thinning-free algorithm for Polygonal Approximation of an arbitrarily thick digital curve, using the concept of “cellular envelope”, which is newly introduced in this paper. The cellular envelope, defined as the smallest set of cells containing the given curve, and hence bounded by two tightest (inner and outer) isothetic polygons, is constructed using a combinatorial technique. This envelope, in turn, is analyzed to determine a Polygonal Approximation of the curve as a sequence of cells using certain attributes of digital straightness. Since a real-world curve=curve-shaped object with varying thickness, unexpected disconnectedness, noisy information, etc., is unsuitable for the existing algorithms on Polygonal Approximation, the curve is encapsulated by the cellular envelope to enable the Polygonal Approximation. Owing to the implicit Euclidean-free metrics and combinatorial properties prevailing in the cellular plane, implementation of the proposed algorithm involves primitive integer operations only, leading to fast execution of the algorithm. Experimental results that include output polygons for different values of the Approximation parameter corresponding to several real-world digital curves, a couple of measures on the quality of Approximation, comparative results related with two other well-referred algorithms, and CPU times, have been presented to demonstrate the elegance and efficacy of the proposed algorithm.
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fast Polygonal Approximation of digital curves using relaxed straightness properties
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007Co-Authors: Partha Bhowmick, Bhargab B. BhattacharyaAbstract:Several existing digital straight line segment (DSS) recognition algorithms can be used to determine the digital straightness of a given one-pixel-thick digital curve. Because of the inherent geometric constraints of digital straightness, these algorithms often produce a large number of segments to cover a given digital curve representing a real-life object/image. Thus, a curve segment, which is not exactly digitally straight but appears to be visually straight, is fragmented into multiple DSS when these algorithms are run. In this paper, a new concept of approximate straightness is introduced by relaxing certain conditions of DSS, and an algorithm is described to extract those segments from a digital curve. The number of such segments required to cover the curve is found to be significantly fewer than that of the exact DSS cover. As a result, the data set required for representing a curve also reduces to a large extent. The extracted set of segments can further be combined to determine a compact Polygonal Approximation of a digital curve based on certain Approximation criteria and a specified error tolerance. The proposed algorithm involves only primitive integer operations and, thus, runs very fast compared to those based on exact DSS. The overall time complexity becomes linear in the number of points present in the representative set. Experimental results on several digital curves demonstrate the speed, elegance, and efficacy of the proposed method.
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ICVGIP - PACE: Polygonal Approximation of thick digital curves using cellular envelope
Computer Vision Graphics and Image Processing, 2006Co-Authors: Partha Bhowmick, Arindam Biswas, Bhargab B. BhattacharyaAbstract:A novel algorithm to derive an approximate cellular envelope of an arbitrarily thick digital curve on a 2D grid is proposed in this paper. The concept of “cellular envelope” is newly introduced in this paper, which is defined as the smallest set of cells containing the given curve, and hence bounded by two tightest (inner and outer) isothetic polygons on the grid. Contrary to the existing algorithms that use thinning as preprocessing for a digital curve with changing thickness, in our work, an optimal cellular envelope (smallest in the number of constituent cells) that entirely contains the given curve is constructed based on a combinatorial technique. The envelope, in turn, is further analyzed to determine Polygonal Approximation of the curve as a sequence of cells using certain attributes of digital straightness. Since a real-world curve/curve-shaped object with varying thickness and unexpected disconnectedness is unsuitable for the existing algorithms on Polygonal Approximation, the curve is encapsulated by the cellular envelope to enable the Polygonal Approximation. Owing to the implicit Euclidean-free metrics and combinatorial properties prevailing in the cellular plane, implementation of the proposed algorithm involves primitive integer operations only, leading to fast execution of the algorithm. Experimental results including CPU time reinforce the elegance and efficacy of the proposed algorithm.
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pace Polygonal Approximation of thick digital curves using cellular envelope
Lecture Notes in Computer Science, 2006Co-Authors: Partha Bhowmick, Arindam Biswas, Bhargab B. BhattacharyaAbstract:A novel algorithm to derive an approximate cellular envelope of an arbitrarily thick digital curve on a 2D grid is proposed in this paper. The concept of cellular envelope is newly introduced in this paper, which is defined as the smallest set of cells containing the given curve, and hence bounded by two tightest (inner and outer) isothetic polygons on the grid. Contrary to the existing algorithms that use thinning as preprocessing for a digital curve with changing thickness, in our work, an optimal cellular envelope (smallest in the number of constituent cells) that entirely contains the given curve is constructed based on a combinatorial technique. The envelope, in turn, is further analyzed to determine Polygonal Approximation of the curve as a sequence of cells using certain attributes of digital straightness. Since a real-world curve/curve-shaped object with varying thickness and unexpected disconnectedness is unsuitable for the existing algorithms on Polygonal Approximation, the curve is encapsulated by the cellular envelope to enable the Polygonal Approximation. Owing to the implicit Euclidean-free metrics and combinatorial properties prevailing in the cellular plane, implementation of the proposed algorithm involves primitive integer operations only, leading to fast execution of the algorithm. Experimental results including CPU time reinforce the elegance and efficacy of the proposed algorithm.
