The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Konstantinos Rapantzikos - One of the best experts on this subject based on the ideXlab platform.
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An embedded saliency map estimator scheme: application to video encoding.
International journal of neural systems, 2007Co-Authors: Nicolas Tsapatsoulis, Konstantinos Rapantzikos, Constantinos S. PattichisAbstract:In this paper we propose a novel saliency-based computational model for visual attention. This model processes both top-down (goal directed) and bottom-up information. Processing in the top-down channel creates the so called skin conspicuity map and emulates the visual search for human faces performed by humans. This is clearly a goal directed task but is generic enough to be context independent. Processing in the bottom-up information channel follows the principles set by Itti et al. but it deviates from them by computing the orientation, intensity and color conspicuity maps within a unified multi-resolution framework based on wavelet subband analysis. In particular, we apply a wavelet based approach for efficient computation of the Topographic Feature maps. Given that wavelets and multiresolution theory are naturally connected the usage of wavelet decomposition for mimicking the center surround process in humans is an obvious choice. However, our implementation goes further. We utilize the wavelet decomposition for inline computation of the Features (such as orientation angles) that are used to create the Topographic Feature maps. The bottom-up Topographic Feature maps and the top-down skin conspicuity map are then combined through a sigmoid function to produce the final saliency map. A prototype of the proposed model was realized through the TMDSDMK642-0E DSP platform as an embedded system allowing real-time operation. For evaluation purposes, in terms of perceived visual quality and video compression improvement, a ROI-based video compression setup was followed. Extended experiments concerning both MPEG-1 as well as low bit-rate MPEG-4 video encoding were conducted showing significant improvement in video compression efficiency without perceived deterioration in visual quality.
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ICANN (2) - Wavelet based estimation of saliency maps in visual attention algorithms
Artificial Neural Networks – ICANN 2006, 2006Co-Authors: Nicolas Tsapatsoulis, Konstantinos RapantzikosAbstract:This paper deals with the problem of saliency map estimation in computational models of visual attention. In particular, we propose a wavelet based approach for efficient computation of the Topographic Feature maps. Given that wavelets and multiresolution theory are naturally connected the usage of wavelet decomposition for mimicking the center surround process in humans is an obvious choice. However, our proposal goes further. We utilize the wavelet decomposition for inline computation of the Features (such as orientation) that are used to create the Topographic Feature maps. Topographic Feature maps are then combined through a sigmoid function to produce the final saliency map. The computational model we use is based on the Feature Integration Theory of Treisman et al and follows the computational philosophy of this theory proposed by Itti et al. A series of experiments, conducted in a video encoding setup, show that the proposed method compares well against other implementations found in the literature both in terms of visual trials and computational complexity.
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Wavelet Based Estimation of Saliency Maps in Visual Attention Algorithms
Lecture Notes in Computer Science, 2006Co-Authors: Nicolas Tsapatsoulis, Konstantinos RapantzikosAbstract:This paper deals with the problem of saliency map estimation in computational models of visual attention. In particular, we propose a wavelet based approach for efficient computation of the Topographic Feature maps. Given that wavelets and multiresolution theory are naturally connected the usage of wavelet decomposition for mimicking the center surround process in humans is an obvious choice. However, our proposal goes further. We utilize the wavelet decomposition for inline computation of the Features (such as orientation) that are used to create the Topographic Feature maps. Topographic Feature maps are then combined through a sigmoid function to produce the final saliency map. The computational model we use is based on the Feature Integration Theory of Treisman et al and follows the computational philosophy of this theory proposed by Itti et al. A series of experiments, conducted in a video encoding setup, show that the proposed method compares well against other implementations found in the literature both in terms of visual trials and computational complexity.
Nicolas Tsapatsoulis - One of the best experts on this subject based on the ideXlab platform.
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An embedded saliency map estimator scheme: application to video encoding.
International journal of neural systems, 2007Co-Authors: Nicolas Tsapatsoulis, Konstantinos Rapantzikos, Constantinos S. PattichisAbstract:In this paper we propose a novel saliency-based computational model for visual attention. This model processes both top-down (goal directed) and bottom-up information. Processing in the top-down channel creates the so called skin conspicuity map and emulates the visual search for human faces performed by humans. This is clearly a goal directed task but is generic enough to be context independent. Processing in the bottom-up information channel follows the principles set by Itti et al. but it deviates from them by computing the orientation, intensity and color conspicuity maps within a unified multi-resolution framework based on wavelet subband analysis. In particular, we apply a wavelet based approach for efficient computation of the Topographic Feature maps. Given that wavelets and multiresolution theory are naturally connected the usage of wavelet decomposition for mimicking the center surround process in humans is an obvious choice. However, our implementation goes further. We utilize the wavelet decomposition for inline computation of the Features (such as orientation angles) that are used to create the Topographic Feature maps. The bottom-up Topographic Feature maps and the top-down skin conspicuity map are then combined through a sigmoid function to produce the final saliency map. A prototype of the proposed model was realized through the TMDSDMK642-0E DSP platform as an embedded system allowing real-time operation. For evaluation purposes, in terms of perceived visual quality and video compression improvement, a ROI-based video compression setup was followed. Extended experiments concerning both MPEG-1 as well as low bit-rate MPEG-4 video encoding were conducted showing significant improvement in video compression efficiency without perceived deterioration in visual quality.
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ICANN (2) - Wavelet based estimation of saliency maps in visual attention algorithms
Artificial Neural Networks – ICANN 2006, 2006Co-Authors: Nicolas Tsapatsoulis, Konstantinos RapantzikosAbstract:This paper deals with the problem of saliency map estimation in computational models of visual attention. In particular, we propose a wavelet based approach for efficient computation of the Topographic Feature maps. Given that wavelets and multiresolution theory are naturally connected the usage of wavelet decomposition for mimicking the center surround process in humans is an obvious choice. However, our proposal goes further. We utilize the wavelet decomposition for inline computation of the Features (such as orientation) that are used to create the Topographic Feature maps. Topographic Feature maps are then combined through a sigmoid function to produce the final saliency map. The computational model we use is based on the Feature Integration Theory of Treisman et al and follows the computational philosophy of this theory proposed by Itti et al. A series of experiments, conducted in a video encoding setup, show that the proposed method compares well against other implementations found in the literature both in terms of visual trials and computational complexity.
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Wavelet Based Estimation of Saliency Maps in Visual Attention Algorithms
Lecture Notes in Computer Science, 2006Co-Authors: Nicolas Tsapatsoulis, Konstantinos RapantzikosAbstract:This paper deals with the problem of saliency map estimation in computational models of visual attention. In particular, we propose a wavelet based approach for efficient computation of the Topographic Feature maps. Given that wavelets and multiresolution theory are naturally connected the usage of wavelet decomposition for mimicking the center surround process in humans is an obvious choice. However, our proposal goes further. We utilize the wavelet decomposition for inline computation of the Features (such as orientation) that are used to create the Topographic Feature maps. Topographic Feature maps are then combined through a sigmoid function to produce the final saliency map. The computational model we use is based on the Feature Integration Theory of Treisman et al and follows the computational philosophy of this theory proposed by Itti et al. A series of experiments, conducted in a video encoding setup, show that the proposed method compares well against other implementations found in the literature both in terms of visual trials and computational complexity.
Siva Chandra - One of the best experts on this subject based on the ideXlab platform.
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An alternative curvature measure for Topographic Feature detection
Lecture Notes in Computer Science, 2006Co-Authors: Jayanthi Sivaswamy, Gopal Datt Joshi, Siva ChandraAbstract:The notion of Topographic Features like ridges, trenches, hills, etc. is formed by visualising the 2D image function as a surface in 3D space. Hence, properties of such a surface can be used to detect Features from images. One such property, the curvature of the image surface, can be used to detect Features characterised by a sharp bend in the surface. Curvature based Feature detection requires an efficient technique to estimate/calculate the surface curvature. In this paper, we present an alternative measure for curvature and provide an analysis of the same to determine its scope. Feature detection algorithms using this measure are formulated and two applications are chosen to demonstrate their performance. The results show good potential of the proposed measure in terms of efficiency and scope.
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ICVGIP - An alternative curvature measure for Topographic Feature detection
Computer Vision Graphics and Image Processing, 2006Co-Authors: Jayanthi Sivaswamy, Gopal Datt Joshi, Siva ChandraAbstract:The notion of Topographic Features like ridges, trenches, hills, etc. is formed by visualising the 2D image function as a surface in 3D space. Hence, properties of such a surface can be used to detect Features from images. One such property, the curvature of the image surface, can be used to detect Features characterised by a sharp bend in the surface. Curvature based Feature detection requires an efficient technique to estimate/calculate the surface curvature. In this paper, we present an alternative measure for curvature and provide an analysis of the same to determine its scope. Feature detection algorithms using this measure are formulated and two applications are chosen to demonstrate their performance. The results show good potential of the proposed measure in terms of efficiency and scope.
William Robson Schwartz - One of the best experts on this subject based on the ideXlab platform.
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Topographic Feature identification based on triangular meshes
Computer Analysis of Images and Patterns, 2001Co-Authors: Helio Pedrini, William Robson SchwartzAbstract:A new method for extracting Topographic Features from images approximated by triangular meshes is presented. Peaks, pits, passes, ridges, valleys, and flat regions are defined by considering the topological and geometric relationship between the triangular elements. The approach is suitable for several computer-based recognition tasks, such as navigation of autonomous vehicles, planetary exploration, and reverse engineering. The method has been applied to a wide range of images, producing very promising results.
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CAIP - Topographic Feature Identification Based on Triangular Meshes
Computer Analysis of Images and Patterns, 2001Co-Authors: Helio Pedrini, William Robson SchwartzAbstract:A new method for extracting Topographic Features from images approximated by triangular meshes is presented. Peaks, pits, passes, ridges, valleys, and flat regions are defined by considering the topological and geometric relationship between the triangular elements. The approach is suitable for several computer-based recognition tasks, such as navigation of autonomous vehicles, planetary exploration, and reverse engineering. The method has been applied to a wide range of images, producing very promising results.
Jayanthi Sivaswamy - One of the best experts on this subject based on the ideXlab platform.
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An alternative curvature measure for Topographic Feature detection
Lecture Notes in Computer Science, 2006Co-Authors: Jayanthi Sivaswamy, Gopal Datt Joshi, Siva ChandraAbstract:The notion of Topographic Features like ridges, trenches, hills, etc. is formed by visualising the 2D image function as a surface in 3D space. Hence, properties of such a surface can be used to detect Features from images. One such property, the curvature of the image surface, can be used to detect Features characterised by a sharp bend in the surface. Curvature based Feature detection requires an efficient technique to estimate/calculate the surface curvature. In this paper, we present an alternative measure for curvature and provide an analysis of the same to determine its scope. Feature detection algorithms using this measure are formulated and two applications are chosen to demonstrate their performance. The results show good potential of the proposed measure in terms of efficiency and scope.
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ICVGIP - An alternative curvature measure for Topographic Feature detection
Computer Vision Graphics and Image Processing, 2006Co-Authors: Jayanthi Sivaswamy, Gopal Datt Joshi, Siva ChandraAbstract:The notion of Topographic Features like ridges, trenches, hills, etc. is formed by visualising the 2D image function as a surface in 3D space. Hence, properties of such a surface can be used to detect Features from images. One such property, the curvature of the image surface, can be used to detect Features characterised by a sharp bend in the surface. Curvature based Feature detection requires an efficient technique to estimate/calculate the surface curvature. In this paper, we present an alternative measure for curvature and provide an analysis of the same to determine its scope. Feature detection algorithms using this measure are formulated and two applications are chosen to demonstrate their performance. The results show good potential of the proposed measure in terms of efficiency and scope.
