The Experts below are selected from a list of 39081 Experts worldwide ranked by ideXlab platform
Anna Maria Fanelli - One of the best experts on this subject based on the ideXlab platform.
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VIRMA : Visual Image Retrieval by Shape Matching
Lecture Notes in Computer Science, 2006Co-Authors: Giovanna Castellano, Ciro Castiello, Anna Maria FanelliAbstract:The huge amount of image collections connected to multimedia applications has brought forth several approaches to content-based image retrieval, that means retrieving images based on their visual content instead of textual descriptions. In this paper, we present a system, called VIRMA (Visual Image Retrieval by Shape Matching), which combines different techniques from Computer Vision to perform content-based image retrieval based on Shape Matching. The architecture of the VIRMA system is portrayed and algorithms underpinning the developed prototype are briefly described. Application of VIRMA to a database of real-world pictorial images shows its effectiveness in visual image retrieval.
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ISMIS - VIRMA: visual image retrieval by Shape Matching
Lecture Notes in Computer Science, 2006Co-Authors: Giovanna Castellano, Ciro Castiello, Anna Maria FanelliAbstract:The huge amount of image collections connected to multimedia applications has brought forth several approaches to content-based image retrieval, that means retrieving images based on their visual content instead of textual descriptions. In this paper, we present a system, called VIRMA (Visual Image Retrieval by Shape Matching), which combines different techniques from Computer Vision to perform content-based image retrieval based on Shape Matching. The architecture of the VIRMA system is portrayed and algorithms underpinning the developed prototype are briefly described. Application of VIRMA to a database of real-world pictorial images shows its effectiveness in visual image retrieval.
Giovanna Castellano - One of the best experts on this subject based on the ideXlab platform.
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VIRMA : Visual Image Retrieval by Shape Matching
Lecture Notes in Computer Science, 2006Co-Authors: Giovanna Castellano, Ciro Castiello, Anna Maria FanelliAbstract:The huge amount of image collections connected to multimedia applications has brought forth several approaches to content-based image retrieval, that means retrieving images based on their visual content instead of textual descriptions. In this paper, we present a system, called VIRMA (Visual Image Retrieval by Shape Matching), which combines different techniques from Computer Vision to perform content-based image retrieval based on Shape Matching. The architecture of the VIRMA system is portrayed and algorithms underpinning the developed prototype are briefly described. Application of VIRMA to a database of real-world pictorial images shows its effectiveness in visual image retrieval.
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ISMIS - VIRMA: visual image retrieval by Shape Matching
Lecture Notes in Computer Science, 2006Co-Authors: Giovanna Castellano, Ciro Castiello, Anna Maria FanelliAbstract:The huge amount of image collections connected to multimedia applications has brought forth several approaches to content-based image retrieval, that means retrieving images based on their visual content instead of textual descriptions. In this paper, we present a system, called VIRMA (Visual Image Retrieval by Shape Matching), which combines different techniques from Computer Vision to perform content-based image retrieval based on Shape Matching. The architecture of the VIRMA system is portrayed and algorithms underpinning the developed prototype are briefly described. Application of VIRMA to a database of real-world pictorial images shows its effectiveness in visual image retrieval.
Ciro Castiello - One of the best experts on this subject based on the ideXlab platform.
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VIRMA : Visual Image Retrieval by Shape Matching
Lecture Notes in Computer Science, 2006Co-Authors: Giovanna Castellano, Ciro Castiello, Anna Maria FanelliAbstract:The huge amount of image collections connected to multimedia applications has brought forth several approaches to content-based image retrieval, that means retrieving images based on their visual content instead of textual descriptions. In this paper, we present a system, called VIRMA (Visual Image Retrieval by Shape Matching), which combines different techniques from Computer Vision to perform content-based image retrieval based on Shape Matching. The architecture of the VIRMA system is portrayed and algorithms underpinning the developed prototype are briefly described. Application of VIRMA to a database of real-world pictorial images shows its effectiveness in visual image retrieval.
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ISMIS - VIRMA: visual image retrieval by Shape Matching
Lecture Notes in Computer Science, 2006Co-Authors: Giovanna Castellano, Ciro Castiello, Anna Maria FanelliAbstract:The huge amount of image collections connected to multimedia applications has brought forth several approaches to content-based image retrieval, that means retrieving images based on their visual content instead of textual descriptions. In this paper, we present a system, called VIRMA (Visual Image Retrieval by Shape Matching), which combines different techniques from Computer Vision to perform content-based image retrieval based on Shape Matching. The architecture of the VIRMA system is portrayed and algorithms underpinning the developed prototype are briefly described. Application of VIRMA to a database of real-world pictorial images shows its effectiveness in visual image retrieval.
Matthew Turk - One of the best experts on this subject based on the ideXlab platform.
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efficient partial Shape Matching using smith waterman algorithm
Computer Vision and Pattern Recognition, 2008Co-Authors: Longbin Chen, Rogério Schmidt Feris, Matthew TurkAbstract:This paper presents an efficient partial Shape Matching method based on the Smith-Waterman algorithm. For two contours of m and n points respectively, the complexity of our method to find similar parts is only O(mn). In addition to this improvement in efficiency, we also obtain comparable accurate Matching with fewer Shape descriptors. Also, in contrast to arbitrary distance functions that are used by previous methods, we use a probabilistic similarity measurement, p-value, to evaluate the similarity of two Shapes. Our experiments on several public Shape databases indicate that our method outperforms state-of-the-art global and partial Shape Matching algorithms in various scenarios.
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CVPR Workshops - Efficient partial Shape Matching using Smith-Waterman algorithm
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008Co-Authors: Longbin Chen, Rogério Schmidt Feris, Matthew TurkAbstract:This paper presents an efficient partial Shape Matching method based on the Smith-Waterman algorithm. For two contours of m and n points respectively, the complexity of our method to find similar parts is only O(mn). In addition to this improvement in efficiency, we also obtain comparable accurate Matching with fewer Shape descriptors. Also, in contrast to arbitrary distance functions that are used by previous methods, we use a probabilistic similarity measurement, p-value, to evaluate the similarity of two Shapes. Our experiments on several public Shape databases indicate that our method outperforms state-of-the-art global and partial Shape Matching algorithms in various scenarios.
Longbin Chen - One of the best experts on this subject based on the ideXlab platform.
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efficient partial Shape Matching using smith waterman algorithm
Computer Vision and Pattern Recognition, 2008Co-Authors: Longbin Chen, Rogério Schmidt Feris, Matthew TurkAbstract:This paper presents an efficient partial Shape Matching method based on the Smith-Waterman algorithm. For two contours of m and n points respectively, the complexity of our method to find similar parts is only O(mn). In addition to this improvement in efficiency, we also obtain comparable accurate Matching with fewer Shape descriptors. Also, in contrast to arbitrary distance functions that are used by previous methods, we use a probabilistic similarity measurement, p-value, to evaluate the similarity of two Shapes. Our experiments on several public Shape databases indicate that our method outperforms state-of-the-art global and partial Shape Matching algorithms in various scenarios.
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CVPR Workshops - Efficient partial Shape Matching using Smith-Waterman algorithm
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008Co-Authors: Longbin Chen, Rogério Schmidt Feris, Matthew TurkAbstract:This paper presents an efficient partial Shape Matching method based on the Smith-Waterman algorithm. For two contours of m and n points respectively, the complexity of our method to find similar parts is only O(mn). In addition to this improvement in efficiency, we also obtain comparable accurate Matching with fewer Shape descriptors. Also, in contrast to arbitrary distance functions that are used by previous methods, we use a probabilistic similarity measurement, p-value, to evaluate the similarity of two Shapes. Our experiments on several public Shape databases indicate that our method outperforms state-of-the-art global and partial Shape Matching algorithms in various scenarios.
