The Experts below are selected from a list of 3444 Experts worldwide ranked by ideXlab platform
M L A Lourakis - One of the best experts on this subject based on the ideXlab platform.
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localizing unordered panoramic images using the Levenshtein Distance
International Conference on Computer Vision, 2007Co-Authors: Damien Michel, Antonis A Argyros, M L A LourakisAbstract:This paper proposes a feature-based method for recovering the relative positions of the viewpoints of a set of panoramic images for which no a priori order information is available, along with certain structure information regarding the imaged environment. The proposed approach operates incrementally, employing the Levenshtein Distance to deduce the spatial proximity of image viewpoints and thus determine the order in which images should be processed. The Levenshtein Distance also provides matches between images, from which their underlying environment points can be recovered. Recovered points that are visible in multiple views permit the localization of more views which in turn allow the recovery of more points. The process repeats until all views have been localized. Periodic refinement of the reconstruction with the aid of bundle adjustment, distributes the reconstruction errors among images. The method is demonstrated on several unordered sets of panoramic images obtained in an indoor environment.
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ICCV - Localizing Unordered Panoramic Images Using the Levenshtein Distance
2007 IEEE 11th International Conference on Computer Vision, 2007Co-Authors: Damien Michel, Antonis A Argyros, M L A LourakisAbstract:This paper proposes a feature-based method for recovering the relative positions of the viewpoints of a set of panoramic images for which no a priori order information is available, along with certain structure information regarding the imaged environment. The proposed approach operates incrementally, employing the Levenshtein Distance to deduce the spatial proximity of image viewpoints and thus determine the order in which images should be processed. The Levenshtein Distance also provides matches between images, from which their underlying environment points can be recovered. Recovered points that are visible in multiple views permit the localization of more views which in turn allow the recovery of more points. The process repeats until all views have been localized. Periodic refinement of the reconstruction with the aid of bundle adjustment, distributes the reconstruction errors among images. The method is demonstrated on several unordered sets of panoramic images obtained in an indoor environment.
Damien Michel - One of the best experts on this subject based on the ideXlab platform.
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localizing unordered panoramic images using the Levenshtein Distance
International Conference on Computer Vision, 2007Co-Authors: Damien Michel, Antonis A Argyros, M L A LourakisAbstract:This paper proposes a feature-based method for recovering the relative positions of the viewpoints of a set of panoramic images for which no a priori order information is available, along with certain structure information regarding the imaged environment. The proposed approach operates incrementally, employing the Levenshtein Distance to deduce the spatial proximity of image viewpoints and thus determine the order in which images should be processed. The Levenshtein Distance also provides matches between images, from which their underlying environment points can be recovered. Recovered points that are visible in multiple views permit the localization of more views which in turn allow the recovery of more points. The process repeats until all views have been localized. Periodic refinement of the reconstruction with the aid of bundle adjustment, distributes the reconstruction errors among images. The method is demonstrated on several unordered sets of panoramic images obtained in an indoor environment.
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ICCV - Localizing Unordered Panoramic Images Using the Levenshtein Distance
2007 IEEE 11th International Conference on Computer Vision, 2007Co-Authors: Damien Michel, Antonis A Argyros, M L A LourakisAbstract:This paper proposes a feature-based method for recovering the relative positions of the viewpoints of a set of panoramic images for which no a priori order information is available, along with certain structure information regarding the imaged environment. The proposed approach operates incrementally, employing the Levenshtein Distance to deduce the spatial proximity of image viewpoints and thus determine the order in which images should be processed. The Levenshtein Distance also provides matches between images, from which their underlying environment points can be recovered. Recovered points that are visible in multiple views permit the localization of more views which in turn allow the recovery of more points. The process repeats until all views have been localized. Periodic refinement of the reconstruction with the aid of bundle adjustment, distributes the reconstruction errors among images. The method is demonstrated on several unordered sets of panoramic images obtained in an indoor environment.
Cuiping Zhang - One of the best experts on this subject based on the ideXlab platform.
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Marker Codes Using the Decoding Based on Weighted Levenshtein Distance in the Presence of Insertions/Deletions
IEEE Access, 2020Co-Authors: Liu Yuan, He Yashuo, Xiaonan Zhao, Cuiping ZhangAbstract:A random marker code is inserted into the information sequences periodically, and a novel symbol-level decoding algorithm considering the weighted Levenshtein Distance (WLD) is designed for correcting insertions, deletions, as well as substitutions in the received sequences. In this method, branch quantities in the decoding trellis are calculated by measuring the WLD, which is done using the dynamic programming. A simulation study is performed to demonstrate the effectiveness of the presented scheme in the practical system, especially for channels with weak synchronization problems.
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marker codes using the decoding based on weighted Levenshtein Distance in the presence of insertions deletions
IEEE Access, 2020Co-Authors: Yuan Liu, Xiaonan Zhao, Cuiping ZhangAbstract:A random marker code is inserted into the information sequences periodically, and a novel symbol-level decoding algorithm considering the weighted Levenshtein Distance (WLD) is designed for correcting insertions, deletions, as well as substitutions in the received sequences. In this method, branch quantities in the decoding trellis are calculated by measuring the WLD, which is done using the dynamic programming. A simulation study is performed to demonstrate the effectiveness of the presented scheme in the practical system, especially for channels with weak synchronization problems.
Antonis A Argyros - One of the best experts on this subject based on the ideXlab platform.
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localizing unordered panoramic images using the Levenshtein Distance
International Conference on Computer Vision, 2007Co-Authors: Damien Michel, Antonis A Argyros, M L A LourakisAbstract:This paper proposes a feature-based method for recovering the relative positions of the viewpoints of a set of panoramic images for which no a priori order information is available, along with certain structure information regarding the imaged environment. The proposed approach operates incrementally, employing the Levenshtein Distance to deduce the spatial proximity of image viewpoints and thus determine the order in which images should be processed. The Levenshtein Distance also provides matches between images, from which their underlying environment points can be recovered. Recovered points that are visible in multiple views permit the localization of more views which in turn allow the recovery of more points. The process repeats until all views have been localized. Periodic refinement of the reconstruction with the aid of bundle adjustment, distributes the reconstruction errors among images. The method is demonstrated on several unordered sets of panoramic images obtained in an indoor environment.
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ICCV - Localizing Unordered Panoramic Images Using the Levenshtein Distance
2007 IEEE 11th International Conference on Computer Vision, 2007Co-Authors: Damien Michel, Antonis A Argyros, M L A LourakisAbstract:This paper proposes a feature-based method for recovering the relative positions of the viewpoints of a set of panoramic images for which no a priori order information is available, along with certain structure information regarding the imaged environment. The proposed approach operates incrementally, employing the Levenshtein Distance to deduce the spatial proximity of image viewpoints and thus determine the order in which images should be processed. The Levenshtein Distance also provides matches between images, from which their underlying environment points can be recovered. Recovered points that are visible in multiple views permit the localization of more views which in turn allow the recovery of more points. The process repeats until all views have been localized. Periodic refinement of the reconstruction with the aid of bundle adjustment, distributes the reconstruction errors among images. The method is demonstrated on several unordered sets of panoramic images obtained in an indoor environment.
Theo G. Swart - One of the best experts on this subject based on the ideXlab platform.
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Modelling Distances between genetically related languages using an extended weighted Levenshtein Distance
Southern African Linguistics and Applied Language Studies, 2009Co-Authors: Filip Paluncic, Hendrik C. Ferreira, Theo G. Swart, W.a. ClarkeAbstract:This article proposes the use of an extended weighted Levenshtein Distance to model the time depth between parent and direct descendant languages and also the dialectal separation between sibling languages. The parent language is usually a proto-language, a hypothetical reconstructed language, whose precise date is usually conjectural. Phonology is used as an indicator of language difference, which is modelled by means of an extended weighted Levenshtein Distance. This idea is applied specifically to the Iranian language family. Southern African Linguistics and Applied Language Studies 2009, 27(4): 381–389
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Bidirectional Viterbi Decoding using the Levenshtein Distance Metric for Deletion Channels
2006 IEEE Information Theory Workshop - ITW '06 Chengdu, 2006Co-Authors: Ling Cheng, Hendrik C. Ferreira, Theo G. SwartAbstract:In this paper, we present a bidirectional Viterbi decoding algorithm using the Levenshtein Distance metric for a regular convolutional encoding system. For a deletion channel, this decoding algorithm can correct an average of 30 deletion errors within a 6000 bit frame, when using an r = 0.67 regular convolutional code; and it can correct an average of 80 deletion errors within a 4000 bit frame, when using an r = 0.25 regular convolutional code.