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Person Re-Identification

Re-identification offers a useful tool for non-invasive biometric validation, surveillance, and human-robot interaction in a broad range of applications from crowd traffic management to personalised healthcare. This comprehensive volume is the first work of its kind dedicated to addressing the challenge of Person Re-Identification, presenting insights from an international selection of leading authorities in the field. Taking a strongly multidisciplinary approach, the text provides an in-depth discussion of recent developments and state-of-the-art methods drawn from the computer vision, pattern recognition and machine learning communities, embracing both fundamental research and practical applications. Topics and features: Introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms, and examines the benefits of semantic attributes. Describes how to segregate meaningful body parts from background clutter. Examines the use of 3D depth images, and contextual constraints derived from the visual appearance of a group. Reviews approaches to feature transfer function and distance metric learning, and discusses potential solutions to issues of data scalability and identity inference. Investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference, and describes techniques for improving post-rank search efficiency. Explores the design rationale and implementation considerations of building a practical re-identification system. This timely collection will be of great interest to academics, industrial researchers and postgraduates involved in computer vision and machine learning, database image retrieval, big data mining, and search engines, as well as to developers keen to exploit this emerging technology for commercial applications. --

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  • "Re-identification offers a useful tool for non-invasive biometric validation, surveillance, and human-robot interaction in a broad range of applications from crowd traffic management to personalised healthcare. This comprehensive volume is the first work of its kind dedicated to addressing the challenge of Person Re-Identification, presenting insights from an international selection of leading authorities in the field. Taking a strongly multidisciplinary approach, the text provides an in-depth discussion of recent developments and state-of-the-art methods drawn from the computer vision, pattern recognition and machine learning communities, embracing both fundamental research and practical applications. Topics and features: Introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms, and examines the benefits of semantic attributes. Describes how to segregate meaningful body parts from background clutter. Examines the use of 3D depth images, and contextual constraints derived from the visual appearance of a group. Reviews approaches to feature transfer function and distance metric learning, and discusses potential solutions to issues of data scalability and identity inference. Investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference, and describes techniques for improving post-rank search efficiency. Explores the design rationale and implementation considerations of building a practical re-identification system. This timely collection will be of great interest to academics, industrial researchers and postgraduates involved in computer vision and machine learning, database image retrieval, big data mining, and search engines, as well as to developers keen to exploit this emerging technology for commercial applications. --"@en
  • "Re-identification offers a useful tool for non-invasive biometric validation, surveillance, and human-robot interaction in a broad range of applications from crowd traffic management to personalised healthcare. This comprehensive volume is the first work of its kind dedicated to addressing the challenge of Person Re-Identification, presenting insights from an international selection of leading authorities in the field. Taking a strongly multidisciplinary approach, the text provides an in-depth discussion of recent developments and state-of-the-art methods drawn from the computer vision, pattern recognition and machine learning communities, embracing both fundamental research and practical applications. Topics and features: Introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms, and examines the benefits of semantic attributes Describes how to segregate meaningful body parts from background clutter Examines the use of 3D depth images, and contextual constraints derived from the visual appearance of a group Reviews approaches to feature transfer function and distance metric learning, and discusses potential solutions to issues of data scalability and identity inference Investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference, and describes techniques for improving post-rank search efficiency Explores the design rationale and implementation considerations of building a practical re-identification system This timely collection will be of great interest to academics, industrial researchers and postgraduates involved in computer vision and machine learning, database image retrieval, big data mining, and search engines, as well as to developers keen to exploit this emerging technology for commercial applications."
  • "Re-identification offers a useful tool for non-invasive biometric validation, surveillance, and human-robot interaction in a broad range of applications from crowd traffic management to personalised healthcare. This comprehensive volume is the first work of its kind dedicated to addressing the challenge of Person Re-Identification, presenting insights from an international selection of leading authorities in the field. Taking a strongly multidisciplinary approach, the text provides an in-depth discussion of recent developments and state-of-the-art methods drawn from the computer vision, pattern recognition and machine learning communities, embracing both fundamental research and practical applications. Topics and features: Introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms, and examines the benefits of semantic attributes Describes how to segregate meaningful body parts from background clutter Examines the use of 3D depth images, and contextual constraints derived from the visual appearance of a group Reviews approaches to feature transfer function and distance metric learning, and discusses potential solutions to issues of data scalability and identity inference Investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference, and describes techniques for improving post-rank search efficiency Explores the design rationale and implementation considerations of building a practical re-identification system This timely collection will be of great interest to academics, industrial researchers and postgraduates involved in computer vision and machine learning, database image retrieval, big data mining, and search engines, as well as to developers keen to exploit this emerging technology for commercial applications."@en
  • "This book provides discussion of recent developments and state-of-the-art methods of re-identification, drawn from the areas of computer vision, pattern recognition and machine learning, and embracing both fundamental research and practical applications. Topics include: examples of robust feature representations, salient feature weighting and selection mechanisms, and the benefits of semantic attributes; how to segregate meaningful body parts from background clutter; use of 3D depth images, and contextual constraints derived from the visual appearance of a group; feature transfer function and distance metric learning, and potential solutions to issues of data scalability and identity inference; limitations of existing benchmark datasets, strategies for camera topology inference, and techniques for improving post-rank search efficiency; and design rationale and implementation considerations of building a practical re-identification system. --"

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  • "Livres électroniques"
  • "Electronic books"@en
  • "Ressources Internet"

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  • "Person Re-Identification"@en
  • "Person Re-Identification"
  • "Person re-identification"
  • "Person re-identification"@en
  • "Person re-Identification"

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