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Evaluation of 2D Local Image Descriptors and Feature Encoding Methods: For Depth Image Based Object Class Recognition - Softcover
Synopsis
In this book, we have investigated the 3D object class recognition problem. We used an approach that solves this problem with the use of depth images obtained from 3D object models. In the approach we used, 3D object class recognition system is composed of two stages; training and testing. In both stages, first, keypoints are detected from the images, and then 2D local image descriptors are built around these keypoints. This is continued by encoding local descriptors into a single descriptor. Just before this step, in training stage, a codebook is learned, and it is used for encoding local descriptors in both stages. Another extra step in training stage is, after the descriptors are encoded, for each class a binary classifier is trained. Then, these classifiers are used in testing stage. We have evaluated different keypoint detection methods, 2D local image descriptors and encoding methods. Then, we experimentally show their superiorities and weaknesses over each other. Experiments clearly show the best performing keypoint detection method, local image description method and feature encoding method in the depth image domain. Different experimental setups yields similar results.
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About the Author
Güney Kayım is a researcher with experience in both industry and academic world. He is interested in any kind of technological innovation, though, he is specialized in image processing, computer vision and related topics. He took his MSc degree from Bogazici University/Turkey and he is currently employed as a senior researcher at Apical Limited/UK.
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Evaluation of 2D Local Image Descriptors and Feature Encoding Methods
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LAP LAMBERT Academic Publishing Jan 2016, 2016
ISBN 10: 3659763209
ISBN 13: 9783659763205
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this book, we have investigated the 3D object class recognition problem. We used an approach that solves this problem with the use of depth images obtained from 3D object models. In the approach we used, 3D object class recognition system is composed of two stages; training and testing. In both stages, first, keypoints are detected from the images, and then 2D local image descriptors are built around these keypoints. This is continued by encoding local descriptors into a single descriptor. Just before this step, in training stage, a codebook is learned, and it is used for encoding local descriptors in both stages. Another extra step in training stage is, after the descriptors are encoded, for each class a binary classifier is trained. Then, these classifiers are used in testing stage. We have evaluated different keypoint detection methods, 2D local image descriptors and encoding methods. Then, we experimentally show their superiorities and weaknesses over each other. Experiments clearly show the best performing keypoint detection method, local image description method and feature encoding method in the depth image domain. Different experimental setups yields similar results. 88 pp. Englisch. Seller Inventory # 9783659763205
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Evaluation of 2D Local Image Descriptors and Feature Encoding Methods
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LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659763209
ISBN 13: 9783659763205
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kayim GueneyGueney Kayim is a researcher with experience in both industry and academic world. He is interested in any kind of technological innovation, though, he is specialized in image processing, computer vision and related topics. . Seller Inventory # 159144770
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Evaluation of 2D Local Image Descriptors and Feature Encoding Methods
Published by
LAP LAMBERT Academic Publishing Jan 2016, 2016
ISBN 10: 3659763209
ISBN 13: 9783659763205
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Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this book, we have investigated the 3D object class recognition problem. We used an approach that solves this problem with the use of depth images obtained from 3D object models. In the approach we used, 3D object class recognition system is composed of two stages; training and testing. In both stages, first, keypoints are detected from the images, and then 2D local image descriptors are built around these keypoints. This is continued by encoding local descriptors into a single descriptor. Just before this step, in training stage, a codebook is learned, and it is used for encoding local descriptors in both stages. Another extra step in training stage is, after the descriptors are encoded, for each class a binary classifier is trained. Then, these classifiers are used in testing stage. We have evaluated different keypoint detection methods, 2D local image descriptors and encoding methods. Then, we experimentally show their superiorities and weaknesses over each other. Experiments clearly show the best performing keypoint detection method, local image description method and feature encoding method in the depth image domain. Different experimental setups yields similar results.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 88 pp. Englisch. Seller Inventory # 9783659763205
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Evaluation of 2D Local Image Descriptors and Feature Encoding Methods : For Depth Image Based Object Class Recognition
Published by
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659763209
ISBN 13: 9783659763205
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Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this book, we have investigated the 3D object class recognition problem. We used an approach that solves this problem with the use of depth images obtained from 3D object models. In the approach we used, 3D object class recognition system is composed of two stages; training and testing. In both stages, first, keypoints are detected from the images, and then 2D local image descriptors are built around these keypoints. This is continued by encoding local descriptors into a single descriptor. Just before this step, in training stage, a codebook is learned, and it is used for encoding local descriptors in both stages. Another extra step in training stage is, after the descriptors are encoded, for each class a binary classifier is trained. Then, these classifiers are used in testing stage. We have evaluated different keypoint detection methods, 2D local image descriptors and encoding methods. Then, we experimentally show their superiorities and weaknesses over each other. Experiments clearly show the best performing keypoint detection method, local image description method and feature encoding method in the depth image domain. Different experimental setups yields similar results. Seller Inventory # 9783659763205
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Evaluation of 2D Local Image Descriptors and Feature Encoding Methods | For Depth Image Based Object Class Recognition
Published by
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659763209
ISBN 13: 9783659763205
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Seller: preigu, Osnabrück, Germany
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Taschenbuch. Condition: Neu. Evaluation of 2D Local Image Descriptors and Feature Encoding Methods | For Depth Image Based Object Class Recognition | Güney Kay¿m (u. a.) | Taschenbuch | 88 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659763205 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 103995721