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This work presents a comparison of multiple approaches to visual terrain classification for outdoor mobile robots based on local features. For this purpose, we put a camera on a mobile robot and use it to capture images which are then analyzed to recognize the terrains present in these images. There are two sets of approaches that we use to classify terrains. The first is based on greyscale images and the second one is based on color images. For greyscale images, we use two different robot platforms for two different scenarios. The first robot platform is a wheeled outdoor robot. The second platform is a flying robot. For terrain classification, we modify and test three approaches called SURF, Daisy and Contrast Context Histogram, which are traditionally not used for texture classification. We compare these with more traditional texture classification approaches, such as Local Binary Patterns (LBP), Local Ternary Patterns (LTP) and a newer extension Local Adaptive Ternary Patterns (LATP). The image is divided into a grid and local features are calculated on the cells of this grid. These features are then used to train a classifier that can differentiate between different terrains.
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Visual Terrain Classification using Ground and Flying Robots
Editore:
LAP LAMBERT Academic Publishing Jun 2016, 2016
ISBN 10: 3659901466
ISBN 13: 9783659901461
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This work presents a comparison of multiple approaches to visual terrain classification for outdoor mobile robots based on local features. For this purpose, we put a camera on a mobile robot and use it to capture images which are then analyzed to recognize the terrains present in these images. There are two sets of approaches that we use to classify terrains. The first is based on greyscale images and the second one is based on color images. For greyscale images, we use two different robot platforms for two different scenarios. The first robot platform is a wheeled outdoor robot. The second platform is a flying robot. For terrain classification, we modify and test three approaches called SURF, Daisy and Contrast Context Histogram, which are traditionally not used for texture classification. We compare these with more traditional texture classification approaches, such as Local Binary Patterns (LBP), Local Ternary Patterns (LTP) and a newer extension Local Adaptive Ternary Patterns (LATP). The image is divided into a grid and local features are calculated on the cells of this grid. These features are then used to train a classifier that can differentiate between different terrains. 132 pp. Englisch. Codice articolo 9783659901461
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Visual Terrain Classification using Ground and Flying Robots
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LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659901466
ISBN 13: 9783659901461
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Condizione: New. Codice articolo 26395103878
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Visual Terrain Classification using Ground and Flying Robots
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LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659901466
ISBN 13: 9783659901461
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Khan Yasir NiazYasir Niaz Khan is a researcher and teacher in Robotics and Artificial Intelligence in Lahore Pakistan. He completed his PhD from University of Tuebingen Germany and previous degrees from FAST-NU Lahore Pakistan. He mo. Codice articolo 158963495
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Visual Terrain Classification using Ground and Flying Robots
Editore:
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659901466
ISBN 13: 9783659901461
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This work presents a comparison of multiple approaches to visual terrain classification for outdoor mobile robots based on local features. For this purpose, we put a camera on a mobile robot and use it to capture images which are then analyzed to recognize the terrains present in these images. There are two sets of approaches that we use to classify terrains. The first is based on greyscale images and the second one is based on color images. For greyscale images, we use two different robot platforms for two different scenarios. The first robot platform is a wheeled outdoor robot. The second platform is a flying robot. For terrain classification, we modify and test three approaches called SURF, Daisy and Contrast Context Histogram, which are traditionally not used for texture classification. We compare these with more traditional texture classification approaches, such as Local Binary Patterns (LBP), Local Ternary Patterns (LTP) and a newer extension Local Adaptive Ternary Patterns (LATP). The image is divided into a grid and local features are calculated on the cells of this grid. These features are then used to train a classifier that can differentiate between different terrains. Codice articolo 9783659901461
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Visual Terrain Classification using Ground and Flying Robots
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LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659901466
ISBN 13: 9783659901461
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Condizione: New. Print on Demand. Codice articolo 402354521
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Visual Terrain Classification using Ground and Flying Robots
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LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659901466
ISBN 13: 9783659901461
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Condizione: New. PRINT ON DEMAND. Codice articolo 18395103884
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Visual Terrain Classification using Ground and Flying Robots
Editore:
LAP LAMBERT Academic Publishing Jun 2016, 2016
ISBN 10: 3659901466
ISBN 13: 9783659901461
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This work presents a comparison of multiple approaches to visual terrain classification for outdoor mobile robots based on local features. For this purpose, we put a camera on a mobile robot and use it to capture images which are then analyzed to recognize the terrains present in these images. There are two sets of approaches that we use to classify terrains. The first is based on greyscale images and the second one is based on color images. For greyscale images, we use two different robot platforms for two different scenarios. The first robot platform is a wheeled outdoor robot. The second platform is a flying robot. For terrain classification, we modify and test three approaches called SURF, Daisy and Contrast Context Histogram, which are traditionally not used for texture classification. We compare these with more traditional texture classification approaches, such as Local Binary Patterns (LBP), Local Ternary Patterns (LTP) and a newer extension Local Adaptive Ternary Patterns (LATP). The image is divided into a grid and local features are calculated on the cells of this grid. These features are then used to train a classifier that can differentiate between different terrains.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch. Codice articolo 9783659901461
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Visual Terrain Classification using Ground and Flying Robots
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LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659901466
ISBN 13: 9783659901461
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Da: preigu, Osnabrück, Germania
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Taschenbuch. Condizione: Neu. Visual Terrain Classification using Ground and Flying Robots | Yasir Niaz Khan | Taschenbuch | 132 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659901461 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 103601068
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