Object categorization: computer and human vision perspectives
This edited volume presents a unique multidisciplinary perspective on the problem of visual object categorization. The result of a series of four highly successful workshops on the topic, the book gathers many of the most distinguished researchers from both computer and human vision to reflect on th...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2009
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| Online-Zugang: | BSB01 FHN01 URL des Erstveröffentlichers |
| Zusammenfassung: | This edited volume presents a unique multidisciplinary perspective on the problem of visual object categorization. The result of a series of four highly successful workshops on the topic, the book gathers many of the most distinguished researchers from both computer and human vision to reflect on their experience, identify open problems, and foster a cross-disciplinary discussion with the idea that parallel problems and solutions have arisen in both domains. Twenty-seven of these workshop speakers have contributed chapters, including fourteen from computer vision and thirteen from human vision. Their contributions range from broad perspectives on the problem to more specific approaches, collectively providing important historical context, identifying the major challenges, and presenting recent research results. This multidisciplinary collection is the first of its kind on the topic of object categorization, providing an outstanding context for graduate students and researchers in both computer and human vision |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xv, 536 pages) |
| ISBN: | 9780511635465 |
| DOI: | 10.1017/CBO9780511635465 |
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| 505 | 8 | |a Structural representation of object shape in the brain Charles Connor; 11. Learning hierarchical compositional representations of object structure Sanja Fidler, Marko Boben and Ales Leonardis; 12. Object categorization in man, monkey, and machine: some answers and some open questions Maximilian Riesenhuber; 13. Learning object category modeling, learning, and recognition by stochastic grammar Jake Porway, Benjamin Yao and Song Chun Zhu; 14. The neurophysiology and computational mechanisms of object representation Edmund Rolls; 15. Recognizing visual classes and individual objects by semantic hierarchies Shimon Ullman; 16. early stages of object categorization Pawan Sinha, Benjamin Balas, Yuri Ostrovsky and Jonas Wulff; 17. Towards integration of different paradigms in modeling, representation and learning of visual categories Mario Fritz and Bernt Schiele; 18. Acquisition and breakdown of category-specificity in the ventral visual stream K.- | |
| 505 | 8 | |a Suzanne Scherf, Marlene Behrmann and Kate Humphreys; 19. Using simple features and relations Marius Leordeanu, Martial Hebert and Rahul Sukthankar; 20. The proactive brain: using memory to anticipate what's next Kestutis Kveraga, Jasmine Boshyan and Moshe Bar; 21. Spatial pyramid matching Svetlana Lazebnik, Cordelia Schmid and Jean Ponce; 22. Perceptual decisions and visual learning in the human brain Zoe Kourtzi; 23. Shapes and shock graphs: from segmented shapes to shapes embedded in images Benjamin Kimia; 24. Correlated structures in natural scenes and their implications on neural learning of prior models for objects and surfaces Tai Sing Lee, Tom Stepleton, Brian Potetz and Jason Samonds; 25. Medial models for recognition Kaleem Siddiqi and Stephen Pizer; 26. Multimodal categorization C. Wallraven and Heinrich Bulthoff; 27. Comparing images of 3-D objects David W. Jacobs | |
| 520 | |a This edited volume presents a unique multidisciplinary perspective on the problem of visual object categorization. The result of a series of four highly successful workshops on the topic, the book gathers many of the most distinguished researchers from both computer and human vision to reflect on their experience, identify open problems, and foster a cross-disciplinary discussion with the idea that parallel problems and solutions have arisen in both domains. Twenty-seven of these workshop speakers have contributed chapters, including fourteen from computer vision and thirteen from human vision. Their contributions range from broad perspectives on the problem to more specific approaches, collectively providing important historical context, identifying the major challenges, and presenting recent research results. This multidisciplinary collection is the first of its kind on the topic of object categorization, providing an outstanding context for graduate students and researchers in both computer and human vision | ||
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Datensatz im Suchindex
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| author2 | Dickinson, Sven J. |
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| contents | Structural representation of object shape in the brain Charles Connor; 11. Learning hierarchical compositional representations of object structure Sanja Fidler, Marko Boben and Ales Leonardis; 12. Object categorization in man, monkey, and machine: some answers and some open questions Maximilian Riesenhuber; 13. Learning object category modeling, learning, and recognition by stochastic grammar Jake Porway, Benjamin Yao and Song Chun Zhu; 14. The neurophysiology and computational mechanisms of object representation Edmund Rolls; 15. Recognizing visual classes and individual objects by semantic hierarchies Shimon Ullman; 16. early stages of object categorization Pawan Sinha, Benjamin Balas, Yuri Ostrovsky and Jonas Wulff; 17. Towards integration of different paradigms in modeling, representation and learning of visual categories Mario Fritz and Bernt Schiele; 18. Acquisition and breakdown of category-specificity in the ventral visual stream K.- Suzanne Scherf, Marlene Behrmann and Kate Humphreys; 19. Using simple features and relations Marius Leordeanu, Martial Hebert and Rahul Sukthankar; 20. The proactive brain: using memory to anticipate what's next Kestutis Kveraga, Jasmine Boshyan and Moshe Bar; 21. Spatial pyramid matching Svetlana Lazebnik, Cordelia Schmid and Jean Ponce; 22. Perceptual decisions and visual learning in the human brain Zoe Kourtzi; 23. Shapes and shock graphs: from segmented shapes to shapes embedded in images Benjamin Kimia; 24. Correlated structures in natural scenes and their implications on neural learning of prior models for objects and surfaces Tai Sing Lee, Tom Stepleton, Brian Potetz and Jason Samonds; 25. Medial models for recognition Kaleem Siddiqi and Stephen Pizer; 26. Multimodal categorization C. Wallraven and Heinrich Bulthoff; 27. Comparing images of 3-D objects David W. Jacobs |
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| dewey-full | 006.3/7 |
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| discipline | Informatik |
| doi_str_mv | 10.1017/CBO9780511635465 |
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| spelling | Object categorization computer and human vision perspectives edited by Sven J. Dickinson [and others] Cambridge Cambridge University Press 2009 1 online resource (xv, 536 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) Structural representation of object shape in the brain Charles Connor; 11. Learning hierarchical compositional representations of object structure Sanja Fidler, Marko Boben and Ales Leonardis; 12. Object categorization in man, monkey, and machine: some answers and some open questions Maximilian Riesenhuber; 13. Learning object category modeling, learning, and recognition by stochastic grammar Jake Porway, Benjamin Yao and Song Chun Zhu; 14. The neurophysiology and computational mechanisms of object representation Edmund Rolls; 15. Recognizing visual classes and individual objects by semantic hierarchies Shimon Ullman; 16. early stages of object categorization Pawan Sinha, Benjamin Balas, Yuri Ostrovsky and Jonas Wulff; 17. Towards integration of different paradigms in modeling, representation and learning of visual categories Mario Fritz and Bernt Schiele; 18. Acquisition and breakdown of category-specificity in the ventral visual stream K.- Suzanne Scherf, Marlene Behrmann and Kate Humphreys; 19. Using simple features and relations Marius Leordeanu, Martial Hebert and Rahul Sukthankar; 20. The proactive brain: using memory to anticipate what's next Kestutis Kveraga, Jasmine Boshyan and Moshe Bar; 21. Spatial pyramid matching Svetlana Lazebnik, Cordelia Schmid and Jean Ponce; 22. Perceptual decisions and visual learning in the human brain Zoe Kourtzi; 23. Shapes and shock graphs: from segmented shapes to shapes embedded in images Benjamin Kimia; 24. Correlated structures in natural scenes and their implications on neural learning of prior models for objects and surfaces Tai Sing Lee, Tom Stepleton, Brian Potetz and Jason Samonds; 25. Medial models for recognition Kaleem Siddiqi and Stephen Pizer; 26. Multimodal categorization C. Wallraven and Heinrich Bulthoff; 27. Comparing images of 3-D objects David W. Jacobs This edited volume presents a unique multidisciplinary perspective on the problem of visual object categorization. The result of a series of four highly successful workshops on the topic, the book gathers many of the most distinguished researchers from both computer and human vision to reflect on their experience, identify open problems, and foster a cross-disciplinary discussion with the idea that parallel problems and solutions have arisen in both domains. Twenty-seven of these workshop speakers have contributed chapters, including fourteen from computer vision and thirteen from human vision. Their contributions range from broad perspectives on the problem to more specific approaches, collectively providing important historical context, identifying the major challenges, and presenting recent research results. This multidisciplinary collection is the first of its kind on the topic of object categorization, providing an outstanding context for graduate students and researchers in both computer and human vision Computer vision Pattern recognition systems Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf 1\p (DE-588)1071861417 Konferenzschrift gnd-content Mustererkennung (DE-588)4040936-3 s 2\p DE-604 Maschinelles Sehen (DE-588)4129594-8 s 3\p DE-604 Dickinson, Sven J. edt Erscheint auch als Druckausgabe 978-0-521-88738-0 https://doi.org/10.1017/CBO9780511635465 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Object categorization computer and human vision perspectives Structural representation of object shape in the brain Charles Connor; 11. Learning hierarchical compositional representations of object structure Sanja Fidler, Marko Boben and Ales Leonardis; 12. Object categorization in man, monkey, and machine: some answers and some open questions Maximilian Riesenhuber; 13. Learning object category modeling, learning, and recognition by stochastic grammar Jake Porway, Benjamin Yao and Song Chun Zhu; 14. The neurophysiology and computational mechanisms of object representation Edmund Rolls; 15. Recognizing visual classes and individual objects by semantic hierarchies Shimon Ullman; 16. early stages of object categorization Pawan Sinha, Benjamin Balas, Yuri Ostrovsky and Jonas Wulff; 17. Towards integration of different paradigms in modeling, representation and learning of visual categories Mario Fritz and Bernt Schiele; 18. Acquisition and breakdown of category-specificity in the ventral visual stream K.- Suzanne Scherf, Marlene Behrmann and Kate Humphreys; 19. Using simple features and relations Marius Leordeanu, Martial Hebert and Rahul Sukthankar; 20. The proactive brain: using memory to anticipate what's next Kestutis Kveraga, Jasmine Boshyan and Moshe Bar; 21. Spatial pyramid matching Svetlana Lazebnik, Cordelia Schmid and Jean Ponce; 22. Perceptual decisions and visual learning in the human brain Zoe Kourtzi; 23. Shapes and shock graphs: from segmented shapes to shapes embedded in images Benjamin Kimia; 24. Correlated structures in natural scenes and their implications on neural learning of prior models for objects and surfaces Tai Sing Lee, Tom Stepleton, Brian Potetz and Jason Samonds; 25. Medial models for recognition Kaleem Siddiqi and Stephen Pizer; 26. Multimodal categorization C. Wallraven and Heinrich Bulthoff; 27. Comparing images of 3-D objects David W. Jacobs Computer vision Pattern recognition systems Maschinelles Sehen (DE-588)4129594-8 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4129594-8 (DE-588)4040936-3 (DE-588)1071861417 |
| title | Object categorization computer and human vision perspectives |
| title_auth | Object categorization computer and human vision perspectives |
| title_exact_search | Object categorization computer and human vision perspectives |
| title_full | Object categorization computer and human vision perspectives edited by Sven J. Dickinson [and others] |
| title_fullStr | Object categorization computer and human vision perspectives edited by Sven J. Dickinson [and others] |
| title_full_unstemmed | Object categorization computer and human vision perspectives edited by Sven J. Dickinson [and others] |
| title_short | Object categorization |
| title_sort | object categorization computer and human vision perspectives |
| title_sub | computer and human vision perspectives |
| topic | Computer vision Pattern recognition systems Maschinelles Sehen (DE-588)4129594-8 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Computer vision Pattern recognition systems Maschinelles Sehen Mustererkennung Konferenzschrift |
| url | https://doi.org/10.1017/CBO9780511635465 |
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