Data Segmentation and Model Selection for Computer Vision: A Statistical Approach
The primary focus of this book is on techniques for segmentation of visual data. By "visual data," we mean data derived from a single image or from a sequence of images. By "segmentation" we mean breaking the visual data into meaningful parts or segments. However, in general, we...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
2000
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| Ausgabe: | 1st ed. 2000 |
| Schlagworte: | |
| Online-Zugang: | UBY01 Volltext |
| Zusammenfassung: | The primary focus of this book is on techniques for segmentation of visual data. By "visual data," we mean data derived from a single image or from a sequence of images. By "segmentation" we mean breaking the visual data into meaningful parts or segments. However, in general, we do not mean "any old data": but data fundamental to the operation of robotic devices such as the range to and motion of objects in a scene. Having said that, much of what is covered in this book is far more general: The above merely describes our driving interests. The central emphasis of this book is that segmentation involves model fitting. We believe this to be true either implicitly (as a conscious or sub conscious guiding principle of those who develop various approaches) or explicitly. What makes model-fitting in computer vision especially hard? There are a number of factors involved in answering this question. The amount of data involved is very large. The number of segments and types (models) are not known in advance (and can sometimes rapidly change over time). The sensors we have involve the introduction of noise. Usually, we require fast ("real-time" or near real-time) computation of solutions independent of any human intervention/supervision. Chapter 1 summarizes many of the attempts of computer vision researchers to solve the problem of segmenta tion in these difficult circumstances |
| Beschreibung: | 1 Online-Ressource (XX, 208 p) |
| ISBN: | 9780387215280 |
| DOI: | 10.1007/978-0-387-21528-0 |
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| 520 | |a The primary focus of this book is on techniques for segmentation of visual data. By "visual data," we mean data derived from a single image or from a sequence of images. By "segmentation" we mean breaking the visual data into meaningful parts or segments. However, in general, we do not mean "any old data": but data fundamental to the operation of robotic devices such as the range to and motion of objects in a scene. Having said that, much of what is covered in this book is far more general: The above merely describes our driving interests. The central emphasis of this book is that segmentation involves model fitting. We believe this to be true either implicitly (as a conscious or sub conscious guiding principle of those who develop various approaches) or explicitly. What makes model-fitting in computer vision especially hard? There are a number of factors involved in answering this question. The amount of data involved is very large. The number of segments and types (models) are not known in advance (and can sometimes rapidly change over time). The sensors we have involve the introduction of noise. Usually, we require fast ("real-time" or near real-time) computation of solutions independent of any human intervention/supervision. Chapter 1 summarizes many of the attempts of computer vision researchers to solve the problem of segmenta tion in these difficult circumstances | ||
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| edition | 1st ed. 2000 |
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| spelling | Data Segmentation and Model Selection for Computer Vision A Statistical Approach edited by Alireza Bab-Hadiashar, David Suter 1st ed. 2000 New York, NY Springer New York 2000 1 Online-Ressource (XX, 208 p) txt rdacontent c rdamedia cr rdacarrier The primary focus of this book is on techniques for segmentation of visual data. By "visual data," we mean data derived from a single image or from a sequence of images. By "segmentation" we mean breaking the visual data into meaningful parts or segments. However, in general, we do not mean "any old data": but data fundamental to the operation of robotic devices such as the range to and motion of objects in a scene. Having said that, much of what is covered in this book is far more general: The above merely describes our driving interests. The central emphasis of this book is that segmentation involves model fitting. We believe this to be true either implicitly (as a conscious or sub conscious guiding principle of those who develop various approaches) or explicitly. What makes model-fitting in computer vision especially hard? There are a number of factors involved in answering this question. The amount of data involved is very large. The number of segments and types (models) are not known in advance (and can sometimes rapidly change over time). The sensors we have involve the introduction of noise. Usually, we require fast ("real-time" or near real-time) computation of solutions independent of any human intervention/supervision. Chapter 1 summarizes many of the attempts of computer vision researchers to solve the problem of segmenta tion in these difficult circumstances Pattern Recognition Pattern recognition Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Robuste Statistik (DE-588)4451047-0 gnd rswk-swf Bildsegmentierung (DE-588)4145448-0 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 s Bildsegmentierung (DE-588)4145448-0 s Robuste Statistik (DE-588)4451047-0 s DE-604 Bab-Hadiashar, Alireza edt Suter, David edt Erscheint auch als Druck-Ausgabe 9781468495089 Erscheint auch als Druck-Ausgabe 9780387988153 Erscheint auch als Druck-Ausgabe 9781468495072 https://doi.org/10.1007/978-0-387-21528-0 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | Data Segmentation and Model Selection for Computer Vision A Statistical Approach Pattern Recognition Pattern recognition Maschinelles Sehen (DE-588)4129594-8 gnd Robuste Statistik (DE-588)4451047-0 gnd Bildsegmentierung (DE-588)4145448-0 gnd |
| subject_GND | (DE-588)4129594-8 (DE-588)4451047-0 (DE-588)4145448-0 |
| title | Data Segmentation and Model Selection for Computer Vision A Statistical Approach |
| title_auth | Data Segmentation and Model Selection for Computer Vision A Statistical Approach |
| title_exact_search | Data Segmentation and Model Selection for Computer Vision A Statistical Approach |
| title_exact_search_txtP | Data Segmentation and Model Selection for Computer Vision A Statistical Approach |
| title_full | Data Segmentation and Model Selection for Computer Vision A Statistical Approach edited by Alireza Bab-Hadiashar, David Suter |
| title_fullStr | Data Segmentation and Model Selection for Computer Vision A Statistical Approach edited by Alireza Bab-Hadiashar, David Suter |
| title_full_unstemmed | Data Segmentation and Model Selection for Computer Vision A Statistical Approach edited by Alireza Bab-Hadiashar, David Suter |
| title_short | Data Segmentation and Model Selection for Computer Vision |
| title_sort | data segmentation and model selection for computer vision a statistical approach |
| title_sub | A Statistical Approach |
| topic | Pattern Recognition Pattern recognition Maschinelles Sehen (DE-588)4129594-8 gnd Robuste Statistik (DE-588)4451047-0 gnd Bildsegmentierung (DE-588)4145448-0 gnd |
| topic_facet | Pattern Recognition Pattern recognition Maschinelles Sehen Robuste Statistik Bildsegmentierung |
| url | https://doi.org/10.1007/978-0-387-21528-0 |
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