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Data Segmentation and Model Selection for Computer Vision: A Statistical Approach - Hardcover

 
9780387988153: Data Segmentation and Model Selection for Computer Vision: A Statistical Approach
Hardcover
ISBN 10: 0387988157 ISBN 13: 9780387988153
Verlag: Springer, 2000

Inhaltsangabe

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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Über die Autorin bzw. den Autor

Tat-Jun Chin was born and raised in Nibong Tebal, Penang. He received his B.Eng. in Electrical Engineering (Mechatronics) from Universiti Teknologi Malaysia (UTM) in 2003, and his Ph.D.in Computer Systems Engineering from Monash University in 2007 with a thesis titled ""Kernel Subspace Methods in Computer Vision."" His undergraduate studies were partly supported by an Agilent Technologies Scholarship, and his Ph.D. studies were mainly supported by an Endeavour Australia-Asia Award. He was a Research Fellow at the Institute for Infocomm Research (I2R) in Singapore from 2007-2008. Since 2008 he has been a Senior Research Associate (2008-2010), Lecturer (2010-2013), and Senior Lecturer (2014-2016) at The University of Adelaide. Since 2017 he was an Associate Professor at the same university. His research interests include robust estimation and geometric optimization. He won a CVPR award and DSTO award (both in 2015) for his research work.David Suter was born in Leeds, UK. He holds the following degrees: BSc (Applied Maths and Physics, The Flinders University of SA, 1977); Grad. Dip. Ed. (Secondary Teaching, The Flinders University of SA, 1978); Grad. Dip. Comp. (Royal Melbourne Institute of Technology, 1984); Ph.D. (Computer Vision, La Trobe University, 1991). He has held the following appointments: Lecturer at La Trobe University 1988-1992); Senior Lecturer, Associate Professor and Professor at Monash University (1992-2008), Professor at The University of Adelaide (2008-). He has served on the Australian Research Council College of Experts (2008-10) and the editorial boards of several journals: International Journal of Computer Vision (2004-2013), Journal of Mathematical Imaging and Vision (2008-2010), Machine Vision and Applications (2006-2008). He was general co-chair of two major conferences: ACCV2002 and ICIP2013 (both hosted in Melbourne Australia).

Von der hinteren Coverseite

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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Verlag
Springer
Erscheinungsdatum
2000
Sprache
Englisch
ISBN 10 
0387988157
ISBN 13 
9780387988153
Einband
Gebundene Ausgabe
Anzahl der Seiten
228
Herausgeber
Bab-Hadiashar Alireza, Suter David
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