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inria-00548614, version 1

Towards category-level object recognition

Jean Ponce () ¹, Martial Hebert ², Cordelia Schmid ( Author to contact preferably ) ³, Andrew Zisserman ()^a^, 4

Towards category-level object recognition (2006) 618 p.

Abstract: This book is the outcome of two workshops that brought together about 40 prominent vision and machine learning researchers interested in the fundamental and applicative aspects of object recognition, as well as representatives of industry. The main goals of these two workshops were (1) to promote the creation of an international object recognition community, with common datasets and evaluation procedures, (2) to map the state of the art and identify the main open problems and opportunities for synergistic research, and (3) to articulate the industrial and societal needs and opportunities for object recognition research worldwide. These goals are reflected in a relatively small number of papers that illustrate the breadth of today's object recognition research and the arsenal of techniques at its disposal, and discuss current achievements and outstanding challenges. Most of the chapters are descriptions of technical approaches, intended to capture the current state of the art. Some of the chapters are of a tutorial nature. They cover fundamental building blocks for object recognition techniques.

a – University of Oxford
1: Ecole Normale Supérieure de Paris (ENS)
Ecole Normale Supérieure de Paris - ENS Paris
2: The Robotics Institute
Carnegie Mellon University
3: LEAR (IMAG-INRIA Rhône-Alpes / GRAVIR)
CNRS : FR71 – CNRS : UMR5527 – INRIA – Université Joseph Fourier - Grenoble I – Institut National Polytechnique de Grenoble (INPG)
4: Visual Geometry Group (VGG)
University of Oxford

inria-00548614, version 1
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Submitted on: Monday, 20 December 2010 10:07:19
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DOI: 10.1007/11957959

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%% inria-00548614, version 1
%% http://hal.inria.fr/inria-00548614
@book{ponce:inria-00548614,
    hal_id = {inria-00548614},
    url = {http://hal.inria.fr/inria-00548614},
    title = {{Towards category-level object recognition}},
    author = {Ponce, Jean and Hebert, Martial and Schmid, Cordelia and Zisserman, Andrew},
    abstract = {{This book is the outcome of two workshops that brought together about 40 prominent vision and machine learning researchers interested in the fundamental and applicative aspects of object recognition, as well as representatives of industry. The main goals of these two workshops were (1) to promote the creation of an international object recognition community, with common datasets and evaluation procedures, (2) to map the state of the art and identify the main open problems and opportunities for synergistic research, and (3) to articulate the industrial and societal needs and opportunities for object recognition research worldwide. These goals are reflected in a relatively small number of papers that illustrate the breadth of today's object recognition research and the arsenal of techniques at its disposal, and discuss current achievements and outstanding challenges. Most of the chapters are descriptions of technical approaches, intended to capture the current state of the art. Some of the chapters are of a tutorial nature. They cover fundamental building blocks for object recognition techniques.}},
    keywords = {3D objects; classification; cognitive computer vision; cognitive systems; computational attention; computer vision; computer vision systems; face detection; feature extraction; image processing; machine learning; object detection; object recognition; object segmentation; object tracking; sequential learning; statistical learning; statistical models; video sequence analysis; vision systems architecture; vision systems design},
    language = {English},
    affiliation = {Ecole Normale Sup{\'e}rieure de Paris - ENS , The Robotics Institute , LEAR - IMAG-INRIA Rh{\^o}ne-Alpes / GRAVIR , Visual Geometry Group - VGG},
    publisher = {Springer},
    pages = {618 p.},
    volume = {4170},
    series = {Lecture Notes in Computer Science (LNCS) },
    isbn = {978-3-540-68794-8 },
    doi = {10.1007/11957959 },
    year = {2006},
}

%F inria-00548614, version 1
%0 Book
%U http://hal.inria.fr/inria-00548614
%2 INFO:INFO_CV
%T Towards category-level object recognition
%A Ponce, Jean
%A Hebert, Martial
%A Schmid, Cordelia
%A Zisserman, Andrew
%+ Ecole Normale Supérieure de Paris - ENS , The Robotics Institute , LEAR - IMAG-INRIA Rhône-Alpes / GRAVIR , Visual Geometry Group - VGG
%X This book is the outcome of two workshops that brought together about 40 prominent vision and machine learning researchers interested in the fundamental and applicative aspects of object recognition, as well as representatives of industry. The main goals of these two workshops were (1) to promote the creation of an international object recognition community, with common datasets and evaluation procedures, (2) to map the state of the art and identify the main open problems and opportunities for synergistic research, and (3) to articulate the industrial and societal needs and opportunities for object recognition research worldwide. These goals are reflected in a relatively small number of papers that illustrate the breadth of today's object recognition research and the arsenal of techniques at its disposal, and discuss current achievements and outstanding challenges. Most of the chapters are descriptions of technical approaches, intended to capture the current state of the art. Some of the chapters are of a tutorial nature. They cover fundamental building blocks for object recognition techniques.
%G English
%8 2006-00-00
%2 
%I Springer
%V 4170
%N Lecture Notes in Computer Science (LNCS)
%P 618 p.
%R 10.1007/11957959
%2 978-3-540-68794-8
%2 http://www.springer.com/computer/image+processing/book/978-3-540-68794-8
%K 3D objects; classification; cognitive computer vision; cognitive systems; computational attention; computer vision; computer vision systems; face detection; feature extraction; image processing; machine learning; object detection; object recognition; object segmentation; object tracking; sequential learning; statistical learning; statistical models; video sequence analysis; vision systems architecture; vision systems design
%2 2006

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Towards category-level object recognition

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