Towards category-level object recognition

Jean Ponce 1 Martial Hebert 2 Cordelia Schmid 3, * Andrew Zisserman 4
* Auteur correspondant
3 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
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.
Type de document :
Ouvrage (y compris édition critique et traduction)
Springer, 4170, 618 p., 2006, Lecture Notes in Computer Science (LNCS), 978-3-540-68794-8. 〈10.1007/11957959〉
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https://hal.inria.fr/inria-00548614
Contributeur : Thoth Team <>
Soumis le : lundi 20 décembre 2010 - 10:07:19
Dernière modification le : mardi 24 avril 2018 - 17:20:13

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Jean Ponce, Martial Hebert, Cordelia Schmid, Andrew Zisserman. Towards category-level object recognition. Springer, 4170, 618 p., 2006, Lecture Notes in Computer Science (LNCS), 978-3-540-68794-8. 〈10.1007/11957959〉. 〈inria-00548614〉

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