A Model Based Framework for Object Detection via Data Transformation - Couverture souple

Guo, Peifang; Bhattacharya, Prabir

 
9783659494574: A Model Based Framework for Object Detection via Data Transformation
Couverture souple
ISBN 10 : 3659494577 ISBN 13 : 9783659494574
Editeur : LAP LAMBERT Academic Publishing, 2013

Synopsis

The dynamic synthesis of nonlinear feature functions is a challenging problem in object detection. This book presents a combinatorial approach of genetic programming and the expectation maximization algorithm (GP-EM) to synthesize nonlinear feature functions automatically for the purpose of object detection. The EM algorithm investigates the use of Gaussian mixture which is able to model the behaviour of the training samples during an optimal GP search strategy. Based on the Gaussian probability assumption, the GP-EM method is capable of performing simultaneously dynamic feature synthesis and model-based generalization. The EM part of the approach leads to the application of the maximum likelihood (ML) operation which provides protection against inter-cluster data separation and thus exhibits improved convergence. The experimental results show that the approach improves the detection accuracy and efficiency of pattern object discovery, as compared to some state-of-the-art methods for object detection existing in the literature.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

The dynamic synthesis of nonlinear feature functions is a challenging problem in object detection. This book presents a combinatorial approach of genetic programming and the expectation maximization algorithm (GP-EM) to synthesize nonlinear feature functions automatically for the purpose of object detection. The EM algorithm investigates the use of Gaussian mixture which is able to model the behaviour of the training samples during an optimal GP search strategy. Based on the Gaussian probability assumption, the GP-EM method is capable of performing simultaneously dynamic feature synthesis and model-based generalization. The EM part of the approach leads to the application of the maximum likelihood (ML) operation which provides protection against inter-cluster data separation and thus exhibits improved convergence. The experimental results show that the approach improves the detection accuracy and efficiency of pattern object discovery, as compared to some state-of-the-art methods for object detection existing in the literature.

Biographie de l'auteur

Peifang Guo:Postdoctoral researcher at University of Cincinnati in USA; Prabir Bhattacharya: Professor at University of Cincinnati in USA.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2013
Langue
anglais
ISBN 10 
3659494577
ISBN 13 
9783659494574
Reliure
Broché
Nombre de pages
64
Coordonnées du fabricant
non disponible
Personne responsable
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