A Model Based Framework for Object Detection via Data Transformation

Peifang Guo (u. a.)

ISBN 10: 3659494577 / ISBN 13: 9783659494574

Published by LAP LAMBERT Academic Publishing, 2013

About this Item

A Model Based Framework for Object Detection via Data Transformation | Peifang Guo (u. a.) | Taschenbuch | 64 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659494574 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 105527006

Bibliographic Details

• Title: A Model Based Framework for Object Detection via Data Transformation
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2013
• Language: English
• ISBN 10: 3659494577
• ISBN 13: 9783659494574
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 113 grams
• Seller catalogs:
  • Bücher

About this title

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.

About the Author

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