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Background Filtering for Improving of Object Detection in Images

Vrusias, BL, Qin, G and Gillam, L (2010) Background Filtering for Improving of Object Detection in Images In: 20th IEEE International Conference on Pattern Recognition (ICPR), 2010-08-23 - 2010-08-27, Istanbul, Turkey.

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Abstract

We propose a method for improving object recognition in street scene images by identifying and filtering out background aspects. We analyse the semantic relationships between foreground and background objects and use the information obtained to remove areas of the image that are misclassified as foreground objects. We show that such background filtering improves the performance of four traditional object recognition methods by over 40%. Our method is independent of the recognition algorithms used for individual objects, and can be extended to generic object recognition in other environments by adapting other object models

Item Type: Conference or Workshop Item (Paper)
Additional Information:

Copyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Divisions: Faculty of Engineering and Physical Sciences > Computing Science
Depositing User: Symplectic Elements
Date Deposited: 06 Nov 2012 16:55
Last Modified: 23 Sep 2013 19:40
URI: http://epubs.surrey.ac.uk/id/eprint/724462

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