Object Detection Using Geometrical Block Structures

Object Detection Using Geometrical Block Structures

We propose a novel method of object detection in unconstrained, clustered scenes. Our method strongly benefits from object representation using geometrical structure of image blocks. It comes from an intuition that object has strong relationships between some of its components. It effectively extend...

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Bibliographic Details
Published in:Third International Conference on Natural Computation (ICNC 2007) Vol. 3; pp. 561 - 565
Main Authors: Zisheng Cao, Feng Chen, Youtian Du
Format: Conference Proceeding
Language:English
Published: IEEE 01-08-2007
Subjects:
Automation
Bismuth
Computer vision
Face detection
Feature extraction
Histograms
Humans
Layout
Object detection
Testing
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Summary:We propose a novel method of object detection in unconstrained, clustered scenes. Our method strongly benefits from object representation using geometrical structure of image blocks. It comes from an intuition that object has strong relationships between some of its components. It effectively extends the features of local area to the global using a complete graph of blocks so as to achieve a perspective of features in geometrical structure of the object. AdaBoost is adopted to select those relations of block pairs which are able to distinguish object from the rest while designing classifier. This method gives a good result when we use face and human detection as testing cases.
ISBN:9780769528755
0769528759
ISSN:2157-9555
DOI:10.1109/ICNC.2007.508