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Local wavelet features for statistical object classification and localisation

Grzegorzek, Marcin and Sav, Sorin Vasile and Izquierdo, Ebroul and O'Connor, Noel E. (2009) Local wavelet features for statistical object classification and localisation. IEEE Multimedia . ISSN 1070-986X (In Press)

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This article presents a system for texture-based probabilistic classification and localisation of 3D objects in 2D digital images and discusses selected applications. The objects are described by local feature vectors computed using the wavelet transform. In the training phase, object features are statistically modelled as normal density functions. In the recognition phase, a maximisation algorithm compares the learned density functions with the feature vectors extracted from a real scene and yields the classes and poses of objects found in it. Experiments carried out on a real dataset of over 40000 images demonstrate the robustness of the system in terms of classification and localisation accuracy. Finally, two important application scenarios are discussed, namely classification of museum artefacts and classification of metallography images.

Item Type:Article (In Press)
Subjects:Computer Science > Image processing
Computer Science > Information retrieval
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Publisher:IEEE Computer Society
Official URL:
Copyright Information:© 2009 IEEE
Funders:EU FP6 Network of Excellence - K-Space
ID Code:2338
Deposited On:06 Apr 2010 16:02 by Noel O'Connor. Last Modified 20 Oct 2016 10:57

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