2003 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'03) Supervised-PCA and SVM Classifiers for Object Detection in Infrared Images Miami, Florida July 21-July 22 ISBN: 0-7695-1971-7
In this paper we tackle the problem of detecting sources of combustion in high definition multispectral Medium Wavelength InfraRed (MWIR) (3-5?m) images. We present a novel approach to this problem consisting in processing the images block-wise using a new technique that we call Supervised Principal Component Analysis (SPCA) to get the components of these blocks. This outperforms state-of-the-art methods with a significant reduction in the complexity of the whole scheme. As a classifier, we propose the use of a Support Vector Machine (SVM) comparing the results from both its novelty-detection and binary non-linear versions. High performance is achieved from a small set of components.
Citation:
R. Santiago-Mozos, J.M. Leiva-Murillo, F. Pérez-Cruz, A. Artés-Rodríguez, "Supervised-PCA and SVM Classifiers for Object Detection in Infrared Images," avss, pp.122, 2003 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'03), 2003 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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