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RESEARCH

Unidimensional Multiscale Local Features for Object Detection under Rotation and Mild Occlusions
M. Villamizar, A. Sanfeliu and J. Andrade Cetto

Abstract - In this article, scale and orientation invariant object detection is performed by matching intensity level histograms. Unlike other global measurement methods, the present one uses a local feature description that allows small changes in the histogram signature, giving robustness to partial occlusions. Local features over the object histogram are extracted during a \emph{Boosting} learning phase, selecting the most discriminant features within a training histogram image set. The Integral Histogram has been used to compute local histograms in constant time.

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