Unidimensional multiscale local features for object detection under rotation and mild occlusions
Document typePart of book or chapter of book
Rights accessOpen Access
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 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.
CitationVillamizar, Michael; Sanfeliu, Alberto; Andrade-Cetto, Juan. "Unidimensional multiscale local features for object detection under rotation and mild occlusions". 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), Girona, Catalunya, 2007. A: Lecture Notes in Computer Science, vol. 4477. Berlin, Alemanya: Springer Verlag, 2007, p. 645 - 651.