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Issue No.09 - Sept. (2012 vol.34)
pp: 1773-1784
O. Barinova , Lomonosov Moscow State Univ., Moscow, Russia
V. Lempitsky , Yandex, Moscow, Russia
P. Kholi , Microsoft Res., Cambridge, UK
ABSTRACT
Hough transform-based methods for detecting multiple objects use nonmaxima suppression or mode seeking to locate and distinguish peaks in Hough images. Such postprocessing requires the tuning of many parameters and is often fragile, especially when objects are located spatially close to each other. In this paper, we develop a new probabilistic framework for object detection which is related to the Hough transform. It shares the simplicity and wide applicability of the Hough transform but, at the same time, bypasses the problem of multiple peak identification in Hough images and permits detection of multiple objects without invoking nonmaximum suppression heuristics. Our experiments demonstrate that this method results in a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category-level (pedestrian) detection problem.
INDEX TERMS
Transforms, Probabilistic logic, Object detection, Image edge detection, Joints, Cognition, Random variables, scene understanding., Hough transforms, object detection in images, line detection
CITATION
O. Barinova, V. Lempitsky, P. Kholi, "On Detection of Multiple Object Instances Using Hough Transforms", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 9, pp. 1773-1784, Sept. 2012, doi:10.1109/TPAMI.2012.79
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