Robust Analysis of Silhouettes by Morphological Size Distributions

We address the topic of real-time analysis and recognition of silhouettes. The method that we propose first produces object features obtained by a new type of morphological operators, which can be seen as an extension of existing granulometric filters, and then insert them into a tailored classification scheme. Intuitively, given a binary segmented image, our operator produces the set of all the largest rectangles that can be wedged inside any connected component of the image. The latter are obtained by a standard background subtraction technique and morphological filtering. To classify connected components into one of the known object categories, the rectangles of a connected component are submitted to a machine learning algorithm called EXtremely RAndomized trees (Extra-trees). The machine learning algorithm is fed with a static database of silhouettes that contains both positive and negative instances. The whole process, including image processing and rectangle classification, is ca...

• Type: Conference
• Year: 2006
• Where: ACIVS
• Authors: Olivier Barnich, Sébastien Jodogne, Marc Van Droogenbroeck
• Keywords: ACIVS 2006, Computer Vision, Connected Component, Machine Learning Algorithm, Produces Object Features