Object Detection and Analysis
BeschreibungDesigning a general purpose computer vision system with performance comparable to that of the human vision system is the goal of many researchers. This book introduces a local appearance method, termed coherency filtering, which allows for the robust detection and analysis of rigid objects contained in heterogeneous scenes by properly exploiting the wealth of information returned by a k-nearest neighbours (k-NN) classifier. A significant advantage of k-NN classifiers is their ability to indicate uncertainty in the classification of a local window by returning a list of k candidate classifications. Classification of a local window can be inherently uncertain when considered in isolation since local windows from different objects may be similar in appearance. In order to robustly identify objects in a query image, a process is needed to appropriately resolve this uncertainty. Coherency filtering resolves this uncertainty by imposing constraints across the colour channels of a query image along with spatial constraints between neighbouring local windows in a manner that produces reliable classification of local windows and ultimately results in the robust identification of objects.
PortraitDonovan H. Parks received his B.Eng. from the University of Victoria in 2004 and his M.Eng. from McGill University in 2007. He has been awarded a Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship to pursue doctoral research in human motion analysis.
Untertitel: A Coherency Filtering Approach. Paperback. Sprache: Englisch.
Verlag: VDM Verlag
Erscheinungsdatum: Mai 2008
Seitenanzahl: 172 Seiten