Designing 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.

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