A Theoretical Framework for Relaxation Processes in Pattern Recognition: Application to Robust Nonparametric Contour Generalization
Issue No. 08 - August (2003 vol. 25)
<p><b>Abstract</b>—While various approaches are suggested in the literature to describe and generalize relaxation processes concerning to several objectives, the wider problem addressed here is to find the best-suited relaxation process for a given assignment problem, or better still, to construct a task-dependent relaxation process. For this, we develop a general framework for the theoretical foundations of relaxation processes in pattern recognition. The resulting structure enables 1) a description of all known relaxation processes in general terms and 2) the design of task-dependent relaxation processes. We show that the well-known standard relaxation formulas verify our approach. Referring to the common problem of generating a generalized description of a contour we demonstrate the applicability of the suggested generalization in detail. Important characteristics of the constructed task-dependent relaxation process are: 1) the independency of the segmentation from any parameters, 2) the invariance to geometric transformations, 3) the simplicity, and 4) efficiency.</p>
Generalization, compatibility function, support function, relaxation operator, significance measure, information theoretic model selection.
P. Faber, "A Theoretical Framework for Relaxation Processes in Pattern Recognition: Application to Robust Nonparametric Contour Generalization," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 25, no. , pp. 1021-1027, 2003.