Information Processing Using Energy Function Minimization
A good deal of recent research into sensory information processing algorithms has focussed on the so-called energy function minimization, or regularization, approach to inverting the world-image mapping. In this chapter we review the application of this paradigm to early vision and its incorporation within the Bayesian framework. We will concentrate in this section on using this formulation to impose smoothness constraints on the solutions; other constraints can be similarly imposed, as will be described in later sections. A large part of this chapter will be concerned with new approaches to formulating energy function based algorithms using techniques borrowed from statistical mechanics.
KeywordsPartition Function Energy Function Data Fusion Markov Random Field Reflectance Function
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