Titlebook: Computer Vision - ECCV 2000; 6th European Confere David Vernon Conference proceedings 2000 Springer-Verlag Berlin Heidelberg 2000 3-D visio

BATE

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大喘气

Calibrating Parameters of Cost Functionalscorrect examples, and given a probabilistic construct for generating wrong examples from correct ones. We introduce a measure of frustration to penalize cases in which wrong responses are preferred to correct ones, and we design a stochastic gradient algorithm which converges to parameters which min

BRACE

Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approachctive Region framework. A statistical analysis based on the Minimum Description Length criterion and the Maximum Likelihood Principle for the observed density function (image histogram) using a mixture of Gaussian elements, indicates the number of the different regions and their intensity properties

Enthralling

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A Probabilistic Interpretation of the Saliency Networknd image curves, maximizing some deterministic quality measure which grows with the length of the curve, its smoothness, and its continuity. This note proposes a modified saliency estimation mechanism, which is based on probabilistically specified grouping cues and on length estimation. In the conte

misanthrope

Layer Extraction with a Bayesian Model of Shapeshis 3D model the scene is represented as a collection of layers and a new method for layer extraction is described. The new segmentation method differs from previous methods in that it uses a specific prior model for layer shape. A probabilistic hierarchical model of layer shape is constructed, whic

Cacophonous

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