Order Parameters for Dete ting Target Curves inImages : When does high level knowledge help ?

High-Level and Generic Models for Visual Search: When Does High Level Knowledge Help?

We analyze the problem of detecting a road target in background clutter and investigate the amount of prior (i.e. target specific) knowledge needed to perform this search task. The problem is formulated in terms of Bayesian inference and we define a Bayesian ensemble of problem instances. This formulation implies that the performance measures of different models depend on order parameters which...

Order Parameters for Minimax Entropy Distributions: When Does High Level Knowledge Help?

Many problems in vision can be formulated as Bayesian inference. It is important to determine the accuracy of these inferences and how they depend on the problem domain. In recent work, Coughlan and Yuille showed that, for a restricted class of problems, the performance of Bayesian inference could be summarized by an order parameter K which depends on the probability distributions which charact...

Detecting Graph-Based Spatial Outliers: Algorithms

ABSTRACT Identi ation of outliers an lead to the dis overy of unexpe ted, interesting, and useful knowledge. Existing methods are designed for dete ting spatial outliers in multidimensional geometri data sets, where a distan e metri is available. In this paper, we fo us on dete ting spatial outliers in graph stru tured data sets. We de ne statisti al tests, analyze the statisti al foundation un...

Exploiting Syntax when Dete ting Protein Names in Text

Dete ting Novel Features of an Environment Using Habituation