Hands: a pattern theoretic study of biological shapes
Hands: a pattern theoretic study of biological shapes
Elements of information theory
Elements of information theory
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and Efficient Detection of Salient Convex Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perception as Bayesian inference
Perception as Bayesian inference
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Dynamic Programming Generation of Curves on Brain Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence rates of algorithms for visual search: detecting visual contours
Proceedings of the 1998 conference on Advances in neural information processing systems II
On the optimal detection of curves in noisy pictures
Communications of the ACM
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Twenty Questions, Focus of Attention, and A*: A Theoretical Comparison of Optimization Strategies
EMMCVPR '97 Proceedings of the First International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Efficient Optimization of a Deformable Template Using Dynamic Programming
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Minimax Entropy Principle and Its Application to Texture Modeling
Neural Computation
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Searching for an optimal path in a tree with random costs
Artificial Intelligence
Hi-index | 0.01 |
This paper develops a theory for the convergence rates of A* algorithms for real-world vision problems, such as road tracking, which can be formulated in terms of maximizing a reward function derived using Bayesian probability theory. Such problems are well suited to A* tree search and it can be shown that many algorithms proposed to solve them are special cases, or variants, of A*. Moreover, the Bayesian formulation naturally defines a probability distribution on the ensemble of problem instances, which we call the Bayesian Ensemble. We analyze the Bayesian ensemble, using techniques from information theory, and mathematically prove expected O(N) convergence rates of inadmissible A* algorithms. These rates depend on an "order parameter" which characterizes the difficulty of the problem.