Artificial intelligence (2nd ed.)
Artificial intelligence (2nd ed.)
Elements of information theory
Elements of information theory
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perception as Bayesian inference
Perception as Bayesian inference
Fundamental Limits of Bayesian Inference: Order Parameters and Phase Transitions for Road Tracking
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
Efficient deformable template detection and localization without user initialization
Computer Vision and Image Understanding
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Order Parameters for Detecting Target Curves in Images: When Does High Level Knowledge Help?
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Efficient Optimization of a Deformable Template Using Dynamic Programming
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Study of Parts-Based Object Class Detection Using Complete Graphs
International Journal of Computer Vision
Recursive Compositional Models for Vision: Description and Review of Recent Work
Journal of Mathematical Imaging and Vision
SLEDGE: Sequential Labeling of Image Edges for Boundary Detection
International Journal of Computer Vision
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Many perception, reasoning, and learning problems can be expressed as Bayesian inference. We point out that formulating a problem as Bayesian inference implies specifying a probability distribution on the ensemble of problem instances. This ensemble can be used for analyzing the expected complexity of algorithms and also the algorithm-independent limits of inference. We illustrate this problem by analyzing the complexity of tree search. In particular, we study the problem of road detection, as formulated by Geman and Jedynak (1996). We prove that the expected convergence is linear in the size of the road (the depth of the tree) even though the worst-case performance is exponential. We also put a bound on the constant of the convergence and place a bound on the error rates.