Elements of information theory
Elements of information theory
On the relative complexity of active vs. passive visual search
International Journal of Computer Vision
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
Robust and Efficient Detection of Salient Convex Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimax entropy principle and its application to texture modeling
Neural Computation
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
Empirically-derived estimates of the complexity of labeling line drawings of polyhedral scenes
Artificial Intelligence
Convergence rates of algorithms for visual search: detecting visual contours
Proceedings of the 1998 conference on Advances in neural information processing systems II
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
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Equivalence of Julesz Ensembles and FRAME Models
International Journal of Computer Vision - Special issue on Genomic Signal Processing
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
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking with the EM Contour Algorithm
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A Markov Process Using Curvature for Filtering Curve Images
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Finding Deformable Shapes Using Loopy Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Sketches with Curvature: The Curve Indicator Random Field and Markov Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manhattan world: orientation and outlier detection by Bayesian inference
Neural Computation
Iris Tracking with Feature Free Contours
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Prediction and change detection in sequential data for interactive applications
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Contour tracking based on marginalized likelihood ratios
Image and Vision Computing
Superpixel analysis for object detection and tracking with application to UAV imagery
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Segmentation of crystalline lens in photorefraction video
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Adaptative road lanes detection and classification
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Motion and gray based automatic road segment method MGARS in urban traffic surveillance
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
How do image complexity, task demands and looking biases influence human gaze behavior?
Pattern Recognition Letters
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There is a growing interest in formulating vision problems in terms of Bayesian inference and, in particular, the maximimum a posteriori (MAP) estimator. This approach involves putting prior probability distributions, $P(X)$, on the variables $X$ to be inferred and a conditional distribution $P(Y|X)$ for the measurements $Y$. For example, $X$ could denote the position and configuration of a road in an aerial image and $Y$ can be the aerial image itself (or a filtered version). We observe that these distributions define a probability distribution $P(X,Y)$ on the ensemble of problem instances. In this paper, we consider the special case of detecting roads from aerial images [9] and demonstrate that analysis of this ensemble enables us to determine fundamental bounds on the performance of the MAP estimate (independent of the inference algorithm employed). We demonstrate that performance measures驴such as the accuracy of the estimate and whether the road can be detected at all驴depend on the probabilities $P(Y|X), P(X)$ only by an order parameter $K$. Intuitively, $K$ summarizes the strength of local cues (as provided by local edge filters) together with prior information (i.e., the probable shapes of roads). We demonstrate that there is a phase transition at a critical value of the order parameter $K$驴below this phase transition, it is impossible to detect the road by any algorithm. In related work [25], [5], we derive closely related order parameters which determine the time and memory complexity of search and the accuracy of the solution using the $A^{\ast}$ search strategy. Our approach can be applied to other vision problems and we briefly summarize results when the model uses the 驴wrong prior驴 [26]. We comment on how our work relates to studies of the complexity of visual search [21] and to critical behaviour (i.e., phase transitions) in the computational cost of solving NP-complete problems [19].