A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using probabilistic domain knowledge to reduce the expected computational cost of template matching
Computer Vision, Graphics, and Image Processing
A Markov Random Field Model-Based Approach to Image Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based image interpretation using genetic algorithms
Image and Vision Computing - Special issue: BMVC 1991
Comparison of the performance of modern heuristics for combinatorial optimization on real data
Computers and Operations Research
Parameter Estimation in Markov Random Field Contextual Models Using Geometric Models of Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching as a Nearest-Neighbor Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Markov Random Field Texture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper describes a region extraction algorithm based on the concept of Markov random fields. Markov random fields (MRFs) are characterized by using a Gibbs Distribution which equates back to the MRF. A heuristically developed energy functional is presented and used with the MRF in an efficient and accurate manner. Since the MRF used in this work is defined using the polar coordinate system, a very large search space exists for radial lengths and sites. To aid in pursuing these radial sites, a combinatorial optimization technique known as Tabu Search is exploited. Also provided is an extensive empirical study on aerial imagery and parts detection, in addition to a final discussion and description of future work.