A Cost Minimization Approach to Edge Detection Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary Approaches to Figure-Ground Separation
Applied Intelligence
Figure-Ground Discrimination: A Combinatorial Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segregating and extracting overlapping data points in two-dimensional plots
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
International Journal of Remote Sensing
Global optimization for first order Markov Random Fields with submodular priors
Discrete Applied Mathematics
Real-time image segmentation on a GPU
Facing the multicore-challenge
Real-time image segmentation on a GPU
Facing the multicore-challenge
Composite stock cutting through simulated annealing
Mathematical and Computer Modelling: An International Journal
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It is shown that simulated annealing, a statistical mechanics method recently proposed as a tool in solving complex optimization problems, can be used in problems arising in image processing. The problems examined are the estimation of the parameters necessary to describe a geometrical pattern corrupted by noise, the smoothing of bi-level images, and the process of halftoning a continuous-level image. The analogy between the system to be optimized and an equivalent physical system, whose ground state is sought, is put forward by showing that some of these problems are formally equivalent to ground state problems for two-dimensional Ising spin systems. In the case of low signal-to-noise ratios (particularly in image smoothing), the methods proposed here give better results than those obtained with standard techniques.