A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Separating figure from ground with a Boltzmann machine
Vision, brain, and cooperative computation
Visual reconstruction
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of the Efficiency of Deterministic and Stochastic Algorithms for Visual Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Classifiers by Mixed Adaptation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A common framework for image segmentation
International Journal of Computer Vision
A Cost Minimization Approach to Edge Detection Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Parallel recombinative simulated annealing: a genetic algorithm
Parallel Computing
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Figure-Ground Discrimination: A Combinatorial Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel Simulated Annealing Algorithms for Cell Placement on Hypercube Multiprocessors
IEEE Transactions on Parallel and Distributed Systems
Image processing by simulated annealing
IBM Journal of Research and Development - High-density magnetic recording
Comments on "ground from figure discrimination"
Pattern Recognition Letters
Proximity Graphs Based Multi-scale Image Segmentation
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
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The problem of figure-ground separation is tackled from the perspective of combinatorial optimization. Previous attempts have used deterministic optimization techniques based on relaxation and gradient descent-based search, and stochastic optimizationtechniques based on simulated annealing and microcanonical annealing. A mathematical model encapsulating the figure-ground separation problem that makes explicit the definition of shape in terms of attributes such as cocircularity, smoothness, proximity and contrast is described. The model is based on the formulation of an energy function that incorporates pairwise interactions between local image features in the form of edgels and is shown to be isomorphic to the interacting spin (Ising) system from quantum physics. This paper explores a class of stochastic optimizationtechniques based on evolutionary algorithms for the problem of figure-ground separation. A class of hybrid evolutionary stochastic optimizationalgorithms based on a combination of evolutionary algorithms, simulated annealing and microcanonical annealing are shown to exhibit superior performance when compared to their purely evolutionary counterparts and to classical simulated annealing and microcanonical annealing algorithms. Experimental results on synthetic edgel maps and edgel maps derived from gray scale images are presented.