Multiscale Image Segmentation Using Energy Minimization

Yin Hui Zhang; Sen Wang; Zhong Hai Shi; Zi Fen He

doi:10.4028/www.scientific.net/AMR.662.940

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Based on the Hybrid Genetic Simulated Annealing Algorithm for Solving Rectangle-Packing
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Data Classification Using Support Vector Machines with Mixture Kernels
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Multiscale Image Segmentation Using Energy Minimization
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Research and Design of Vehicle Dynamic Safety Warning System
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Multiple Attribute Group Decision Making Based on Dominance of Intuitionistic Fuzzy Sets
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Ontology-Based Context Modeling for Mobile Catering Recommendation
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Job Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 662Multiscale Image Segmentation Using Energy...

Multiscale Image Segmentation Using Energy Minimization

Abstract:

We consider the role of multiscale prior information of the object in the form of a Bayesian framework to address the posterior inference problem. The multiscale prior is implicitly estimated from the given image. We show how the multiscale prior effectively exploits the available image data for hierarchical modeling and exploiting posterior inference scheme to determine the posterior likelihood at each iteration with definite number of iteration steps. Extensive experiments show that this method achieves robust multiscale image segmentation results in the presence of dynamic Gaussian noises.