Unsupervised texture based image segmentation by simulated annealing using Markov random field and Potts models

Goktepe, M.; Atalay, Volkan; Yalabik, Nese; Yalabik, C.

doi:10.1109/icpr.1998.711275

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Some algorithms are based on simple tools such as the histograms and others are very computationally expensive such as MRF-label modeling. Optimization algorithms such as mean-field annealing (MFA), stochastic simulated annealing (SSA), and iterative conditional mode (ICM) [3,4,5] among others are used to solve segmentation problems in general. MFA has proven its superiority on others in two ways, computational complexity and optimality of the solution [6], and thus was chosen for this work.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Dynamic Scene Algorithm for Segmentation and Motion Estimation

Abdel‐Qader

Bujanovic

2005

EURASIP J. Adv. Signal Process.

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Segmentation and motion estimation are two problems that require accurate estimation for many applications in computer vision and image analysis. This work presents a solution to these two problems simultaneously. Both the segmentation and motion fields are integrated and estimated in parallel to reduce computation time. The presented algorithm is based on producing motion estimates and restored pixel intensity values through an optimization process that uses deterministic mean-field annealing (MFA) framework. The MFA results at different temperature values are used to run a segmentation process using the concept of region-growing-based algorithm. The segmentation process starts at high temperatures and continues in parallel to the annealing process to refine the segmentation process at lower temperatures. The algorithm results are good and dependent on the annealing parameters. Several experimental results from synthetic and real-world sequences are presented

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Section: Introductionmentioning

confidence: 99%

An Integrated Dynamic Scene Algorithm for Segmentation and Motion Estimation

Abdel‐Qader

Bujanovic

2005

EURASIP J. Adv. Signal Process.

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“…Especially, random field models such as Gibbs and Markov random fields have been extensively used to model images [16,17,18,19]. …”

Section: Introductionmentioning

confidence: 99%

Texture Classification and Retrieval Using the Random Neural Network Model

Teke

Atalay

2006

CMS

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Texture is one of the most important characteristics used in computer vision and image processing applications. In this thesis, a new texture classification and retrieval method is proposed for texture analysis applications. The technique makes use of the random neural network model and it is supervised. The main aim is to represent textures with parameters which are the random neural network weights and classify and retrieve textures using this texture definition. The network has neurons that correspond to each image pixel, and the neurons are connected according to neighboring relationship between pixels. The method is tested on artificial images produced by using Brodatz album and texture blocks cut from remotely sensed imagery.

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Segmentation via annealed motion estimates

Abdel‐Qader

Bujanovic

2004

2004 IEEE Electro/Information Technology Conference

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Unsupervised texture based image segmentation by simulated annealing using Markov random field and Potts models

Cited by 6 publications

References 13 publications

An Integrated Dynamic Scene Algorithm for Segmentation and Motion Estimation

An Integrated Dynamic Scene Algorithm for Segmentation and Motion Estimation

Texture Classification and Retrieval Using the Random Neural Network Model

Segmentation via annealed motion estimates

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