MRF-based texture segmentation using wavelet decomposed images

Noda, Hideki; Shirazi, M.N.; Kawaguchi, Eiji

doi:10.1016/s0031-3203(01)00077-2

Cited by 62 publications

(42 citation statements)

References 10 publications

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“…To maintain spatial and edge consistency, we apply Markov Random Field (MRF) [29], which asserts that the conditional probability of a pixel only depends on its neighbors. In this paper, we use a Gaussian MRF to model P(l(x)), which is characterized by the following local conditional probability density function…”

Section: Prior P(s)mentioning

confidence: 99%

A Bayesian Probabilistic Framework for Rain Detection

Yao

Wang

Hong

et al. 2014

Entropy

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Heavy rain deteriorates the video quality of outdoor imaging equipments. In order to improve video clearness, image-based and sensor-based methods are adopted for rain detection. In earlier literature, image-based detection methods fall into spatio-based and temporal-based categories. In this paper, we propose a new image-based method by exploring spatio-temporal united constraints in a Bayesian framework. In our framework, rain temporal motion is assumed to be Pathological Motion (PM), which is more suitable to time-varying character of rain steaks. Temporal displaced frame discontinuity and spatial Gaussian mixture model are utilized in the whole framework. Iterated expectation maximization solving method is taken for Gaussian parameters estimation. Pixels state estimation is finished by an iterated optimization method in Bayesian probability formulation. The experimental results highlight the advantage of our method in rain detection.

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Section: Prior P(s)mentioning

confidence: 99%

A Bayesian Probabilistic Framework for Rain Detection

Yao

Wang

Hong

et al. 2014

Entropy

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“…MRF is the most widely used statistical model which has a lot of applications in the fields of image edge detection, segmentation, veins analysis and image recovery [25]. MRF increases the constraints of image process with prior knowledge, combining with Gaussian conditional distribution, MRF provides a convenient method to describe the space-related features of every image pixel in probability.…”

Section: Image Segmentation Based On Mrf Modelmentioning

confidence: 99%

“…Segmentation with above function is not ideal as it does not consider the influence of gray value, in the proposed algorithm, we introduced gray value into the potential function, taking full advantage of contexts, the new potential function of gray value is defined as [25]:…”

Section: Mrf Image Modelmentioning

confidence: 99%

A Novel Sar Target Detection Algorithm Based on Contextual Knowledge

Gao¹,

Ru²,

Sun³

et al. 2013

PIER

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Abstract-This paper proposes a Synthetic Aperture Radar (SAR) vehicle target detection algorithm based on contextual knowledge. The proposed algorithm firstly obtains the general classification of SAR image with a Markov Random Field (MRF)-based segmentation algorithm; then modifies the prior target presence probability utilizing terrain types, distances to boundary and target aggregation degree; finally gains the detection results using improved Cell AveragingConstant False Alarm Rate (CA-CFAR). Detections with real SAR image data show that the proposed algorithm can effectively improve target detection rate and reduce false alarms compared with conventional CA-CFAR.

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“…Its applications can be found in a wide variety of areas such as remote sensing, vehicle and robot navigation, medical imaging, surveillance, target identiÿcation and tracking, scene analysis, product inspection/quality control, etc. It is also frequently cast as an optimization problem wherein the partitioning of the target image corresponding to the optimal value of an objective function is sought [3,12,14,17,19]. The appropriateness of the objective function dictates the accuracy of the segmentation results.…”

Section: Introductionmentioning

confidence: 99%

Multiresolution image segmentation integrating Gibbs sampler and region merging algorithm

2003

Signal Processing

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MRF-based texture segmentation using wavelet decomposed images

Cited by 62 publications

References 10 publications

A Bayesian Probabilistic Framework for Rain Detection

A Bayesian Probabilistic Framework for Rain Detection

A Novel Sar Target Detection Algorithm Based on Contextual Knowledge

Multiresolution image segmentation integrating Gibbs sampler and region merging algorithm

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