scholarly journals Irregular Tessellation and Statistical Modeling Based Regionalized Segmentation for SAR Intensity Image

2020 ◽  
Vol 12 (5) ◽  
pp. 753
Author(s):  
Quanhua Zhao ◽  
Hongyun Zhang ◽  
Guanghui Wang ◽  
Yu Li
Keyword(s):  
Gamma Distribution ◽  
Probability Model ◽  
Voronoi Tessellation ◽  
Processing Unit ◽  
Image Domain ◽  
Segmentation Methods ◽  
Likelihood Model ◽  
Intensity Images ◽  
Homogeneous Regions ◽  
Gamma Distribution Parameter

This paper presents a regionalized segmentation method for synthetic aperture radar (SAR) intensity images based on tessellation with irregular polygons. In the proposed method, the image domain is partitioned into a collection of irregular polygons, which are constructed using sets of nodes and are used to fit homogeneous regions with arbitrary shapes. Each partitioned polygon is taken as the basic processing unit. Assuming the intensities of the pixels in the polygon follow an independent and identical gamma distribution, the likelihood of the image intensities is modeled. After defining the prior distributions of the tessellation and the parameters for the likelihood model, a posterior probability model can be built based on the Bayes theorem as a segmentation model. To obtain optimal segmentation, a reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm is designed to simulate from the segmentation model, where the move operations include updating the gamma distribution parameter, updating labels, moving nodes, merging polygons, splitting polygons, adding nodes, and deleting nodes. Experiments were carried out on synthetic and real SAR intensity images using the proposed method while the regular and Voronoi tessellation-based methods were also preformed for comparison. Our results show the proposed method overcomes some intrinsic limitations of current segmentation methods and is able to generate good results for homogeneous regions with different shapes.

scholarly journals AN ADAPTIVE POLYGONAL CENTROIDAL VORONOI TESSELLATION ALGORITHM FOR SEGMENTATION OF NOISY SAR IMAGES

2014 ◽  
Vol XL-2/W3 ◽  
pp. 65-68 ◽  
Author(s):  
G. Askari ◽  
Y. Li ◽  
R. MoezziNasab
Keyword(s):  
Voronoi Tessellation ◽  
Speckle Noise ◽  
Sar Images ◽  
Image Domain ◽  
Voronoi Polygons ◽  
Centroidal Voronoi Tessellation ◽  
Novel Method ◽  
Low Sensitivity ◽  
Homogeneous Regions ◽  
User Friendly

In this research, a fast, adaptive and user friendly segmentation methodology is developed for highly speckled SAR images. The developed region based centroidal Voronoi tessellation (R-BCVT) algorithm is a kind of polygon-based clustering approach in which the algorithm attempts to (1) split the image domain into j numbers of centroidal Voronoi polygons (2) assign each polygon a label randomly, then (3) classify the image into k cluster iteratively to satisfy optimum segmentation, and finally a k-mean clustering method refine the detected boundaries of homogeneous regions. The advantages of the novel method arise from adaptively, simplicity and rapidity as well as low sensitivity of the model to speckle noise.

A statistical approach to identifying closed object boundaries in images

1994 ◽  
Vol 26 (4) ◽  
pp. 831-854 ◽  
Author(s):  
Jeffrey D. Helterbrand ◽  
Noel Cressie ◽  
Jennifer L. Davidson
Keyword(s):  
Prior Probability ◽  
Statistical Approach ◽  
Transition Probability ◽  
Probability Model ◽  
Monte Carlo Algorithm ◽  
Structural Components ◽  
Stationary Model ◽  
Boundary Estimate ◽  
Image Domain ◽  
Statistical Approaches

In this research, we present a statistical theory, and an algorithm, to identify one-pixel-wide closed object boundaries in gray-scale images. Closed-boundary identification is an important problem because boundaries of objects are major features in images. In spite of this, most statistical approaches to image restoration and texture identification place inappropriate stationary model assumptions on the image domain. One way to characterize the structural components present in images is to identify one-pixel-wide closed boundaries that delineate objects. By defining a prior probability model on the space of one-pixel-wide closed boundary configurations and appropriately specifying transition probability functions on this space, a Markov chain Monte Carlo algorithm is constructed that theoretically converges to a statistically optimal closed boundary estimate. Moreover, this approach ensures that any approximation to the statistically optimal boundary estimate will have the necessary property of closure.

scholarly journals Variability in rainfall dispersion in Madhya Pradesh

MAUSAM ◽  
2021 ◽  
Vol 43 (1) ◽  
pp. 29-36
Author(s):  
H.P. DAS ◽  
A. CHOWDHURY
Keyword(s):  
Gamma Distribution ◽  
Rainfall Variability ◽  
Probability Model ◽  
Regional Distribution ◽  
Madhya Pradesh ◽  
Regression Equations ◽  
Scale Parameters ◽  
Agronomic Practices ◽  
Distribution Probability ◽  
The Mean

An attempt has been made to examine distribution and dispersion in rainfall variability in Madhya Pradesh by applying Gamma distribution probability model, The spatial and regional distribution of shape and scale parameters of the Gamma distribution have been examined, Periods of water surpluses and deficiencies have been identified by comparing the probability rainfall with the water requirement. Regression equations have been developed to find probabilitistic rainfall from the mean rainfall. Agronomic practices have been evaluated for efficient utilization of water resources for crop planning.  

scholarly journals Gamma distributions for stationary Poisson flat processes

2009 ◽  
Vol 41 (4) ◽  
pp. 911-939 ◽  
Author(s):  
Volker Baumstark ◽  
Günter Last
Keyword(s):  
Poisson Process ◽  
Gamma Distribution ◽  
Shape Parameter ◽  
Voronoi Tessellation ◽  
Isotropic Case ◽  
Intensity Measure ◽  
First Case ◽  
Gamma Distributions ◽  
The Face ◽  
The Mean

We consider a stationary Poisson process X of k-flats in ℝd with intensity measure Θ and a measurable set S of k-flats depending on F1,…,Fn∈ X, x∈ℝd, and X in a specific equivariant way. If (F1,…,Fn,x) is properly sampled (in a ‘typical way’) then Θ(S) has a gamma distribution. This result generalizes and unifies earlier work by Miles (1971), Møller and Zuyev (1996), and Zuyev (1999). As a new example, we will show that the volume of the fundamental region of a typical j-face of a stationary Poisson–Voronoi tessellation is conditionally gamma distributed. This is true in the area-biased and the area-debiased cases. In the first case the shape parameter is not integer valued. As another new example, we will show that the generalized integral-geometric contents of the (area-biased and area-debiased) typical j-face of a Poisson hyperplane tessellation are conditionally gamma distributed. In the isotropic case the contents boil down to the mean breadth of the face.

scholarly journals SAR Image Segmentation Using Region Smoothing and Label Correction

2020 ◽  
Vol 12 (5) ◽  
pp. 803
Author(s):  
Ronghua Shang ◽  
Junkai Lin ◽  
Licheng Jiao ◽  
Yangyang Li
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Majority Voting ◽  
Sar Image ◽  
Application Process ◽  
Sar Images ◽  
Sar Image Segmentation ◽  
Segmentation Methods ◽  
The Difference ◽  
Homogeneous Regions

The traditional unsupervised image segmentation methods are widely used in synthetic aperture radar (SAR) image segmentation due to the simple and convenient application process. In order to solve the time-consuming problem of the common methods, an SAR image segmentation method using region smoothing and label correction (RSLC) is proposed. In this algorithm, the image smoothing results are used to approximate the results of the spatial information polynomials of the image. Thus, the segmentation process can be realized quickly and effectively. Firstly, direction templates are used to detect the directions at different coordinates of the image, and smoothing templates are used to smooth the edge regions according to the directions. It achieves the smoothing of the edge regions and the retention of the edge information. Then the homogeneous regions are presented indirectly according to the difference of directions. The homogeneous regions are smoothed by using isotropic operators. Finally, the two regions are fused for K-means clustering. The majority voting algorithm is used to modify the clustering results, and the final segmentation results are obtained. Experimental results on simulated SAR images and real SAR images show that the proposed algorithm outperforms the other five state-of-the-art algorithms in segmentation speed and accuracy.

scholarly journals Gamma distributions for stationary Poisson flat processes

2009 ◽  
Vol 41 (04) ◽  
pp. 911-939 ◽  
Author(s):  
Volker Baumstark ◽  
Günter Last
Keyword(s):  
Poisson Process ◽  
Gamma Distribution ◽  
Shape Parameter ◽  
Voronoi Tessellation ◽  
Isotropic Case ◽  
Intensity Measure ◽  
First Case ◽  
Gamma Distributions ◽  
The Face ◽  
The Mean

We consider a stationary Poisson process X of k-flats in ℝd with intensity measure Θ and a measurable set S of k-flats depending on F 1,…,F n ∈ X, x∈ℝd, and X in a specific equivariant way. If (F 1,…,F n ,x) is properly sampled (in a ‘typical way’) then Θ(S) has a gamma distribution. This result generalizes and unifies earlier work by Miles (1971), Møller and Zuyev (1996), and Zuyev (1999). As a new example, we will show that the volume of the fundamental region of a typical j-face of a stationary Poisson–Voronoi tessellation is conditionally gamma distributed. This is true in the area-biased and the area-debiased cases. In the first case the shape parameter is not integer valued. As another new example, we will show that the generalized integral-geometric contents of the (area-biased and area-debiased) typical j-face of a Poisson hyperplane tessellation are conditionally gamma distributed. In the isotropic case the contents boil down to the mean breadth of the face.

A statistical approach to identifying closed object boundaries in images

1994 ◽  
Vol 26 (04) ◽  
pp. 831-854 ◽  
Author(s):  
Jeffrey D. Helterbrand ◽  
Noel Cressie ◽  
Jennifer L. Davidson
Keyword(s):  
Prior Probability ◽  
Statistical Approach ◽  
Transition Probability ◽  
Probability Model ◽  
Monte Carlo Algorithm ◽  
Structural Components ◽  
Stationary Model ◽  
Boundary Estimate ◽  
Image Domain ◽  
Statistical Approaches

In this research, we present a statistical theory, and an algorithm, to identify one-pixel-wide closed object boundaries in gray-scale images. Closed-boundary identification is an important problem because boundaries of objects are major features in images. In spite of this, most statistical approaches to image restoration and texture identification place inappropriate stationary model assumptions on the image domain. One way to characterize the structural components present in images is to identify one-pixel-wide closed boundaries that delineate objects. By defining a prior probability model on the space of one-pixel-wide closed boundary configurations and appropriately specifying transition probability functions on this space, a Markov chain Monte Carlo algorithm is constructed that theoretically converges to a statistically optimal closed boundary estimate. Moreover, this approach ensures that any approximation to the statistically optimal boundary estimate will have the necessary property of closure.

scholarly journals REGIONAL SAR IMAGE SEGMENTATION BASED ON FUZZY CLUSTERING WITH GAMMA MIXTURE MODEL

2017 ◽  
Vol IV-2/W4 ◽  
pp. 471-475
Author(s):  
X. L. Li ◽  
Q. H. Zhao ◽  
Y. Li
Keyword(s):  
Image Segmentation ◽  
Mixture Model ◽  
Fuzzy Clustering ◽  
Voronoi Tessellation ◽  
Dissimilarity Measure ◽  
Model Parameters ◽  
Sar Image ◽  
Sar Images ◽  
Image Domain ◽  
Sar Image Segmentation

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

EVALUATION OF DYNAMIC CHANGES OF CONSUMERS' ONLINE REVIEWS FROM THE ELABORATION LIKELIHOOD MODEL METHOD

2021 ◽  
Vol 65 (04) ◽  
pp. 17-23
Author(s):  
Kai Yang ◽  
◽  
Mahammad Malikov ◽  
Keyword(s):  
Source Credibility ◽  
Probability Model ◽  
Online Reviews ◽  
Elaboration Likelihood Model ◽  
Elaboration Likelihood ◽  
Dynamic Changes ◽  
Model Method ◽  
Likelihood Model ◽  
Research Findings ◽  
Elaboration Process

One of the essential models that explain the concept of persuasion is the Elaboration Probability Model, which argues that there are two processes in the persuasion process, namely central and environmental persuasion. İt is thought that the article, including a detailed explanation of the model and information about the model, filled a gap in this regard. The research findings show that the model is mainly used in marketing and advertising studies. In terms of the level of affecting the probability of elaboration, the feature subject to the most significant number of studies is interesting. It was revealed that the factors involved in the elaboration process and whose effect has been handled the most in the number of studies are the power of assertion and the source credibility. The review provides valuable information in terms of allowing the model's use and the variables in the model to be seen as a holistic example in Amazon. Key words: ELM method, Amazon, main factors, reviews

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