Range Image Segmentation by Randomized Region Growing and Bayesian Edge Regularization

Mazouzi, Smaine; Batouche, Mohamed

doi:10.3844/jcssp.2007.310.317

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…A recent application of Bayesian classifier [8] is in the implementation of the Bayesian training method to construct a series of hybrid Artificial Neural Network (ANN) structures to model hot rolling force prediction of real input/output data and practical expressions. In [9], the Bayesian estimation, based on some prior assumptions on the regions for the range image segmentation has been used. Image priors were modeled by a new Markov Random Field (MRF) model.…”

Section: Introductionmentioning

confidence: 99%

Modified Unsupervised Image Segmentation based on Gaussian Mixture Model for Traffic Surveillance Applications

SivaNagiReddy¹,

Bhaskara²,

Vikram³

2012

IJCA

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This paper deals with an efficient image segmentation algorithm for video images which is quite useful for video based traffic surveillance applications. It includes video segmentations, morphological operations and labeling. In the field of surveillance system, effective video object segmentation is a conveyance for video analysis and processing. It presents a new algorithm for video object segmentation i.e. unsupervised image segmentation based on Gaussian mixture model with modified EM procedure. It uses the spatial unsupervised GMM clustering technique in which the objective function is modified or the prior term is added in the Bayesian. Firstly we use EM algorithm to estimate the distribution of input image data with which the number of mixture components is automatically determined. Secondly the segmentation is arrived at by clustering each pixel into the appropriate component according to the Minimum Message Length (MML) criterion with the help of appropriate priors like Dirichlet-Normal-Wishart (DNW) prior. The proposed technique automatically decides the best number of clusters for images. The best number of clusters is obtained by using the cluster validity criterion with the help of Gaussian distribution.

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Section: Introductionmentioning

confidence: 99%

Modified Unsupervised Image Segmentation based on Gaussian Mixture Model for Traffic Surveillance Applications

SivaNagiReddy¹,

Bhaskara²,

Vikram³

2012

IJCA

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show abstract

“…Moussaoui et al (2006) implemented Bayesian training method to construct a series of hybrid Artificial Neural Network (ANN) structures to model hot rolling force prediction from real input/output data and empirical expressions. Mazouzi and Batouche (2007) used Bayesian estimation, based on some prior assumptions on the regions for Range image segmentation. Image priors were modeled by a new Markov Random Field (MRF) model.…”

Section: Unsupervisedmentioning

confidence: 99%

A New Modified Gaussian Mixture Model for Color-Texture Segmentation

Sujaritha¹,

Annadurai²

2011

Journal of Computer Science

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Problem statement:This study presents a new, simple and efficient modified Gaussian mixture model based clustering algorithm for color-texture segmentation. The proposed mixture model introduces a new component density function which incorporates spatial information and the weighting factor for neighborhood effect is fully adaptive to the image content. Approach: It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge-blurring effect. An Expectation Maximization (EM) model fitting Maximum A Posteriori (MAP) algorithm segments the image by utilizing the pixel's color and texture features and the captured neighborhood relationships among them. Results: The algorithm simultaneously calculates the model parameters and segments the pixels iteratively in an interleaved manner. Finally, it converges to a solution where the model parameters and pixel labels are stabilized within a specified criterion. Conclusion:The experimental results with synthetic and natural images demonstrate that the proposed method is effective in improving the segmentation and it outperforms the Fusion of Clustering Results (FCR) algorithm, which is the recent popular segmentation approach.

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“…These methods suffer from their computational complexity and their implementation not as easy as the previous named methods. Mazouzi and Batouche (2007) used randomized region growing and bayesian edge regularization.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

A New Approach for 3D Range Image Segmentation using Gradient Method

Hafiz¹,

Sheta²,

Bayoumi³

et al. 2011

Journal of Computer Science

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Problem statement: Segmentation of 3D range images is widely used in computer vision as an essential pre-processing step before the methods of high-level vision can be applied. Segmentation aims to study and recognize the features of range image such as 3D edges, connected surfaces and smooth regions. Approach: This study presents new improvements in segmentation of terrestrial 3D range images based on edge detection technique. The main idea is to apply a gradient edge detector in three different directions of the 3D range images. This 3D gradient detector is a generalization of the classical sobel operator used with 2D images, which is based on the differences of normal vectors or geometric locations in the coordinate directions. The proposed algorithm uses a 3D-grid structure method to handle large amount of unordered sets of points and determine neighborhood points. It segments the 3D range images directly using gradient edge detectors without any further computations like mesh generation. Our algorithm focuses on extracting important linear structures such as doors, stairs and windows from terrestrial 3D range images these structures are common in indoors and outdoors in many environments. Results: Experimental results showed that the proposed algorithm provides a new approach of 3D range image segmentation with the characteristics of low computational complexity and less sensitivity to noise. The algorithm is validated using seven artificially generated datasets and two real world datasets. Conclusion/Recommendations: Experimental results showed that different segmentation accuracy is achieved by using higher Grid resolution and adaptive threshold.

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Range Image Segmentation by Randomized Region Growing and Bayesian Edge Regularization

Cited by 8 publications

References 23 publications

Modified Unsupervised Image Segmentation based on Gaussian Mixture Model for Traffic Surveillance Applications

Modified Unsupervised Image Segmentation based on Gaussian Mixture Model for Traffic Surveillance Applications

A New Modified Gaussian Mixture Model for Color-Texture Segmentation

A New Approach for 3D Range Image Segmentation using Gradient Method

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