IMAGE SEGMENTATION: A WATERSHED TRANSFORMATION ALGORITHM

The goal of this work is to present a new method for image segmentation using mathematicalmorphology. The approach used is based on the watershed transformation. In order to avoid an oversegmentation, we propose to adapt the topological gradient method. The watershed transformation combined with a fast algorithm based on the topological gradient approach gives good results. The numerical tests obtained illustrate the efficiency of our approach for image segmentation.

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Fast Algorithm for Selective Image Segmentation Model

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.33.23481 ◽

2018 ◽

Vol 7 (4.33) ◽

pp. 41

Author(s):

Abdul K Jumaat ◽

Ke Chen

Keyword(s):

Image Segmentation ◽

Fast Algorithm ◽

Visual Quality ◽

Projection Algorithm ◽

Computational Time ◽

Segmentation Result ◽

Numerical Tests ◽

Specific Object ◽

The Common

Selective image segmentation model aims to separate a specific object from its surroundings. To solve the model, the common practice to deal with its non-differentiable term is to approximate the original functional. While this approach yields to successful segmentation result, however the segmentation process can be slow. In this paper, we showed how to solve the model without approximation using Chambolle’s projection algorithm. Numerical tests show that good visual quality of segmentation is obtained in a fast-computational time.

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Automatic Image Segmentation Using Edge and Marker-Controlled Watershed Transformation

International Conference on Advances in Engineering and Technology (ICAET'2014) March 29-30, 2014 Singapore ◽

10.15242/iie.e0314062 ◽

2014 ◽

Keyword(s):

Image Segmentation ◽

Watershed Transformation

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One Extended OTSU Flame Image Recognition Method Using RGBL and Stripe Segmentation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.2141 ◽

2011 ◽

Vol 121-126 ◽

pp. 2141-2145 ◽

Cited By ~ 11

Author(s):

Wei Gang Yan ◽

Chang Jian Wang ◽

Jin Guo

Keyword(s):

Image Segmentation ◽

Water Vapor ◽

Image Recognition ◽

Recognition Accuracy ◽

New Method ◽

Recognition Method ◽

Otsu’S Method ◽

Flame Shape ◽

Image Segmentation Algorithm ◽

Rgb Image

This paper proposes a new image segmentation algorithm to detect the flame image from video in enclosed compartment. In order to avoid the contamination of soot and water vapor, this method first employs the cubic root of four color channels to transform a RGB image to a pseudo-gray one. Then the latter is divided into many small stripes (child images) and OTSU is employed to perform child image segmentation. Lastly, these processed child images are reconstructed into a whole image. A computer program using OpenCV library is developed and the new method is compared with other commonly used methods such as edge detection and normal Otsu’s method. It is found that the new method has better performance in flame image recognition accuracy and can be used to obtain flame shape from experiment video with much noise.

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Fast algorithm for optimal graph-Laplacian based 3D image segmentation

10.1063/1.4965010 ◽

2016 ◽

Author(s):

S. Harizanov ◽

I. Georgiev

Keyword(s):

Image Segmentation ◽

Fast Algorithm ◽

Graph Laplacian ◽

3D Image ◽

3D Image Segmentation ◽

Optimal Graph

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An Overview of the Topological Gradient Approach in Image Processing: Advantages and Inconveniences

Journal of Applied Mathematics ◽

10.1155/2010/761783 ◽

2010 ◽

Vol 2010 ◽

pp. 1-19 ◽

Cited By ~ 1

Author(s):

Lamia Jaafar Belaid

Keyword(s):

Image Processing ◽

Image Analysis ◽

Numerical Experiments ◽

Grey Level ◽

Major Drawback ◽

Topological Gradient ◽

Boundary Measurements ◽

Topological Asymptotic Expansion ◽

Gradient Approach ◽

Segmentation Problem

Image analysis by topological gradient approach is a technique based upon the historic application of the topological asymptotic expansion to crack localization problem from boundary measurements. This paper aims at reviewing this methodology through various applications in image processing; in particular image restoration with edge detection, classification and segmentation problems for both grey level and color images is presented in this work. The numerical experiments show the efficiency of the topological gradient approach for modelling and solving different image analysis problems. However, the topological gradient approach presents a major drawback: the identified edges are not connected and then the results obtained particularly for the segmentation problem can be degraded. To overcome this inconvenience, we propose an alternative solution by combining the topological gradient approach with the watershed technique. The numerical results obtained using the coupled method are very interesting.

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