Image segmentation based on gray level and local relative entropy two dimensional histogram

Spatial correlation information between pixels is considered to be very important in thresholding methods. However, it is often ignored and thus unsatisfied segmentation results maybe obtained. To overcome this shortcoming, we propose a new image segmentation approach by taking not only pixels’ spatial information but also pixels’s gray level into account. First, a non-local mean filter is imposed on the image. Then the filtered image and the original image together are adopted to build a two dimensional histogram, it is called non-local mean two dimensional histogram. Finally, a minimum relative entropy criteria is used to select the ideal thresholding vector. Since the non-local mean filter process is performed in a neighborhood of current pixel, it carries out the spatial information of current pixel. Segmentation results on several images illustrate the effectiveness of the proposed thresholding method, whose segmentation accuracy are greatly improved compared to most existing thresholding methods.

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Thresholding based on Grey Levels, Gradient Magnitude and Spatial Correlation

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d1117.029420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 89-93

Keyword(s):

Image Segmentation ◽

Spatial Correlation ◽

Real World ◽

Experimental Results ◽

Gray Level ◽

Two Dimensional ◽

Grey Level ◽

Gradient Magnitude ◽

Spacial Information

Image segmentation gained significant importance in recent years. The goal of segmentation is partitioning an image into distinct regions containing each pixel with similar attributes. Several Image segmentation techniques exist based on thresholding and clustering. Image segmentation based on thresholding is typically doesn’t find any objects and bounds (lines, curves, etc.) in image. To boost the segmentation performance based on thresholding strategies, a unique strategy that integrates the spacial information between pixel’s is designed. The proposed strategy utilizes pixel’s grey level Gradient magnitude and gray level spacial correlation at intervals a part to construct a unique two dimensional bar graph, known as GLGM & GLSC. This technique is valid through segmenting many real world pictures. Experimental results proved this method outperforms several existing Thresholding strategies.

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Combining Global Probability Density Difference and Local Gray Level Fitting for Ultrasound Image Segmentation

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2010.00951 ◽

2010 ◽

Vol 36 (7) ◽

pp. 951-959 ◽

Cited By ~ 2

Author(s):

Bo LIU ◽

Jian-Hua HUANG ◽

Xiang-Long TANG ◽

Jia-Feng LIU ◽

Ying-Tao ZHANG

Keyword(s):

Image Segmentation ◽

Probability Density ◽

Ultrasound Image ◽

Density Difference ◽

Gray Level ◽

Ultrasound Image Segmentation

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An Image Segmentation Method Based on Two-Dimensional Entropy and Chaotic Lightning Attachment Procedure Optimization Algorithm

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420540300 ◽

2020 ◽

Vol 34 (11) ◽

pp. 2054030 ◽

Cited By ~ 1

Author(s):

Wei Liu ◽

Shuai Yang ◽

Zhiwei Ye ◽

Qian Huang ◽

Yongkun Huang

Keyword(s):

Image Segmentation ◽

Maximum Entropy ◽

Optimization Algorithm ◽

Search Algorithm ◽

Computational Time ◽

Two Dimensional ◽

Threshold Segmentation ◽

Grasshopper Optimization Algorithm ◽

Grasshopper Optimization ◽

Competitive Algorithms

Threshold segmentation has been widely used in recent years due to its simplicity and efficiency. The method of segmenting images by the two-dimensional maximum entropy is a species of the useful technique of threshold segmentation. However, the efficiency and stability of this technique are still not ideal and the traditional search algorithm cannot meet the needs of engineering problems. To mitigate the above problem, swarm intelligent optimization algorithms have been employed in this field for searching the optimal threshold vector. An effective technique of lightning attachment procedure optimization (LAPO) algorithm based on a two-dimensional maximum entropy criterion is offered in this paper, and besides, a chaotic strategy is embedded into LAPO to develop a new algorithm named CLAPO. In order to confirm the benefits of the method proposed in this paper, the other seven kinds of competitive algorithms, such as Ant–lion Optimizer (ALO) and Grasshopper Optimization Algorithm (GOA), are compared. Experiments are conducted on four different kinds of images and the simulation results are presented in several indexes (such as computational time, maximum fitness, average fitness, variance of fitness and other indexes) at different threshold levels for each test image. By scrutinizing the results of the experiment, the superiority of the introduced method is demonstrated, which can meet the needs of image segmentation excellently.

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Image Segmentation Using Rough Set Theory

International Journal of Rough Sets and Data Analysis ◽

10.4018/ijrsda.2014070105 ◽

2014 ◽

Vol 1 (2) ◽

pp. 62-74 ◽

Cited By ~ 43

Author(s):

Payel Roy ◽

Srijan Goswami ◽

Sayan Chakraborty ◽

Ahmad Taher Azar ◽

Nilanjan Dey

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Gray Level ◽

Rule Generation ◽

Local Minima ◽

Fuzzy Correlation ◽

Segmentation Methods

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

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IMAGES CONVERTER OF GEOLOGIC- LITHOLOGICAL PROFILES

NEWS of National Academy of Sciences of the Republic of Kazakhstan ◽

10.32014/2021.2518-170x.17 ◽

2021 ◽

Vol 445 (1) ◽

pp. 121-126

Author(s):

D. B. Nurseitov ◽

N. A. Toiganbayeva ◽

M. O. Kenzhebayeva

Keyword(s):

Image Segmentation ◽

Distribution Density ◽

Graphical Representation ◽

Oil Field ◽

Object Oriented Programming ◽

Two Dimensional ◽

Dimensional Array ◽

Digital Format ◽

The Matrix ◽

Oriented Programming Language

The article is devoted to the program "Converter", which allows you to translate the geologic-lithological profile of a mineral field into a digital format in the form of a two-dimensional array. The object-oriented programming language Python was used to write the program. The NumPy, OpenCV, and MatPlotlib libraries are actively used. The implementation of this program is based on image segmentation and finding the prevailing colors in the OpenCV library. Image segmentation is a preliminary step in image processing. The obtained values allow you to find out the density distribution in the area under consideration. The program "Converter" has a good graphical representation of the results obtained using the MatPlotlib library. The program writes the final converted result as a two-dimensional array to a text file along the desired path. Thus, the matrix is easy to read for further use in conjunction with other programs. The purpose of this work was to create a program that converts the geologic-lithological profile of the field into a digital format in the form of a two-dimensional array, for further use of this matrix as the distribution density of the oil field. The "Converter" program converts any geologic-lithological profile into a two-dimensional array in a matter of minutes.

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A Lightweight Dynamic Binarization Method for 2D Barcode Scanning Terminal

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.130-134.4079 ◽

2011 ◽

Vol 130-134 ◽

pp. 4079-4083

Author(s):

Jia Jia Li ◽

Ke Liang Zhang ◽

Gang Wei ◽

Bai Feng Wu

Keyword(s):

Density Ratio ◽

Recognition System ◽

Gray Level ◽

Two Dimensional ◽

2D Barcode ◽

Space Cost ◽

Binarization Method ◽

Local Average ◽

Number Of Iterations ◽

Average Gray Level

It is a difficult task to binarize image under uneven illumination, and this problem is always met in the image recognition system, such as two-dimensional barcode scanning terminal. In this paper, an efficient approach is proposed to binarize image which can tolerant uneven illumination and different light intensity. The method initializes thresholds with local average gray level and adjusts thresholds by calculating light density ratio. Due to characteristic of our approach, it can even obtain a sound result by limiting number of iterations which will seriously reduce computations and space cost. According to experiments, we can find that our method can achieve a good performance and meet the real-time requirement and quality demand for barcode scanning terminal.

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