An Improved Entropy Function and Chaos Optimization Based Scheme for Two-Dimensional Entropic Image Segmentation

2007 ◽  
pp. 991-1000
Author(s):  
Cheng Ma ◽  
Chengshun Jiang
Keyword(s):  
Image Segmentation ◽  
Entropy Function ◽  
Two Dimensional ◽  
Chaos Optimization

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

2020 ◽  
Vol 34 (11) ◽  
pp. 2054030 ◽  
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.

scholarly journals IMAGES CONVERTER OF GEOLOGIC- LITHOLOGICAL PROFILES

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.

Thresholding Selection Based on Fuzzy Entropy and Bee Colony Algorithm for Image Segmentation

2013 ◽  
pp. 165-183
Author(s):  
Yonghao Xiao ◽  
Weiyu Yu ◽  
Jing Tian
Keyword(s):  
Image Segmentation ◽  
Fuzzy Entropy ◽  
Single Parameter ◽  
Entropy Function ◽  
Experimental Results ◽  
Superior Performance ◽  
Image Thresholding ◽  
Minimum Function ◽  
Minimum Value ◽  
Bee Colony

Image thresholding segmentation based on Bee Colony Algorithm (BCA) and fuzzy entropy is presented in this chapter. The fuzzy entropy function is simplified with single parameter. The BCA is applied to search the minimum value of the fuzzy entropy function. According to the minimum function value, the optimal image threshold is obtained. Experimental results are provided to demonstrate the superior performance of the proposed approach.

Image Segmentation Research Based on GA and Improved Otsu Algorithm

2017 ◽  
Vol 7 (2) ◽  
pp. 533
Author(s):  
Xiaoqun Qin
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Two Dimensional ◽  
One Dimensional ◽  
Otsu Method ◽  
Segmented Object ◽  
The Face ◽  
Image Segmentation Algorithm ◽  
Small Dispersion ◽  
Better Than

<p>In the face of the problem of high complexity of two-dimensional Otsu adaptive threshold algorithm, a new fast and effective Otsu image segmentation algorithm is proposed based on genetic algorithm. This algorithm replaces the segmentation threshold of the traditional two - dimensional Otsu method by finding the threshold of two one-dimensional Otsu method, it reduces the computational complexity of the partition from O (L4) to O (L). In order to ensure the integrity of the segmented object, the algorithm introduces the concept of small dispersion in class, and the automatic optimization of parameters are achieved by genetic algorithm. Theoretical analysis and experimental results show that the algorithm is not only better than the original two-dimensional Otsu algorithm, but also it has better segmentation effect.</p>

scholarly journals Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 827 ◽  
Author(s):  
Chundi Jiang ◽  
Wei Yang ◽  
Yu Guo ◽  
Fei Wu ◽  
Yinggan Tang
Keyword(s):  
Image Segmentation ◽  
Relative Entropy ◽  
Spatial Information ◽  
Two Dimensional ◽  
Nonlocal Means ◽  
Mean Filter ◽  
Filter Process ◽  
Local Mean ◽  
Non Local ◽  
Correlation Information

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.

Sign in / Sign up

Export Citation Format

Share Document