scholarly journals Single-threshold Image Segmentation Algorithm Based on Improved Bat Algorithm

CONVERTER ◽  
2021 ◽  
pp. 738-747
Author(s):  
Wentan Jiao, Wenqing Chen
Keyword(s):  
Image Segmentation ◽  
Fitness Function ◽  
Bat Algorithm ◽  
Local Optimum ◽  
Variance Method ◽  
Individual Fitness ◽  
Adaptive Parameter ◽  
Logistic Mapping ◽  
The Individual ◽  
Image Segmentation Algorithm

The Improved Bat Algorithm (IBA) is proposed for the image segmentation based on the maximum interclass variance method. Firstly, the principle of image segmentation based on the maximum interclass variance method is explained, and secondly, the bat algorithm is improved by using chaotic logistic mapping to initialize the population to improve the diversity of solutions, using adaptive parameter optimization to avoid falling into local optimum, using Monkey algorithm for individual selection, and finally, the image segmentation function in image segmentation is used as the individual fitness function of the bat algorithm for solving. The simulation experiments show that compared with the bat algorithm and the monkey group algorithm, this algorithm has better segmentation effect under different threshold values.

scholarly journals Ordering Strategy Analysis of Prefabricated Component Manufacturer in Construction Supply Chain

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Hongxiong Yang ◽  
Jacky K. H. Chung ◽  
Yuhong Chen ◽  
Yifan Pan ◽  
Zhiling Mei ◽  
...  
Keyword(s):  
Supply Chain ◽  
Fitness Function ◽  
Construction Material ◽  
Optimal Order ◽  
Cost Models ◽  
Inventory Cost ◽  
Strategy Analysis ◽  
Individual Fitness ◽  
Eoq Model ◽  
The Individual

Firstly, the characteristics and present situations of the prefabricated construction supply chain are analyzed; inventory cost models for construction material of every phase order, one-off order, and fractionated order are built based on traditional EOQ model and construction supply chain theory. Next, the order decision is represented in binary numbers 0 and 1, in which 0 stands for “no order” and 1 for “order.” The analysis uses the genetic algorithm, sets the objective function, and undergoes testing and assessing the individual fitness function, encoding, decoding, crossover, mutation, and selecting parameter. Moreover, inventory cost of construction supply chain is processed and optimized in Matlab. The research establishes a research paradigm on supply chain management of component manufacturing and materials supply. This study concludes the ordering strategy on construction material, identifies the optimal order points and order batches, and provides recommendations for further research.

Fuzzy Clustering Color Image Segmentation Algorithm Based on CPSO

2012 ◽  
Vol 461 ◽  
pp. 526-531
Author(s):  
Xiao Hong Zhang ◽  
Hong Mei Ning
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Segmentation Algorithm ◽  
Color Image Segmentation ◽  
Local Optimum ◽  
Fcm Clustering ◽  
Clustering Center ◽  
Membership Matrix ◽  
Image Segmentation Algorithm

Fuzzy C-mean algorithm (FCM) has been well used in the field of color image segmentation. But it is sensitive to initial clustering center and membership matrix, and likely converges into the local minimum, which causes the quality of image segmentation lower. By use of the properties-ergodicity, randomicity of chaos, a new image segmentation algorithm is proposed, which combines the chaos particle swarm optimization (CPSO) and FCM clustering. Some experimental results are shown that this method not only has the ability to prevent the particles to convergence to local optimum, but also has faster convergence and higher accuracy for segmentation. Using the feature distance instead of Euclidian distance, robustness of this method is enhanced.

scholarly journals Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

2019 ◽  
Vol 11 (12) ◽  
pp. 1421 ◽  
Author(s):  
Heming Jia ◽  
Chunbo Lang ◽  
Diego Oliva ◽  
Wenlong Song ◽  
Xiaoxu Peng
Keyword(s):  
Image Segmentation ◽  
Oil Pollution ◽  
Fitness Function ◽  
Statistical Tests ◽  
Satellite Image ◽  
Dynamic Control ◽  
Engineering Problem ◽  
Function Evaluation ◽  
Local Optimum ◽  
Mutation Mechanism

In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability. To evaluate the performance of the proposed method, a series of experiments are carried out on various satellite images. Eight advanced thresholding approaches are selected for comparison. Three criteria are adopted to determine the segmentation thresholds, namely Kapur’s entropy, Tsallis entropy, and Otsu between-class variance. Furthermore, four oil pollution images are used to further assess the practicality and feasibility of the proposed method on real engineering problem. The experimental results illustrate that the DHHO/M based thresholding technique is superior to others in the following three aspects: fitness function evaluation, image segmentation effect, and statistical tests.

scholarly journals An improved image segmentation algorithm based on the maximum class variance method

2020 ◽  
Vol 309 ◽  
pp. 03029
Author(s):  
Qianhui Qi ◽  
Yimin Tian ◽  
Lili Han
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Optimization Theory ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Variance Method ◽  
Optimal Segmentation ◽  
Image Segmentation Algorithm ◽  
Cuckoo Algorithm

Image segmentation is an important part of image processing. The result of image segmentation directly affects the effect of subsequent image processing. However the efficiency of the traditional maximum class variance method is low. This paper uses the cuckoo algorithm to optimize the traditional maximum class variance method to achieve a better segmentation effect. This image segmentation method combined with optimization theory can achieve the purpose of finding the optimal segmentation.

scholarly journals A smoke image segmentation algorithm based on rough set and region growing

2019 ◽  
Vol 65 (No. 8) ◽  
pp. 321-329
Author(s):  
Haitao Wang ◽  
Yanli Chen
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Region Growing ◽  
Recall Rate ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Seed Point ◽  
Black Smoke ◽  
Growth Method ◽  
Image Segmentation Algorithm

Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

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