An Automatic Threshold Segmentation and Mining Optimum Credential Features by Using HSV Model

3D Research ◽  
2019 ◽  
Vol 10 (2) ◽  
Author(s):  
A. Prabhu Chakkaravarthy ◽  
A. Chandrasekar
Keyword(s):  
Threshold Segmentation

scholarly journals Counting of Pine Wood Nematode Disease Trees Based on Threshold Segmentation

2021 ◽  
Vol 1961 (1) ◽  
pp. 012033
Author(s):  
Bowen Yu ◽  
Yifan Liu ◽  
Tong Zhao
Keyword(s):  
Pine Wood Nematode ◽  
Threshold Segmentation ◽  
Pine Wood

Key algorithm based on threshold segmentation for attitude correction application in tactical training

2021 ◽  
Author(s):  
Weilan Guo ◽  
Haitao Wang ◽  
Fengyun Lu ◽  
Xin Tong ◽  
Xiaoxu Gao
Keyword(s):  
Threshold Segmentation ◽  
Tactical Training

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.

Study of Segmentation Threshold Based on Wavelet Transform

2013 ◽  
Vol 756-759 ◽  
pp. 3855-3859
Author(s):  
Jian Yi Li ◽  
Hui Juan Wang
Keyword(s):  
Wavelet Transform ◽  
Digital Image ◽  
Region Growing ◽  
Detection Methods ◽  
Mathematical Tool ◽  
Matlab Simulation ◽  
Segmentation Method ◽  
Transform Method ◽  
Threshold Segmentation ◽  
Digital Image Enhancement

Based on the research of the four kinds of algorithms of digital image segmentation, based on edge detection methods, based on region growing method, threshold segmentation method and digital image threshold segmentation method based on wavelet transform, using MATLAB simulation of all digital image enhancement and segmentation process, the obtained results are analyzed, proving the threshold segmentation wavelet transform method has unparalleled advantages in information extraction in medical image. Wavelet transform is a mathematical tool widely used in recent years, compared with the Fu Liye transform, the window of Fu Liye transform, wavelet transform is the local transform of space and frequency, it can be very effective in extracting information from the signal [[1.

The Detection Method of Lane Line Based on the Improved Otsu Threshold Segmentation

2015 ◽  
Vol 741 ◽  
pp. 354-358 ◽  
Author(s):  
Yang Shan Tang ◽  
Dao Hua Xia ◽  
Gui Yang Zhang ◽  
Li Na Ge ◽  
Xin Yang Yan
Keyword(s):  
Image Segmentation ◽  
Processing Time ◽  
Detection Method ◽  
Segmentation Algorithm ◽  
Test Results ◽  
Threshold Segmentation ◽  
Otsu Method ◽  
Time Requirements ◽  
Lane Line ◽  
Center Distance

For overcoming the shortage of Otsu method, proposed an improved Otsu threshold segmentation algorithm. On the basis of Otsu threshold segmentation algorithm, the gray level was divided into two classes according to the image segmentation, to determine the best threshold by comparing their center distance, so as to achieve peak line recognition under the condition of multiple gray levels. Then did experiments on image segmentation of the lane line with MATLAB by traditional Otsu threshold segmentation algorithm and the improved algorithm, the threshold of traditional Otsu threshold segmentation algorithm is 144 and the threshold of the improved Otsu threshold segmentation algorithm is 131, the processing time is within 0.453 s. Test results show that the white part markings appear more, the intersection place of white lines and the background is more clear, so this method can identify lane markings well and meet the real-time requirements.

Optical correlation tracking of low contrast target based on wavelet threshold segmentation

2012 ◽  
Author(s):  
Su Zhang ◽  
You Jian Wang ◽  
Qi Bo Zhang ◽  
Wen Sheng Wang
Keyword(s):  
Optical Correlation ◽  
Threshold Segmentation ◽  
Low Contrast ◽  
Correlation Tracking

Multi-threshold Segmentation Using Learning Automata

2017 ◽  
pp. 101-127 ◽  
Author(s):  
Margarita-Arimatea Díaz-Cortés ◽  
Erik Cuevas ◽  
Raúl Rojas
Keyword(s):  
Learning Automata ◽  
Threshold Segmentation
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