Research of glass defects detection based on DFT and optimal threshold method

2012 ◽  
Author(s):  
Hongxi Zhang ◽  
Zhenduo Guo ◽  
Zegang Qi ◽  
Jiuge Wang
Keyword(s):  
Optimal Threshold ◽  
Threshold Method

Segmentation of LED Pixel Matrix by Using Optimal Threshold Method with Area Constraint

2012 ◽  
Vol 27 (5) ◽  
pp. 653-657
Author(s):  
于洪春 YU Hong-chun ◽  
邓意成 DENG Yi-cheng ◽  
郑喜凤 ZHENG Xi-feng
Keyword(s):  
Optimal Threshold ◽  
Threshold Method ◽  
Pixel Matrix

Image Segmentation Method for Wheel Set Online Measurement

2010 ◽  
Vol 44-47 ◽  
pp. 3854-3858
Author(s):  
Kai Hua Wu ◽  
Tao Ban
Keyword(s):  
Image Segmentation ◽  
Measurement Accuracy ◽  
Image Acquisition ◽  
Measuring Method ◽  
Optimal Threshold ◽  
Online Measurement ◽  
Threshold Method ◽  
Iterative Analysis ◽  
Entropy Criterion ◽  
Image Segmentation Algorithm

Wheel set is the major running components of a train. Online measurement of wheel set wear parameters is important for the safety of train. The acquisition and processing of wheel set profile image is the key problem in an online measuring method based on machine vision. Factors influencing the effect of image acquisition were analyzed. The main factors included environmental illumination, light reflection, CCD exposure time and light source change. An optimal threshold method based on entropy criterion and genetic algorithm for image threshold segmentation was proposed. The optimal threshold was found by iterative analysis. The image segmentation algorithm eliminated effectively the interferences in the image acquisition and extracted wheel set profile curve from varying background. Better segmentation effect ensured the measurement accuracy of wheel set wear parameters.

Water Film Detection in Water Distribution Test of PCCS

2013 ◽  
Vol 385-386 ◽  
pp. 663-667
Author(s):  
Xiao Feng Li
Keyword(s):  
Water Distribution ◽  
Cooling System ◽  
Motion Segmentation ◽  
Water Film ◽  
Optimal Threshold ◽  
Segmentation Method ◽  
Threshold Method ◽  
Illumination Change ◽  
Complex Background

In the passive containment cooling system (PCCS), water distribution tests are essential to verify the water distribution devices performance. Without regular boundaries and homogeneous intensities, images of water film acquired from these tests are hard to be detected by conventional approaches. We propose an improved segmentation method to identify the water film areas from the complex background. Considering the gray distortion resulted from asymmetric illumination, the method combines the modified motion segmentation and optimal threshold method. Detection results show that this method is hardly affected by the illumination change, and also insensitive to noise.

scholarly journals An Infrared Image Target Segmentation Based on Improved Threshold Method

2021 ◽  
Vol 15 ◽  
pp. 820-828
Author(s):  
Manzeng Ma ◽  
Dan Liu ◽  
Ruirui Zhang
Keyword(s):  
Image Segmentation ◽  
Target Detection ◽  
Infrared Imaging ◽  
Infrared Image ◽  
Operation Time ◽  
Optimal Threshold ◽  
Threshold Method ◽  
Intelligent Algorithms ◽  
Infrared Image Segmentation ◽  
Detection And Recognition

In recent years, infrared images have been applied in more and more extensive fields and the current research of infrared image segmentation and recognition can’t satisfy the needs of practical engineering applications. The interference of various factors on infrared detectors result in the targets detected presenting the targets of low contrast, low signal-to-noise ratio (SNR) and fuzzy edges on the infrared image, thus increasing the difficulty of target detection and recognition; therefore, it is the key point to segment the target in an accurate and complete manner when it comes to infrared target detection and recognition and it has great importance and practical value to make in-depth research in this respect. Intelligent algorithms have paved a new way for infrared image segmentation. To achieve target detection, segmentation, recognition and tracking with infrared imaging infrared thermography technology mainly analyzes such features as the grayscale, location and contour information of both background and target of infrared image, segments the target from the background with the help of various tools, extracts the corresponding target features and then proceeds recognition and tracking. To seek the optimal threshold of an image can be seen as to find the optimum value of a confinement problem. As to seek the threshold requires much computation, to seek the threshold through intelligent algorithms is more accurate. This paper proposes an automatic segmentation method for infrared target image based on differential evolution (DE) algorithm and OTSU. This proposed method not only takes into consideration the grayscale information of the image, but also pays attention to the relevant information of neighborhood space to facilitate more accurate image segmentation. After determining the scope of the optimal threshold, it integrates DE’s ability of globally searching the optimal solution. This method can lower the operation time and improve the segmentation efficiency. The simulation experiment proves that this method is very effective.

scholarly journals Optimization of Brain Tumor MR Image Classification Accuracy Using Optimal Threshold, PCA and Training ANFIS with Different Repetitions

2018 ◽  
Author(s):  
M J Tahmasebi Birgani ◽  
N Chegeni ◽  
F Farhadi Birgani ◽  
D Fatehi ◽  
Gh Akbarizadeh ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Classification Accuracy ◽  
Fuzzy Inference ◽  
Principal Component ◽  
Feature Reduction ◽  
Optimal Threshold ◽  
Threshold Method ◽  
Inference System ◽  
Efficient Treatment ◽  
And Training

Background: One of the leading causes of death is brain tumors. Accurate tumor classification leads to appropriate decision making and providing the most efficient treatment to the patients. This study aims to optimize brain tumor MR images classification accuracy using optimal threshold, PCA and training Adaptive Neuro Fuzzy Inference System (ANFIS) with different repetitions.Material and Methods: The procedure used in this study consists of five steps: (1) T1, T2 weighted images collection, (2) tumor separation with different threshold levels, (3) feature extraction, (4) presence and absence of feature reduction applying principal component analysis (PCA) and (5) ANFIS classification with 0, 20 and 200 training repetitions. Results: ANFIS accuracy was 40%, 80% and 97% for all features and 97%, 98.5% and 100% for the 6 selected features by PCA in 0, 20 and 200 training repetitions, respectively.Conclusion: The findings of the present study demonstrated that accuracy can be raised up to 100% by using an optimized threshold method, PCA and increasing training repetitions.

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