scholarly journals A Wear Debris Segmentation Method for Direct Reflection Online Visual Ferrography

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 723 ◽  
Author(s):  
Song Feng ◽  
Guang Qiu ◽  
Jiufei Luo ◽  
Leng Han ◽  
Junhong Mao ◽  
...  
Keyword(s):  
Wear Debris ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Reflected Light ◽  
Segmentation Algorithms ◽  
Filling Method ◽  
Canny Algorithm ◽  
Optimal Segmentation ◽  
Direct Reflection ◽  
Segmentation Threshold

Wear debris in lube oil was observed using a direct reflection online visual ferrograph (OLVF) to monitor the machine running condition and judge wear failure online. The existing research has mainly concentrated on extraction of wear debris concentration and size according to ferrograms under transmitted light. Reports on the segmentation algorithm of the wear debris ferrograms under reflected light are lacking. In this paper, a wear debris segmentation algorithm based on edge detection and contour classification is proposed. The optimal segmentation threshold is obtained by an adaptive canny algorithm, and the contour classification filling method is applied to overcome the problems of excessive brightness or darkness of some wear debris that is often neglected by traditional segmentation algorithms such as the Otsu and Kittler algorithms.

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.

Comparison of Thresholding and Edge Detection Segmentation Techniques

2013 ◽  
Vol 860-863 ◽  
pp. 2783-2786
Author(s):  
Yu Bing Dong ◽  
Hai Yan Wang ◽  
Ming Jing Li
Keyword(s):  
Image Segmentation ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Experimental Results ◽  
Mean Square ◽  
Segmentation Method ◽  
Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

scholarly journals Object segmentation methods for online model acquisition to guide robotic grasping

2021 ◽  
Author(s):  
Dmitri Ignakov
Keyword(s):  
Object Segmentation ◽  
Vision System ◽  
Geometric Model ◽  
Graph Cuts ◽  
Segmentation Algorithm ◽  
Service Robots ◽  
Segmentation Method ◽  
Robotic Grasping ◽  
Model Acquisition ◽  
Segmentation Methods

A vision system is an integral component of many autonomous robots. It enables the robot to perform essential tasks such as mapping, localization, or path planning. A vision system also assists with guiding the robot's grasping and manipulation tasks. As an increased demand is placed on service robots to operate in uncontrolled environments, advanced vision systems must be created that can function effectively in visually complex and cluttered settings. This thesis presents the development of segmentation algorithms to assist in online model acquisition for guiding robotic manipulation tasks. Specifically, the focus is placed on localizing door handles to assist in robotic door opening, and on acquiring partial object models to guide robotic grasping. . First, a method for localizing a door handle of unknown geometry based on a proposed 3D segmentation method is presented. Following segmentation, localization is performed by fitting a simple box model to the segmented handle. The proposed method functions without requiring assumptions about the appearance of the handle or the door, and without a geometric model of the handle. Next, an object segmentation algorithm is developed, which combines multiple appearance (intensity and texture) and geometric (depth and curvature) cues. The algorithm is able to segment objects without utilizing any a priori appearance or geometric information in visually complex and cluttered environments. The segmentation method is based on the Conditional Random Fields (CRF) framework, and the graph cuts energy minimization technique. A simple and efficient method for initializing the proposed algorithm which overcomes graph cuts' reliance on user interaction is also developed. Finally, an improved segmentation algorithm is developed which incorporates a distance metric learning (DML) step as a means of weighing various appearance and geometric segmentation cues, allowing the method to better adapt to the available data. The improved method also models the distribution of 3D points in space as a distribution of algebraic distances from an ellipsoid fitted to the object, improving the method's ability to predict which points are likely to belong to the object or the background. Experimental validation of all methods is performed. Each method is evaluated in a realistic setting, utilizing scenarios of various complexities. Experimental results have demonstrated the effectiveness of the handle localization method, and the object segmentation methods.

scholarly journals Division of Myocardial Enzyme Reference Intervals in Population Aged 1 to <18 Years Old Based on Fisher’s Optimal Segmentation Method

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Wenjia Guo ◽  
Qi Zhou ◽  
Yanan Jia ◽  
Jiancheng Xu
Keyword(s):  
Creatine Kinase ◽  
Cardiac Development ◽  
Reference Interval ◽  
Reference Intervals ◽  
Impact Factors ◽  
Segmentation Method ◽  
Splitting Sequence ◽  
Biochemical Analyzer ◽  
Optimal Segmentation ◽  
Ordered Data

Background. Reference interval (RI) research is to make it a concise, effective, and practical diagnostic tool. This study aimed to establish sex- and age-specific RI for myocardial enzyme activity in population aged 1–<18 years old in Changchun, China. Methods. Healthy subjects (n = 6,322, 1–<18 years old) were recruited from communities and schools. Aspartate aminotransferase (AST), lactate dehydrogenase (LDH), creatine kinase (CK), and creatine kinase isoenzyme (CKMB) were measured using an automatic biochemical analyzer. Fisher’s optimal segmentation method was used to partition by including percentiles as impact factors, aiming at minimizing the sum of the squares of the total dispersion into groups as splitting sequence of ordered data. Results. AST decreased gradually and was partitioned as 1, 2∼<10 and 10∼<18 years old. LDH presented disparate descending rate among 1∼<4, 4∼<12, and 12∼<18 years old. CK stood quite stable with the same RI in all ages. CKMB began to differ at 6 years of age sexually and then remained stable during 6∼<14 years old for male while it continued to decline in female. Cardiac development was partitioned as 1∼<6, 6∼<13, and 13∼<18 years old using multiple percentiles from massive data that reflect characteristics of totality as impact factors. Conclusions. Fisher’s optimal segmentation method excelled for multidimensionality, continuity, and loop calculating as dealing with RIs for myocardial enzymes activity and cardiac development process despite limitations. In future, impact of partition on the overall interval should be delved into.

PCNN Based Welding Seam Image Segmentation Algorithm

2012 ◽  
Vol 155-156 ◽  
pp. 861-866 ◽  
Author(s):  
Bei Ji Zou ◽  
Hao Yu Zhou ◽  
Zai Liang Chen ◽  
Hao Chen ◽  
Guo Jiang Xin
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
Spatial Proximity ◽  
Variance Ratio ◽  
Segmentation Method ◽  
Welding Seam ◽  
Pulse Coupled Neural Network ◽  
Image Segmentation Algorithm

A new welding seam image segmentation method based on pulse-coupled neural network (PCNN) is presented in this paper. The method segments image by utilizing PCNN’s specific feature that the fire of one neuron can capture firing of its adjacent neurons due to their spatial proximity and intensity similarity. The method can automatically confirm the best iteration times by comparing the maximum of variance ratio and get the best segmentation results. Experimental results show that the proposed method has good performance in both results and execution efficiency.

Image Segmentation Algorithm Combined with Regularized P-M De-Noising Model and Improved Watershed Algorithm

2020 ◽  
Vol 10 (2) ◽  
pp. 515-521 ◽  
Author(s):  
Guorui Chen
Keyword(s):  
Image Segmentation ◽  
Brain Mri ◽  
Small Region ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Segmentation Algorithms ◽  
Segmentation Image ◽  
The Brain ◽  
Image Segmentation Algorithm ◽  
Mri Image

Aiming at the problems of noise sensitivity and unclear contour in existing MRI image segmentation algorithms, a segmentation method combining regularized P-M de-noising model and improved watershed algorithm is proposed. First, the brain MRI image is pre-processed to obtain a brain nuclear image. Then, the brain nuclear image is de-noised by a regularized P-M model. After that, the image is preliminarily segmented by the traditional watershed algorithm to extract the features of each small region. Finally, the small regions are merged by Fuzzy Clustering with Spatial Pattern (FCSP) to obtain the segmentation image with smooth edges. The experimental results show that the algorithm can accurately segment the gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) regions. The average AOM and ME of the segmentation results on the BrainWeb dataset reached 0.93 and 0.04, respectively.

Extracting Golden Area from Image Based on Canny Operator

2015 ◽  
Vol 731 ◽  
pp. 201-204
Author(s):  
Ying Wu ◽  
Xiu Ping Zhao ◽  
Yang Jin ◽  
Xi Zhang
Keyword(s):  
Image Segmentation ◽  
Color Space ◽  
Threshold Value ◽  
Segmentation Algorithm ◽  
Canny Operator ◽  
Small Areas ◽  
Color Separation ◽  
Lab Color Space ◽  
Canny Algorithm ◽  
Image Segmentation Algorithm

This paper researched application of Canny algorithm on the color separation of golden image , to generate a separated golden image plate base on the extraction of golden area, so as to get the effect more closer to the real metallic. Canny algorithm is based on the gray-scale image segmentation algorithm. The image is mapped from RGB to Lab color space. According to the color attributes of b, the golden target regions are extracted using Canny algorithm. But it’s difficult to get the closed target boundary outlet by Canny algorithm, so this paper modified image segmentation algorithm. Firstly, the image is filtered by Canny operator; secondly, small areas on the Canny processed image are removed by using some pre-determined threshold value.; then processed the image through using smoothing and sharping method so to make inner area of image more smooth meanwhile improving boundary sharpness. The experimental results showed that the method based on Canny operator is very suitable for golden area extraction from a image. The golden target-regions can be closed boundary outlet, which makes the golden areas are more accurate and continuous.

Research on Threshold Segmentation Algorithms

2013 ◽  
Vol 860-863 ◽  
pp. 2888-2891
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Ying Sun
Keyword(s):  
Pattern Recognition ◽  
Image Segmentation ◽  
Segmentation Method ◽  
Threshold Segmentation ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Better Than ◽  
Iterative Threshold

Thresholding is one of the critical steps in pattern recognition and has a significant effect on the upcoming steps of image application, the important objectives of thresholding are as follows, and separating objects from background, decreasing the capacity of data consequently increases speed. Various threshold segmentation methods are studied. These methods are compared by using MATLAB7.0. The qualities of image segmentation are elaborated. The results show that iterative threshold segmentation method is better than others.

A Kind of Watershed Image Segmentation Method Based on Combination Pre-Processing

2010 ◽  
Vol 121-122 ◽  
pp. 320-324
Author(s):  
Jin Xi Wang ◽  
Lin Xiang Liu ◽  
Xiu Zheng Li
Keyword(s):  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Simple Operation ◽  
Processing Steps ◽  
Segmentation Problem ◽  
Image Segmentation Algorithm

The watershed algorithm has been widely used in image segmentation for its characteristics of accurately positioning edge, simple operation and etc. But it also has drawbacks of easy to over-segmentation and loss important outline for the character of sensitive to noise. Aiming at the problem of over-segmentation of watershed algorithm, the paper brought out an improved image segmentation algorithm based on watershed, which can limit the number of existing regions that are allowed with combination pre-processing steps, so that the over-segmentation problem can be better solved. The result of experiment also verifies the correctness and feasibility of the proposed algorithm in the paper.

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