Image Processing of the Hook on the Catenary Based on Machine Vision

2011 ◽  
Vol 480-481 ◽  
pp. 1028-1032 ◽  
Author(s):  
Jing Yu ◽  
Chang Chun Li ◽  
Shi Feng Wang ◽  
Hua Guan Liu
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Edge Detection ◽  
Scale Transformation ◽  
Gray Scale ◽  
Image Edge ◽  
Loading And Unloading ◽  
Prewitt Edge Detection

In this paper, the idea loading and unloading workpiece on catenary based on machine vision has been proposed, for the feature of the hook on catenary, we first used gray-scale transformation, used a variety of edge detection operators to detect the image edge, then combine prewitt edge detection operator and Otsu to segment the hook image, the result shows that this method can extract the image edge of hook effectively, lay a good foundation for loading and unloading workpiece automatically.

Study on Image Processing of Wheel Valve Hole Based on Machine Vision

2011 ◽  
Vol 63-64 ◽  
pp. 541-546 ◽  
Author(s):  
Chang Chun Li ◽  
Shi Feng Wang ◽  
Jing Yu ◽  
Hua Guan Liu
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Machine Vision ◽  
Edge Detection ◽  
Basic Principle ◽  
Image Acquisition ◽  
Wheel Speed ◽  
Hole Edge

This paper discusses the basic principle for automatic searching the wheel valve hole based on machine vision. Image acquisition and image processing have been done, and we analyzed the factors that impact the image quality of wheel valve hole. This paper argues that many parameters such as the wheel speed, painting color, the distance between the camera and the valve hole, edge detection operator, and they will affect the quality of the image acquisition and image processing of valve hole.

Comparison and Evaluation of Edge Detection Segmentation Techniques

2014 ◽  
Vol 889-890 ◽  
pp. 1069-1072
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Hai Yan Wang
Keyword(s):  
Image Processing ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Basic Problem ◽  
Mean Square ◽  
Detection Techniques ◽  
Image Edge Detection ◽  
Image Edge

Edge detection is the basic problem in the field of image processing. Various image edge detection techniques are introduced. Using various edge detection techniques different images are analyzed and compared by MATLAB7.0. In order to evaluate the effect of edge segmentation, the root mean square error is used. The experimental results show that no an edge detection technique works well for all types of images.

Method of Recognizing of Chip Shape Based on Machine Vision Technology

2011 ◽  
Vol 188 ◽  
pp. 158-161 ◽  
Author(s):  
K.E. Qing ◽  
Yi Wen Wang ◽  
Li Jia Liu ◽  
Z.R. Zhang ◽  
Z.Q. Yu
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Edge Detection ◽  
Euler Number ◽  
Local Maximum ◽  
Machining Process ◽  
Chip Shape ◽  
Shape Monitoring

According to the problem of chip shape monitoring in machining process, this paper presents a methods of the recognition of shape based on machine vision technology. First make a image processing such as binary processing、opening operation、expansion treatment and edge detection to the image of chip by researching the features of the image of the typical chip which are C-shape chip、long spiral chip、random chip and zonal chip. Then automatically identifying the chip shape by the features that euler number and number of curvature local maximum. The experiment proved this methods feasible and effectively to identifying the chip shape and classification by the features of image of chip.

Large NC Machining Fast Knife Mould Based on Image Technology

2011 ◽  
Vol 188 ◽  
pp. 613-616
Author(s):  
Xian Li Liu ◽  
Xiao Ran Song ◽  
L.J. Liu ◽  
Zhong Yang Zhao ◽  
D.L. Ma
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Nc Machining ◽  
Machining Accuracy ◽  
Thinning Algorithm ◽  
Image Edge Detection ◽  
Image Edge ◽  
Characteristics Of Image ◽  
Small Parts

In NC machining large moulds, second mold will install deviated orientation problem. Based on image technology, after the second for installation of workpiece, collected clip in the concrete pins on measuring and calculating the image processing, analysis, and finally got the localization generated during installation and adjustment, the deviation of the machine to eliminate biases. In image processing of PSCP thinning algorithm, based on the characteristics of image edge detection were analyzed, the extraction and processing, improve the machining accuracy and efficiency. This method can also be used in small parts processing detection, etc.

scholarly journals Comparative Analysis of Image Quality Values on Edge Detection Methods

2020 ◽  
Vol 4 (2) ◽  
pp. 345-351
Author(s):  
Wicaksono Yuli Sulistyo ◽  
Imam Riadi ◽  
Anton Yudhana
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Edge Detection ◽  
Computer Technology ◽  
Detection Method ◽  
Detection Methods ◽  
Sobel Operator ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method

Identification of object boundaries in a digital image is developing rapidly in line with advances in computer technology for image processing. Edge detection becomes important because humans in recognizing the object of an image will pay attention to the edges contained in the image. Edge detection of an image is done because the edge of the object in the image contains very important information, the information obtained can be either size or shape. The edge detection method used in this study is Sobel operator, Prewitt operator, Laplace operator, Laplacian of Gaussian (LoG) operator and Kirsch operator which are compared and analyzed in the five methods. The results of the comparison show that the clear margins are the Sobel, Prewitt and Kirsch operators, with PSNR calculations that produce values ​​above 30 dB. Laplace and LoG operators only have an average PSNR value below 30 dB. Other quality comparisons use the histogram value and the contrast value with the highest value results in the Laplace and LoG operators with an average histogram value of 110 and a contrast value of 24. The lowest histogram and contrast value are owned by the Sobel and Prewitt operators.  

scholarly journals Algorithmic Approaches For Image Edge Detection

2021 ◽  
Vol 10 (3) ◽  
pp. 1942-1948
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Image Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Edge Detection ◽  
Image Edge ◽  
Algorithmic Approaches

Edge detection is most important technique in digital image processing. It play an important role in image segmentation and many other applications. Edge detection providesfoundation to many medical and military applications.It difficult to generate a generic code for edge detection so many kinds ofalgorithms are available. In this article 4 different approaches Global image enhancement with addition (GIEA), Global image enhancement with Multiplication (GIEM),Without Global image enhancement with Addition (WOGIEA),and without Global image enhancement with Multiplication (WOGIEM)for edge detection is proposed. These algorithms are validatedon 9 different images. The results showthat GIEA give us more accurate results as compare to other techniques.

scholarly journals Optimization in Edge Detection using Ant Colony Optimization

2019 ◽  
Vol 8 (3) ◽  
pp. 8167-8170
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Ant Colony Optimization ◽  
Computation Time ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Detection Techniques ◽  
Medical Security ◽  
Satellite Applications ◽  
Image Edge

Image processing is now emerged in different fields like medical, security and surveillance, remote sensing & satellite applications and much more. Image processing includes different operations such as feature extraction, object detection and recognition, X-ray scanning etc. All such operations required edge detection to get better quality image. Edge detection is performed to distinguish different objects in an image by finding the boundaries or edges between them. Edges are used to isolate particular objects from their background as well as to recognize or classify objects. In this paper, comparison of various edge detection techniques such as Sobel, Prewitt, Roberts, Canny, LoG and Ant Colony Optimization Algorithm is given. Ant colony Optimization(ACO) use parallelism which reduces the computation time as size of an image increases.

scholarly journals Comparison of Adaptive Ant Colony Optimization for Image Edge Detection of Leaves Bone Structure

2018 ◽  
Vol 6 (2) ◽  
pp. 328-336 ◽  
Author(s):  
Febri Liantoni ◽  
Rifki Indra Perwira ◽  
Daniel Silli Bataona
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Ant Colony Optimization ◽  
Digital Image Processing ◽  
Optimization Algorithm ◽  
Bone Structure ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Image Edge Detection ◽  
Image Edge

Leaf bone structure has a characteristic that can be used as a reference in digital image processing. One form of digital image processing is image edge detection. Edge detection is the process of extracting edge information from an image. In this research, Adaptive Ant Colony Optimization algorithm is proposed for edge image detection of leaf bone structure. The Adaptive Ant Colony Optimization method is a modification of Ant Colony Optimization, in which the initial an ant dissemination process is no longer random, but it is done by a pixel placement process that allows for an edge based on the value of the image gradient. As a comparison also performed edge detection using Robert and Sobel method. Based on the experiments performed, Adaptive Ant Colony Optimization algorithm is capable of producing more detailed image edge detection and has thicker borders than others. Keywords: edge detection, ant colony optimization, robert, sobel

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