Application Research on Precision Detection of Small Gear with Composite Edge Detection Method

2013 ◽  
Vol 333-335 ◽  
pp. 1123-1128
Author(s):  
Xin Luo ◽  
Li Ming Wu ◽  
De Zhi Zeng
Keyword(s):  
Edge Detection ◽  
Real Time ◽  
High Speed ◽  
Dynamic Range ◽  
Detection Method ◽  
Detection System ◽  
Structural Characteristics ◽  
Low Cost ◽  
Edge Image ◽  
Edge Detection Method

Vision-based measurement method can be widely used for a variety of real-time and online precision measurements, and particularly well suited for dynamic real-time precision measurement of geometry parameters of the part, which has advantages of non-contact, high-speed, big dynamic range, rich amount of information, and relatively low cost. After the study of vision-based online detection system of small gear, we propose a composite subpixel edge detection method, which combines the four-way weighted differential algorithm based on the classic Sobel operator and OFMM (Orthogonal Fourier-Mellin Moment), aiming at achieving the precision location of the subpixel edge firstly. And then detect tooth profile defects rapidly through scanning circularly the edge image, according to the structural characteristics of gears. The theoretical analysis and experimental results show that the detection method has so high accuracy and speed that it can meet the industrial online tests requirements.

Polynomial Interpolation Sub-Pixel Edge Detection Method Based on Improved Canny Operator

2014 ◽  
Vol 563 ◽  
pp. 203-207
Author(s):  
Kun Lin Yu ◽  
Zhi Yu Xie
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Polynomial Interpolation ◽  
Detection Algorithm ◽  
Edge Image ◽  
Canny Operator ◽  
Edge Detection Algorithm ◽  
Canny Edge ◽  
Image Edge ◽  
Edge Detection Method

According to the shortcoming of traditional Canny edge detection algorithm is sensitive to noise and low positioning accuracy, this paper proposes an algorithm of Polynomial interpolation Sub-pixel edge detection based on improved Canny operator: We first use improved Canny operator edge detection algorithm to extract rough image edge, then use the quadratic Polynomial interpolation to calculate on the rough extraction edge, finally refine the edge image. Experiments show that the improved method is better than the traditional detection method can accurately locate the image edge.

scholarly journals A novel edge detection method based on efficient gaussian binomial filter

2021 ◽  
Vol 7 (2) ◽  
pp. 211
Author(s):  
El Houssain Ait Mansour ◽  
Francois Bretaudeau
Keyword(s):  
Edge Detection ◽  
High Speed ◽  
Detection Method ◽  
Detection Methods ◽  
Gaussian Filter ◽  
Image Edge Detection ◽  
Low Pass ◽  
Image Edge ◽  
Edge Detection Method ◽  
Low Pass Filters

Most basic and recent image edge detection methods are based on exploiting spatial high-frequency to localize efficiency the boundaries and image discontinuities. These approaches are strictly sensitive to noise, and their performance decrease with the increasing noise level. This research suggests a novel and robust approach based on a binomial Gaussian filter for edge detection. We propose a scheme-based Gaussian filter that employs low-pass filters to reduce noise and gradient image differentiation to perform edge recovering. The results presented illustrate that the proposed approach outperforms the basic method for edge detection. The global scheme may be implemented efficiently with high speed using the proposed novel binomial Gaussian filter.

Edge Detection Combined Local Gray Level Difference and Competitive Fuzzy Logic

2012 ◽  
Vol 220-223 ◽  
pp. 1284-1287
Author(s):  
Tong Tong ◽  
Yan Cai ◽  
Da Wei Sun
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Experimental Result ◽  
Gray Level ◽  
Human Visual Perception ◽  
Edge Image ◽  
New Approach ◽  
Level Difference ◽  
Edge Detection Method ◽  
Noisy Condition

In this paper, we present a new approach by local gray level difference based competitive fuzzy edge detection. In the light of human visual perception, a preprocessing step is proposed to simplify original images and further enhance the performance of edge extraction. Then we define the feature vector of each pixel in four directions and six edge prototype. Finally, BP neural network is used to classify the type of edge, and the competitive rule is adopted to thin the thick edge image. From the experimental result, it can be seen that the edge detection method proposed in this paper is superior to Canny method and Log method under the noisy condition.

Study on the Precise Detection Method of Pavement Structural Depth

2014 ◽  
Vol 1030-1032 ◽  
pp. 2274-2278
Author(s):  
Jian Feng Wang ◽  
Fan Yang
Keyword(s):  
High Speed ◽  
Detection Method ◽  
Detection System ◽  
Low Cost ◽  
High Accuracy ◽  
Engineering Practice ◽  
Important Indicator ◽  
Key Factor ◽  
Pavement Structure ◽  
Detection Principle

Pavement structural depth is an important indicator of Pavement macro-structure. So how to use better method, with low cost to obtain the high accuracy of pavement structural depth indicators become the difficulty and key factor in road detection area.In this paper we propose a new detection pavement structure depth method.mainly studies the accurate detection principle of pavement structure depth, develops pavement structure depth detection system which can be used in engineering practice with high-speed, high-accuracy. making the detection of pavement structure depth more accurate and rapid.

scholarly journals Vision-Based Edge Detection System for Fruit Recognition

2021 ◽  
Vol 2107 (1) ◽  
pp. 012066
Author(s):  
Sean Huey Tan ◽  
Chee Kiang Lam ◽  
Kamarulzaman Kamarudin ◽  
Abdul Halim Ismail ◽  
Norasmadi Abdul Rahim ◽  
...  
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Detection System ◽  
Learning Model ◽  
Support Vector ◽  
Machine Learning Model ◽  
Different Types ◽  
Canny Edge ◽  
Edge Detection Method ◽  
Edge Features

Abstract There are variety of fruits around the world, different types of fruits contain different types of nutrients and vitamins which could benefits our health. In order to understand which fruit can provide specific type of nutrients, we need to identify the types of fruits. However, fruits grow in a different shape, colour and texture based on the country they were planted and the environment of the land. Implementing a machine vision-based recognition on the fruits can help people recognize them easily. In this paper, an edge detection method is applied using computer vision approach to recognize different types of fruits. The fruits are classified based on the features extracted from their images. In the experiment, a total of 450 images of three types of fruit are used, which are apples, lemons and mangoes. Pre-processing steps are applied on the captured image to improve the quality of fruit details and the edge features are extracted using Canny Edge Detection method. Classification of the fruits is accomplished using two different types of learning model, the deep leaning model, Convolution Neural Network (CNN) and machine learning model, Support Vector Machines (SVM). The performance of both classifiers is compared and the model with the best performance, SVM is chosen as the model for the system. The system can achieve 86% classification accuracy with the SVM model, which is good enough for fruit recognition.

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