Research on Burrs Detection of Parts Surface Based on Threshold Segmentation

2014 ◽  
Vol 618 ◽  
pp. 453-457
Author(s):  
Yi Zhang ◽  
Xiao Rong Chen ◽  
Hao Lin Li ◽  
Fu Sheng Tan ◽  
Jun Fan Yan
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Production Efficiency ◽  
Cutting Process ◽  
Automatic Polishing ◽  
Solid Foundation ◽  
Production Automation ◽  
Vision Technique

Mental cutting process, a widespread process in the machining, which can produce the maximum number of burrs. Burr detection and deburring are crucially important to safe reliability of parts. In order to avoid the effects of subjective factors effectively, and improve the production efficiency and production automation, we introduced the machine vision technique. According to the universal burrs produced in the cutting process, this paper principally studied the image segmentation, burrs feature extraction, the improvement of adaptability based on digital image processing. The authors conclude that the algorithm applied in this paper can detect the burrs information effectively, laid a solid foundation for automatic polishing, with the certain practical value.

scholarly journals AN OPTIMIZED SURF-BASED FIRE AND SMOKE DETECTION SYSTEM USING IMAGE PROCESSING FROM SURVEILLANCE VIDEO.

2020 ◽  
Vol 9 (10) ◽  
pp. 86-96
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Optimization Approach ◽  
Surveillance Video ◽  
Smoke Detection ◽  
Video Frames ◽  
Optimal Feature

Over the last decades, digital image processing based fire and smoke detection have been improving steadily to provide a more accurate detection results in the area of surveillance security system. Detection of the fire and smoke from the surveillance videos is very challenging task due to the complex structural properties of the video frames or images and need improvisation in the existing work by utilization of feature selection or optimization approach to select on optimal feature according to the fire and smoke. A research based on the combination of various feature extraction techniques with feature selection approach for fire and smoke detection has been presented in this paper. In this research, we develop Fire and Smoke Detection (FSD) system using digital image processing with the concept of Speed up Robust Feature (SURF) along with the Intelligent Water Drops (IWD) as a feature selection and optimization algorithm. Here, Artificial Neural Network (ANN) is used as an Artificial Intelligence (AI) technique with that helps to select a set of optimal feature from the extracted by SURF descriptor from the video frames. By utilizing the concept of optimized ANN, the accuracy of proposed FSD system is increases in terms of detection accuracy and with minimum percentage of error. At last, the performance of the FSD system is calculated to validate the model and this shows that it is possible to use IWD with SURF as a feature extraction technique in order to detect the fire or smoke form the surveillance video with minimum error rate and the simulation results clearly show the effectiveness of proposed FSD system

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

Development of Cable Inspection System Based on Image Processing

2014 ◽  
Vol 602-605 ◽  
pp. 2199-2204
Author(s):  
Huan Liu ◽  
Chao Tao Liu
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Image Recognition ◽  
Host Computer ◽  
Inspection System ◽  
Energy Distributions ◽  
Single Motor ◽  
Image Acquisition System

A stayed cable inspection system was developed which consists of robot, host computer, cameras and image acquisition system. The robot was driven with single motor and could climb cables of various and variable diameters. Pictures of the cables’ were taken by the robot, and the defects and mars were identified automatically with image recognition. The steps of image recognition includes image de-noising, image enhancement, image segmentation, feature extraction, and recognition with the features of the images’ histogram grayscale distributions and energy distributions.

The Measurement of Small Cylindrical Pieces Based on Digital Image Processing

2012 ◽  
Vol 229-231 ◽  
pp. 1304-1307
Author(s):  
Xiao Jing Tian ◽  
Hua Jun Dong ◽  
Dong Ming Li ◽  
Xiao Bo Zhang
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Ccd Camera ◽  
Light Sources ◽  
Online Measurement ◽  
Fast Detection ◽  
Parameters Extraction ◽  
Solid Foundation ◽  
Parameters Measurement

This paper establishes a monitoring system suitable for online measurement of dimentional parametres of small cylindrical pieces. The system ,which is based on digital image processing ,consists of optical light sources, a CCD camera, PC, and MATLAB digital image processing toolbox. The image acquired by CCD is pre-processed through the procedure of image gray enhancement, image equalization, image binarization and dimentional parameters extraction interface to achieve online measurement. The smallest rectangle method is used to obtain the dimentional parameters. An automatic detection interface of dimentional parameters measurement based on MATLAB is compiled which can provide a solid foundation for the online and fast detection of dimentional parameters based on image processing technology.

FEATURE EXTRACTION BY RANK-ORDER FILTERING FOR IMAGE SEGMENTATION

1988 ◽  
Vol 02 (02) ◽  
pp. 301-319 ◽  
Author(s):  
P. ZAMPERONI
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Density Function ◽  
Rank Order ◽  
Experimental Results ◽  
Textural Features ◽  
Segmentation Method ◽  
Unified Approach

The aim of this paper is to outline a unified approach to feature extraction for segmentation purposes by means of the rank-order filtering of grey values in a neighbourhood of each pixel of a digitized image. In the first section an overview of rank-order filtering for image processing is given, and a fast histogram algorithm is proposed. Section 2 deals with the extraction of a “locally most representative grey value”, defined as the maximum of the local histogram density function. In Section 3 several textural features are described, which can be extracted from the local histogram by means of rank-order filtering, and their properties are discussed. Section 4 formulates some general requirements to be met by the process of image segmentation, and describes a method based upon the features introduced in the former sections. In the last section some experimental results applied to aerial views obtained with the segmentation method of Sect. 4 are reported. These test images have been analyzed within the scope of an investigation centered on terrain recognition for agricultural and ecological purposes.

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 Texture Detection of Aluminum Foil Based on Top-Hat Transformation and Connected Region Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Fei He ◽  
Yuxing Hu ◽  
Jian Wang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Detection Method ◽  
Aluminum Foil ◽  
Quantitative Detection ◽  
Test Results ◽  
Detection Time ◽  
Texture Detection ◽  
Better Than

A new method of texture detection for aluminum foil based on digital image processing technology is proposed. Top-hat transformation and image segmentation technology based on the connected domain are used to change the method of determining texture fraction by using human experience. Compared with the brightness method, pit detection method, and EBSD technology, this method can complete quantitative detection efficiently, automatically, and accurately, and reduce the detection time and manpower. It eliminates the instability of manual detection and ensures the accuracy of detection. By this method, the error of test results can be controlled within 1.6%, which is much better than 7.3% of the brightness method and 4% of the pitting method. It provides more accurate test results for the production process control of aluminum foil.

Study of Various Image Segmentation Methodologies

2017 ◽  
pp. 1-27
Author(s):  
Abahan Sarkar ◽  
Ram Kumar
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
New Technologies ◽  
Region Of Interest ◽  
Literature Survey ◽  
Segmentation Algorithms ◽  
Segmentation Image

In day-to-day life, new technologies are emerging in the field of Image processing, especially in the domain of segmentation. Image segmentation is the most important part in digital image processing. Segmentation is nothing but a portion of any image and object. In image segmentation, the digital image is divided into multiple set of pixels. Image segmentation is generally required to cut out region of interest (ROI) from an image. Currently there are many different algorithms available for image segmentation. This chapter presents a brief outline of some of the most common segmentation techniques (e.g. Segmentation based on thresholding, Model based segmentation, Segmentation based on edge detection, Segmentation based on clustering, etc.,) mentioning its advantages as well as the drawbacks. The Matlab simulated results of different available image segmentation techniques are also given for better understanding of image segmentation. Simply, different image segmentation algorithms with their prospects are reviewed in this chapter to reduce the time of literature survey of the future researchers.

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