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.

Experimental Study of Human Fingerprint Image Recognition Analysis

2014 ◽  
Vol 971-973 ◽  
pp. 1616-1619
Author(s):  
Su Ling Zhang
Keyword(s):  
Image Processing ◽  
Experimental Study ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Image Recognition ◽  
Fingerprint Image ◽  
Image Preprocessing ◽  
Advantages And Disadvantages ◽  
Demand Pattern ◽  
Image Segmentation Algorithm

respectively cited the fingerprint image preprocessing for image segmentation , demand pattern, image enhancement and binarization of several algorithms , and each algorithm were compared. Image segmentation algorithm studied in this paper , image enhancement algorithms, can be very good to complete the project requirements. Because each method has its advantages and disadvantages , and therefore use different methods to get different results after image processing .

scholarly journals Detection of lung Cancer on CT Scan Using Image Processing Techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 1-5
Author(s):  
Osman Mudathir ◽  
Alaa Elfadel Kamil ◽  
Suha Salah ◽  
Marwa Gamar ◽  
Zeinab Nouraldaem
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Ct Scan ◽  
Image Enhancement ◽  
Image Processing Techniques ◽  
Important Stage ◽  
Processing Techniques

This paper represents detection of lung cancer using image processing which is followed by image enhancement using three filters. These filters are Gabor, madian and mean filters. Then, image segmentation is applied using a technique called marker controlled watershed with masking that has advantages over other methods in terms of reducing the time needed for detection. On that ground, this method rejoiced with better quality. Finally, an important stage is made to decide whether the lung is infected with cancer or not this stage is called feature extraction .therefore, results were reached with less human efforts.

scholarly journals Plant Diseases Recognition Based on Image Processing Technology

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Guiling Sun ◽  
Xinglong Jia ◽  
Tianyu Geng
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Image Recognition ◽  
Color Image ◽  
Regional Growth ◽  
Recognition System ◽  
Plant Diseases ◽  
Image Feature

A new image recognition system based on multiple linear regression is proposed. Particularly, there are a number of innovations in image segmentation and recognition system. In image segmentation, an improved histogram segmentation method which can calculate threshold automatically and accurately is proposed. Meanwhile, the regional growth method and true color image processing are combined with this system to improve the accuracy and intelligence. While creating the recognition system, multiple linear regression and image feature extraction are utilized. After evaluating the results of different image training libraries, the system is proved to have effective image recognition ability, high precision, and reliability.

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 An Extensive Review on Image Segmentation Techniques

2021 ◽  
Vol 1 (2) ◽  
pp. 1-5
Author(s):  
Kamlesh Sharma ◽  
Nidhi Garg
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Digital Images ◽  
Extensive Review ◽  
Current Image ◽  
Segmentation Image

Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

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.

Development of Inspection System for IC Lead Frame Defects

1993 ◽  
Vol 5 (4) ◽  
pp. 369-373
Author(s):  
Hirokazu Tsuji ◽  
◽  
Kazuo Maruyama
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Pattern Matching ◽  
Processing System ◽  
Local Feature ◽  
Image Processing Technique ◽  
Inspection System ◽  
Lead Frame ◽  
Feature Extraction Method ◽  
Local Feature Extraction

A visual inspection system for IC lead frame defects based on the image processing technique is developed, and its inspection algorithm for the practical use is discussed. The inspection system consists of an image input system using a microscope and a TV camera, an image processing system, and a lead frame carrier. The inspection algorithm to recognize defects consists of pattern matching and local feature extraction. In the pattern matching method, minimum recognition size of defects is limited to the positioning error of the lead frame; therefore, small defects are recognized by the local feature extraction method in which local irregular patterns are detected using logical filter.

scholarly journals An Extensive Review on Image Segmentation Techniques

2021 ◽  
pp. 1-5
Author(s):  
Dr. Kamlesh Sharma ◽  
◽  
Nidhi Garg ◽  
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Digital Images ◽  
Extensive Review ◽  
Current Image ◽  
Segmentation Image

Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

scholarly journals Texture classification using gene expression programming

2021 ◽  
Author(s):  
Fereshteh Mahvarsayyad
Keyword(s):  
Gene Expression ◽  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Texture Classification ◽  
Gene Expression Programming ◽  
Simple Arithmetic ◽  
Image Characteristics ◽  
Processing Step

In computer vision, segmentation refers to the process of subdividing a digital image into constituent regions with homogeneity in some image characteristics. Image segmentation is considered as a pre-processing step for object recognition. The problem of segmentation, being one of the most difficult tasks in image processing, gets more complicated in the presence of random textures in the image. This paper focuses on texture classification, which is defined as supervised texture segmentation with prior knowledge of textures in the image. We investigate a classification method using Gene Expression Programming (GEP). It is shown that GEP is capable of evolving accurate classifiers using simple arithmetic operations and direct pixel values without employing complicated feature extraction algorithms. It is also shown that the accuracy of classification is related to the fact that GEP can detect the regularities of texture patterns. As part of this project, we implemented a Photoshop plug-in that uses the evolved classifiers to identify and select target textures in digital images.

scholarly journals Slope Collapse Detection Based on Image Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin-Dai An ◽  
Xiang-Wen Xie ◽  
Di Wu ◽  
Ke-Feng Song
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Recognition ◽  
Motion Detection ◽  
Detection Algorithm ◽  
Image Feature ◽  
Moving Targets ◽  
Image Feature Extraction ◽  
Slope Collapse ◽  
River Embankment

In this paper, we study a task of slope collapse detection (SCD) for river embankment and formulate it as the tasks of motion detection and image recognition. Specifically, we introduce an SCD method based on motion detection and image recognition technologies to help inspector attendants detect the slope collapse. In this method, we use the foreground motion detection algorithm to identify the slope collapse of the scene of the river embankment. Since the moving targets in the foreground may not only be the slope collapse but also maybe some biology, we further use the image feature extraction and image recognition technology to recognize the foreground motion area, thus eliminating the influence of the biology on the detection results. Experimental results on the relevant scene data show that the proposed method can identify the slope collapse in real-time, and can effectively eliminate the motion interference of the biology, which has a high practical value.

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