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 .

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.

One Extended OTSU Flame Image Recognition Method Using RGBL and Stripe Segmentation

2011 ◽  
Vol 121-126 ◽  
pp. 2141-2145 ◽  
Author(s):  
Wei Gang Yan ◽  
Chang Jian Wang ◽  
Jin Guo
Keyword(s):  
Image Segmentation ◽  
Water Vapor ◽  
Image Recognition ◽  
Recognition Accuracy ◽  
New Method ◽  
Recognition Method ◽  
Otsu’S Method ◽  
Flame Shape ◽  
Image Segmentation Algorithm ◽  
Rgb Image

This paper proposes a new image segmentation algorithm to detect the flame image from video in enclosed compartment. In order to avoid the contamination of soot and water vapor, this method first employs the cubic root of four color channels to transform a RGB image to a pseudo-gray one. Then the latter is divided into many small stripes (child images) and OTSU is employed to perform child image segmentation. Lastly, these processed child images are reconstructed into a whole image. A computer program using OpenCV library is developed and the new method is compared with other commonly used methods such as edge detection and normal Otsu’s method. It is found that the new method has better performance in flame image recognition accuracy and can be used to obtain flame shape from experiment video with much noise.

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.

Fingerprint Preprocessing and Recognition System Development and Design

2014 ◽  
Vol 971-973 ◽  
pp. 1897-1900
Author(s):  
Qian Wu
Keyword(s):  
Image Processing ◽  
System Development ◽  
Recognition System ◽  
Identity Authentication ◽  
Fingerprint Identification ◽  
Fingerprint Image ◽  
Image Preprocessing ◽  
The Core ◽  
Core Technology ◽  
Theoretical Significance

fingerprint image preprocessing and is one of the branch of image processing and pattern recognition, after several years of development the increasing maturity of the technology. Due to the uniqueness and invariability of fingerprints, and the feasibility and practicability of the fingerprint identification technology, fingerprint identification has become the most popular, the most convenient, one of the most reliable personal identity authentication technology. Although on this technology has a variety of molding products, but because many of the core technology by commercial interests and confidentiality need without open, as well as the development of the society put forward higher requirements on the performance of the system, so in this field of research, still has important theoretical significance and practical value.

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.

Research and Simulation on the Image Segmentation Algorithm with Corn Image Based on HSI Color Model

2012 ◽  
Vol 459 ◽  
pp. 128-131
Author(s):  
Xue Feng Hou ◽  
Yuan Yuan Shang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Detection Algorithm ◽  
Color Model ◽  
Edge Detection Algorithm ◽  
Segmentation Algorithms ◽  
Hsi Color Model ◽  
Classical Image ◽  
Image Segmentation Algorithm

Image segmentation is one focus of digital image processing. In this paper, fourteen different kinds of classical image segmentation algorithms are studied and compared using corn image and simulating in MATLAB based on HSI color model. The result reveals that the method that using H component based on HSI color model to deal with the histogram threshold algorithm and Laplace edge detection algorithm is effectively extract the plant from the corn image

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