scholarly journals A fast template matching algorithm based on principal orientation difference

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877822 ◽  
Author(s):  
Jichao Jiao ◽  
Xin Wang ◽  
Zhongliang Deng ◽  
Jichang Cao ◽  
Weihua Tang
Keyword(s):  
Real Time ◽  
Template Matching ◽  
Feature Points ◽  
Matching Method ◽  
Matching Algorithm ◽  
Training Samples ◽  
Illumination Changes ◽  
Orientation Difference ◽  
Direction Information ◽  
State Of Art

In the case that the background scene is dense map regularization complex and the detected objects are low texture, the method of matching according to the feature points is not applicable. Usually, the template matching method is used. When training samples are insufficient, the template matching method gets a worse detection result. In order to resolve the problem stably in real time, we propose a fast template matching algorithm based on the principal orientation difference feature. The algorithm firstly obtains the edge direction information by comparing the images that are binary. Then, the template area is divided where the different features are extracted. Finally, the matching positions are searched around the template. Experiments on the videos whose speed is 30 frames/s show that our algorithm detects the low-texture objects in real time with a matching rate of 95%. Compared with other state-of-art methods, our proposed method reduces the training samples significantly and is more robust to the illumination changes.

A Multi-Gradation Stereo Matching Method Based on Edge Feature Points

2013 ◽  
Vol 748 ◽  
pp. 624-628
Author(s):  
Zhu Lin Li
Keyword(s):  
Object Recognition ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Feature Points ◽  
Image Pair ◽  
Three Dimensional Reconstruction ◽  
Matching Method ◽  
Matching Algorithm ◽  
Dimensional Reconstruction ◽  
Edge Feature

A gradation stereo matching algorithm based on edge feature points was proposed. Its basic idea is: firstly edge feature points of image pair were extracted; then, gradient invariability and singular eigenvalue invariability were analyzed, two-grade stereo matching method was build, foundation matrix was solved further, and three-grade stereo matching algorithm was finished by foundation matrix guidance. The result indicates that the algorithm can improve matching precision, from 58.3% to 73.2%, it is simple and utility, and it is important for object recognition, tracking, and three-dimensional reconstruction.

Real Time Multi Face Blurring on Uncontrolled Environment based on Color Space algorithm

2019 ◽  
pp. 618-1626
Author(s):  
Alya'a R. Ali ◽  
Ban N. Dhannoon
Keyword(s):  
Real Time ◽  
Template Matching ◽  
Color Space ◽  
Second Step ◽  
Dynamic Background ◽  
Matching Algorithm ◽  
Complex Processes ◽  
Single Face ◽  
The Face ◽  
Advanced Computer

Faces blurring is one of the important complex processes that is considered one of the advanced computer vision fields. The face blurring processes generally have two main steps to be done. The first step has detected the faces that appear in the frames while the second step is tracking the detected faces which based on the information extracted during the detection step. In the proposed method, an image is captured by the camera in real time, then the Viola Jones algorithm used for the purpose of detecting multiple faces in the captured image and for the purpose of reducing the time consumed to handle the entire captured image, the image background is removed and only the motion areas are processed. After detecting the faces, the Color-Space algorithm is used to tracks the detected faces depending on the color of the face and to check the differences between the faces the Template Matching algorithm was used to reduce the processes time. Finally, thedetected faces as well as the faces that were tracked based on their color were obscured by the use of the Gaussian filter. The achieved accuracy for a single face and dynamic background are about 82.8% and 76.3% respectively.

A Rapid Matching Algorithm Based on Filtering Feature Points

2011 ◽  
Vol 66-68 ◽  
pp. 1954-1959
Author(s):  
Hong Bo Zhu ◽  
Xue Jun Xu ◽  
Xue Song Chen ◽  
Shao Hua Jiang
Keyword(s):  
Image Registration ◽  
Feature Vector ◽  
High Dimensional ◽  
Feature Points ◽  
Matching Method ◽  
Registration Accuracy ◽  
Matching Algorithm ◽  
Feature Vectors ◽  
Vast Amount

Matching feature points is an important step in image registration. For high- dimensional feature vector, the process of matching is very time-consuming, especially matching the vast amount of points. In the premise of ensuring the registration, filtering the candidate vectors to reduce the number of feature vectors, can effectively reduce the time matching the vectors. This paper presents a matching algorithm based on filtering the feature points on their characteristics of the corner feature. The matching method can effectively improve the matching speed, and can guarantee registration accuracy as well.

Image Template Matching Algorithm for Removing Useless Feature Points

2011 ◽  
Vol 271-273 ◽  
pp. 19-23 ◽  
Author(s):  
Jian Shu Gao ◽  
Tao Yang ◽  
Zhi Jing Yu
Keyword(s):  
Template Matching ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Image Feature ◽  
Feature Points ◽  
Illumination Variation ◽  
Matching Algorithm ◽  
Plane Image ◽  
Special Points ◽  
Fixed Structure

With the aim to remove the unwanted feature points, template matching algorithm which includes pixel-coherence and fixed structure is proposed in this paper. Corner detection algorithm can extract image feature points with the flexibility for illumination variation and affine transformation. This paper gets the special points of plane image by corner detection algorithm and proposes the problem of existing more useless point. Then, template matching algorithm is used to solve the issue and collects the best match points. Finally, the paper compares the template image and the matched image. The experimental results prove that the feature points by template matching algorithm have excellent accurate characteristic for the illumination, transfer and the rotation transform, and they are highly satisfactory.

Research of an Improved Snail Image Recognition Method Based on Grayscale Template Matching

2013 ◽  
Vol 433-435 ◽  
pp. 700-704
Author(s):  
Yin E Zhang
Keyword(s):  
Template Matching ◽  
Source Image ◽  
Complex Environment ◽  
Recognition Method ◽  
Matching Method ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Template Image ◽  
Traditional Algorithm ◽  
Template Selection

As the lack in the accuracy and speed of the template matching algorithm for the snail image in the complex environment, the snail source image and the template image have the appropriate scaling in order to improve their sizes in the traditional algorithm. The new algorithm avoids the very big and accurate characteristics about the snail images through shrinking the source images down. The grayscale template matching method is put forward based on the traditional template selection set to prevent that the error caused by human factors on the selected template, the redundancy between the templates is removed in a large extent, further the accuracy of the matching is improved, and the matching time is reduced greatly in the case of matching accuracy guarantee.

scholarly journals Detection of Vehicles Number Plate Using Image Processing with Template matching Method

2019 ◽  
Vol 7 (1) ◽  
pp. 14-24
Author(s):  
Ivany Sarief ◽  
Harfin Yusuf Biu ◽  
Fajar Harismana ◽  
Sepryan Ismail Chandra
Keyword(s):  
Image Processing ◽  
Template Matching ◽  
Input Image ◽  
License Plate ◽  
Processing Unit ◽  
Matching Method ◽  
Capture Process ◽  
Matching Algorithm ◽  
Template Image ◽  
Parking System

To design a system in order to identify an object number plate for the Indonesian format, an initial system is designed, in the form of a vehicle licence plate recognition application using template matching method. The goal of this application is to be implemented to the parking system by identifying the number plate. This system uses the camera for the image capture process, by utilizing image processing technology with the matching correlation template method for recognition to produce a string value from the image. Before doing recognition process, First, the pre processing stage is performed on the input image which includes grayscale, binary, until the segmentation stage before the correlation / comparison process is carried out on the image of Template. The process that occure in the pre-processing unit done for some reason including to make the image lighter and less complex. This process will make the image easer to be processed and also to increase the proses speed of the system. Before aply template matching algorithm to the image output from segmentation process, the image has to be resized first to match the size of the template image stored in data base. This has to done so that the target image and the template image can be match directly with template matching algorithm.  The output of this system is a string value which is refer to the value of the license plate capture by camera used by the system. The problem that arises in the introduction process is how to identify various types of characters with various sizes and shapes so that the string value is the same as the text image. The average success rate of this application is 70% so that further research must be carried out so this system can be implemented into the parking system. Keyword : Image Processing, Template matching, Camera, Number Plate, Matlab

scholarly journals PENGENALAN IRIS MATA MENGGUNAKAN METODE TEMPLATE MATCHING CORRELATION

2019 ◽  
Vol 2 (2) ◽  
pp. 105
Author(s):  
Sayuti Rahman ◽  
Ulfa Sahira
Keyword(s):  
Correlation Coefficient ◽  
Programming Language ◽  
Template Matching ◽  
Matching Method ◽  
Processing Stage ◽  
Matching Algorithm ◽  
Template Image ◽  
Automatic Methods ◽  
System Success ◽  
Human Characteristic

Abstract - Biometrics is the study of automatic methods for recognizing humans based on one or more parts of the human body that are unique. One human characteristic that can be used is iris, iris features can be used as distinguishing characteristics with other individuals. The stage that the writer did to be able to recognize the iris pattern of someone's eye in a digital image was the pre-processing stage, the template saving stage and the matching stage. In this study the author applies the template matching method to store the image into a template image stored in the database and the algorithm correlation coefficient for the characteristic matching algorithm between template data and test data. The application is designed using the Matlab R2010a programming language. The results of testing 22 images obtained by the percentage of system success was 86.36%. Keywords - Iris, Template Matching, Correlation Coefficient

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