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

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 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.

scholarly journals Angle and Scale Invariant Template Matching for Handling Image Distortions

2018 ◽  
Vol 7 (2.24) ◽  
pp. 102
Author(s):  
Badrinaathan J ◽  
L N.B.Srinivas
Keyword(s):  
Template Matching ◽  
Histogram Equalization ◽  
Vital Role ◽  
Source Image ◽  
Image Patch ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Template Image ◽  
Computer Vision Applications

Template matching is a diagnostic approach for detecting a patch of a template image in a given source image. This plays a vital role in multitudinal computer vision applications. In this paper, we propose a methodology that makes the naive template matching algorithm scale and angle invariant during the image recognition process where the source and template is converted to gray scale which makes the technique enhance its proficiency. The proposed algorithm handles the arbitrary modulations of the image patch with respect to size and angle by an exhaustive search of all combinations of sizes are done along with populous combinations of angles. The images adapted are subjected to certain filtering and convolution methods which deepens the quality of the images which in turn assists in retrieving the features with accuracy. The image intensities are adjusted using histogram equalization to enhance the image contrast. These images are then subjected to perform template matching using normalized cross correlation to measure similarity between those two images.  

scholarly journals Pengenalan Plat Kendaraan Bermotor dengan Menggunakan Metode Template Matching dan Deep Belief Network

2019 ◽  
Vol 19 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Michael Michael ◽  
Frenky Tanoto ◽  
Eric Wibowo ◽  
Frenky Lutan ◽  
Abdi Dharma
Keyword(s):  
Template Matching ◽  
Deep Belief Network ◽  
License Plate ◽  
Matching Method ◽  
Matching Algorithm ◽  
Belief Network ◽  
Traffic Police ◽  
The Third ◽  
The Mean ◽  
Success Percentage

The license plate of the vehicle is unique and is only owned by one vehicle per vehicle plate series, to make it easier for the police, especially the traffic police, to track traffic violators through the vehicle number plate. The Deep Belief Network algorithm works by processing the dataset through 3 stages, where the first layer is trained, the results of the first layer are then re-trained, and the results of the second layer calculation are made into the third layer count, the mean results on the calculation of the third layer become the result of learning Deep Belief Network then with the Template Matching algorithm, Deep Belief Network is assisted with the introduction of vehicle plates. In a study conducted using the DBN algorithm with the Template Matching method succeeded in recognizing a vehicle plate with a success percentage of 80% from 20 trials. The experiments carried out included plates that were not clearly seen. Failures that occur in the trials are generally due to under- or over-lighting on the vehicle plate.

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