scholarly journals Repeat pattern segmentation of print fabric based on adaptive template matching

2020 ◽  
Vol 15 ◽  
pp. 155892502097328
Author(s):  
Zhong Xiang ◽  
Ding Zhou ◽  
Miao Qian ◽  
Miao Ma ◽  
Yang Liu ◽  
...  
Keyword(s):  
Template Matching ◽  
Local Maximum ◽  
Edge Density ◽  
Original Image ◽  
Edge Structure ◽  
Template Image ◽  
Pattern Retrieval ◽  
Pattern Segmentation ◽  
Periodic Repetition ◽  
Repeat Pattern

Patterned fabrics are generally constructed from the periodic repetition of a primitive pattern unit. Repeat pattern segmentation of printed fabrics has a very significant impact on the pattern retrieval and pattern defect detection. In this paper, we propose a new approach for repeat pattern segmentation by employing the adaptive template matching method. In contrast to the traditional method for template matching, the proposed algorithm first selects an adaptive size template image in the repeat pattern image based on the size of the original image and its local maximum edge density. Then it uses the sum of absolute differences as the matching features to identify the matched regions in the original image, and the minimum envelope border of the primitive pattern, typically as a parallelogram, can be determined from the results of the four adjacent matched templates. Finally, image traversal base on the obtained parallelogram is implemented over the original image using minimum information loss theory to produce a well-segmented primitive pattern with a complete edge structure. The results from the experiments conducted using an extensive database of real fabric images show that the proposed algorithm has the advantage of rotation invariance and scaling invariance and will not be affected when the background or foreground color is changed.

scholarly journals Rail Fastener Positioning Based on Double Template Matching

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yijin Qiu ◽  
Xingjie Chen ◽  
Zhaomin Lv
Keyword(s):  
Template Matching ◽  
Two Dimensions ◽  
Original Image ◽  
Positioning Accuracy ◽  
Precise Positioning ◽  
Horizontal Dimension ◽  
Positioning Strategy ◽  
Template Image ◽  
Positioning Method ◽  
Coarse To Fine

For global template matching (GTM), which is commonly used in the positioning of rail fasteners, only the fastener template is used to search the global image in both two dimensions, which will result in errors in two dimensions, and the lower positioning accuracy will be caused. A positioning method for rail fasteners based on double template matching (DTM) is proposed in this paper, in which the double template contains the rail template and the fastener template. First, the rail template is used to scan the original image in horizontal dimension, and the squared Euclidean distance (SED) is used to obtain the rail positioning in the original image. Combining with the prior knowledge of the fastener template image, the image composed of the rail and the fastener can be obtained, which is called the Rail Area Map (RAM) in this paper. Then, after preprocessing the RAM and the fastener template image, the fastener template image is used to scan the RAM in vertical dimension, and the normalized correlation coefficient (NCC) is used to calculate the similarity between the template and the subgraph of the RAM to achieve precise positioning of the fastener. The proposed DTM method adopts a positioning strategy from coarse to fine, and two templates are used to complete different positioning tasks in their own dimension, respectively. Due to the rail can be precise positioned in horizontal dimension, the error of the fastener positioning in the horizontal dimension can be avoided, and thus, the positioning accuracy can be improved. Experiments on the on-site line fastener images prove that the proposed method can effectively achieve the precise positioning of fasteners.

scholarly journals Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2123
Author(s):  
Aaron Aeberli ◽  
Kasper Johansen ◽  
Andrew Robson ◽  
David Lamb ◽  
Stuart Phinn
Keyword(s):  
Template Matching ◽  
Crop Production ◽  
Local Maximum ◽  
Banana Plant ◽  
Essential Information ◽  
Object Based ◽  
Agricultural Applications ◽  
Multi Temporal ◽  
Rule Set ◽  
Geographic Object

Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial (<1 m) and temporal (on-demand) resolution data facilitates monitoring of individual plants over time and can provide essential information about health, yield, and growth in a timely and quantifiable manner. Such applications would be beneficial for cropped banana plants due to their distinctive growth characteristics. Limited studies have employed UAV data for mapping banana crops and to our knowledge only one other investigation features multi-temporal detection of banana crowns. The purpose of this study was to determine the suitability of multiple-date UAV-captured multi-spectral data for the automated detection of individual plants using convolutional neural network (CNN), template matching (TM), and local maximum filter (LMF) methods in a geographic object-based image analysis (GEOBIA) software framework coupled with basic classification refinement. The results indicate that CNN returns the highest plant detection accuracies, with the developed rule set and model providing greater transferability between dates (F-score ranging between 0.93 and 0.85) than TM (0.86–0.74) and LMF (0.86–0.73) approaches. The findings provide a foundation for UAV-based individual banana plant counting and crop monitoring, which may be used for precision agricultural applications to monitor health, estimate yield, and to inform on fertilizer, pesticide, and other input requirements for optimized farm management.

Improved Watershed Algorithm Based on Morphology and Distance Transform

2013 ◽  
Vol 333-335 ◽  
pp. 1071-1075 ◽  
Author(s):  
Peng He ◽  
Kang Ling Fang ◽  
Xin Hai Liu
Keyword(s):  
Mathematical Morphology ◽  
Local Maximum ◽  
Face Image ◽  
Watershed Algorithm ◽  
Distance Transform ◽  
Original Image ◽  
Target Image ◽  
Gradient Operator ◽  
Gray Image ◽  
Circular Structure

In this paper we proposed an improved watershed algorithm for the quasi-circle overlapping images of the bars end face. According to the classical watershed algorithm, which often causes over-segmentation, the improved algorithm does a series of pretreatment with the original image, such as sobel filter. With the gradient operator and mathematical morphology method, we firstly obtain the smooth image of the forced local maximum marks. Then, on the basis of the quasi-circle characteristic of the target image, we proceed to maximize the erosion with circular structure in order to prevent under-segmentation. Finally, we use the watershed algorithm to segment the gray image based on distance transform. So we can separate the target from each other to achieve the accurate counting purpose. By using the proposed algorithm in the article, we obtain satisfactory segmentation results of the quasi-circle overlapping image of the bars end face image.

scholarly journals Individual Tree Position Extraction and Structural Parameter Retrieval Based on Airborne LiDAR Data: Performance Evaluation and Comparison of Four Algorithms

2020 ◽  
Vol 12 (3) ◽  
pp. 571 ◽  
Author(s):  
Chen ◽  
Xiang ◽  
Moriya
Keyword(s):  
Template Matching ◽  
Local Maximum ◽  
Airborne Lidar ◽  
Forest Monitoring ◽  
Lidar Data ◽  
Individual Tree ◽  
Matching Algorithm ◽  
Crown Width ◽  
Individual Trees ◽  
Airborne Lidar Data

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.

scholarly journals Optical Disc and Blood Vessel Segmentation in Retinal Fundus Images

2019 ◽  
Vol 8 (3) ◽  
pp. 67
Author(s):  
Amira B. Sallow ◽  
Hawkar Kh. Shaikha
Keyword(s):  
Blood Vessel ◽  
Template Matching ◽  
Cross Correlation ◽  
Region Of Interest ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Original Image ◽  
Matching Algorithm ◽  
A New Technique ◽  
Retinal Fundus

Segmentation of optical disk (OD) and blood vessel is one of the significant steps in automatic diabetic retinopathy (DR) detecting. In this paper, a new technique is presented for OD segmentation that depends on the histogram template matching algorithm and OD size. In addition, Kirsch method is used for Blood Vessel (BV) segmentation which is one of the popular methods in the edge detection and image processing technique. The template matching algorithm is used for finding the center of the OD. In this step, the histogram of each RGB (Red, Green, and Blue) planes are founded and then the cross-correlation is founded between the template and the original image, OD location is the point with maximum cross-correlation between them. The OD size varies according to the camera field of sight and the resolution of the original image. The rectangle size of OD is not the same for various databases, the estimated size for DRIVE, STARE, DIARTDB0, and DIARTDB1 are 80×80, 140×140, 190×190, and 190×190 respectively. After finding the OD center and rectangle size of OD, a binary mask is created with Region of Interest (ROI) for segmenting the OD. The DIARTDB0 is used to evaluate the proposed technique, the result is robust and vital with an accuracy of 96%.

Mouth Movement Recognition Using Template Matching and its Implementation in an Intelligent Room

2012 ◽  
Vol 24 (2) ◽  
pp. 311-319
Author(s):  
Kiyoshi Takita ◽  
◽  
Takeshi Nagayasu ◽  
Hidetsugu Asano ◽  
Kenji Terabayashi ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Similarity Measure ◽  
Template Matching ◽  
Target Object ◽  
Facial Region ◽  
Mouth Region ◽  
Home Appliances ◽  
Movement Recognition ◽  
Template Image ◽  
Mouth Movement

This paper proposes a method of recognizing movements of the mouth from images and implements the method in an intelligent room. The proposed method uses template matching and recognizes mouth movements for the purpose of indicating a target object in an intelligent room. First, the operator’s face is detected. Then, the mouth region is extracted from the facial region using the result of template matching with a template image of the lips. Dynamic Programming (DP) matching is applied to a similarity measure that is obtained by template matching. The effectiveness of the proposed method is evaluated through experiments to recognize several names of common home appliances and operations.

scholarly journals Research on track fastener positioning method based on local unidirectional template matching

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110261
Author(s):  
Liang Li ◽  
Zhaomin Lv ◽  
Xingjie Chen ◽  
Yijin Qiu ◽  
Liming Li ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Template Matching ◽  
Surrounding Area ◽  
Vertical Projection ◽  
Ballastless Track ◽  
Complex Background ◽  
Template Image ◽  
Ballasted Track ◽  
Positioning Method ◽  
Segmentation Threshold

Commonly used fastener positioning methods include pixel statistics (PS) method and template matching (TM) method. For the PS method, it is difficult to judge the image segmentation threshold due to the complex background of the track. For the TM method, the search in both directions of the global is easily affected by complex background, as a result, the locating accuracy of fasteners is low. To solve the above problems, this paper combines the PS method with the TM method and proposes a new fastener positioning method called local unidirectional template matching (LUTM). First, the rail positioning is achieved by the PS method based on the gray-scale vertical projection. Then, based on the prior knowledge, the image of the rail and the surrounding area of the rail is obtained which is referred to as the 1-shaped rail image; then, the 1-shaped rail image and the produced offline symmetrical fastener template is pre-processed. Finally, the symmetrical fastener template image is searched from top to bottom along the rail and the correlation is calculated to realize the fastener positioning. Experiments have proved that the method in this paper can effectively realize the accurate locating of the fastener for ballastless track and ballasted track at the same time.

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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