Multi-template Matching Algorithm Based on Adaptive Fusion

2017 ◽  
pp. 602-613
Author(s):  
Bing Li ◽  
Juan Su ◽  
Dan Chen ◽  
Wei Wu
Keyword(s):  
Template Matching ◽  
Matching Algorithm ◽  
Adaptive Fusion

Research on the measurement of belt speed by video in coal mine based on improved template matching algorithm

2011 ◽  
Vol 17 (4) ◽  
pp. 469-474 ◽  
Author(s):  
Ai-chun Zhu ◽  
Wei Hua ◽  
Chun Wang ◽  
Yong-xing Wang
Keyword(s):  
Coal Mine ◽  
Template Matching ◽  
Matching Algorithm ◽  
Belt Speed

scholarly journals Indian Currency Denomination Recognition and Fake Currency Identification

2021 ◽  
Vol 2089 (1) ◽  
pp. 012008
Author(s):  
B Padmaja ◽  
P Naga Shyam Bhargav ◽  
H Ganga Sagar ◽  
B Diwakar Nayak ◽  
M Bhushan Rao
Keyword(s):  
Template Matching ◽  
Visually Impaired ◽  
Senior Citizens ◽  
Region Of Interest ◽  
Automated System ◽  
Canny Edge Detection ◽  
Matching Algorithm ◽  
Multi Scale ◽  
Currency Note ◽  
Canny Edge

Abstract Visually impaired and senior citizens find it difficult to identify different banknotes, driving the need for an automated system to recognize currency notes. This study proposes recognizing Indian currency notes of various denominations using Deep Learning through the CNN model. While not recognizing currency notes is one issue, identifying fake notes is another major issue. Currency counterfeiting is the illegal imitation of currency to deceive its recipient. The current existing methodologies for identifying a phony note rely on hardware. A method completely devoid of hardware that relies on specific security features to help distinguish a legitimate currency note from an illegitimate one is much needed. These features are extracted using the boundary box region of interest (ROI) and Canny Edge detection in OpenCV implemented in Python, and the multi scale template matching algorithm is applied to match the security features and differentiate fake notes from legitimate notes.

A Robust Template Matching Algorithm Based on Best-Buddies Similarity

2017 ◽  
Vol 37 (3) ◽  
pp. 0315003
Author(s):  
王刚 Wang Gang ◽  
孙晓亮 Sun Xiaoliang ◽  
尚洋 Shang Yang ◽  
于起峰 Yu Qifeng
Keyword(s):  
Template Matching ◽  
Matching Algorithm

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.

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