Evaluation of Matching Accuracy of Template Matching Using a Steganography Algorithm

2015 ◽  
Author(s):  
Hirokazu Ishizuka ◽  
Koichi Sakurai ◽  
Isao Echizen ◽  
Keiichi Iwamura
Keyword(s):  
Template Matching ◽  
Matching Accuracy

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 A Hybrid Rao-NM Algorithm for Image Template Matching

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 678
Author(s):  
Xinran Liu ◽  
Zhongju Wang ◽  
Long Wang ◽  
Chao Huang ◽  
Xiong Luo
Keyword(s):  
Image Matching ◽  
Template Matching ◽  
Optimization Problems ◽  
Computing Time ◽  
Optimal Solution ◽  
Matching Accuracy ◽  
Swarm Optimization ◽  
Matching Problems ◽  
Applied Optimization ◽  
Search Capability

This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.

Detection and Tracking of Vehicle Target Based on Super-Resolution Reconstruction and Variable Template Matching

2014 ◽  
Vol 541-542 ◽  
pp. 1429-1432
Author(s):  
Jun Yong Ma ◽  
Shao Dong Chen ◽  
Sheng Wei Zhang
Keyword(s):  
Template Matching ◽  
Super Resolution ◽  
Image Sequence ◽  
Low Resolution ◽  
Matching Method ◽  
Matching Accuracy ◽  
High Quality ◽  
Resolution Image ◽  
Detection And Tracking ◽  
Image Super Resolution

Vehicle Target Detection and Tracking Method Based on Image Super-Resolution Reconstruction and Variable Template Matching is Put Forward. Firstly, a Nonlinear Iterative Algorithm is Applied to Reconstruct a Super-Resolution Image from Low Resolution Image Sequence; then, the Image is Standardized and the Movement Areas are Determined; Finally, the Variable Template Matching Method is Used to Detect and Track the Vehicle Targets in Movement Areas. from the Characteristics of Algorithm and the Experiment Results, we can see that the Proposed Algorithm Improves the Matching Accuracy of Target Tracking and Better Solves the Limitation of Missed Detection for Traditional Methods. the Reason of the Good Performance of the Proposed Algorithm Relies in High Quality Images Acquired by Super-Resolution Reconstruction from Low Resolution Image Sequence and the Application of Variable Template Matching Method.

Ensemble Visual Content based Search and Retrieval for Natural Scene Images

2019 ◽  
Vol 12 ◽  
Author(s):  
Pushpendra Singh ◽  
P.N. Hrisheekesha ◽  
Vinai Kumar Singh
Keyword(s):  
Template Matching ◽  
Feature Descriptor ◽  
Natural Scene ◽  
Visual Content ◽  
Other Information ◽  
Similar Images ◽  
State Of Art ◽  
Natural Scene Images ◽  
Hybrid Methodology ◽  
Search And Retrieval

Content based image retrieval (CBIR) is one of the field for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, color, shape, and other information. In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the literature suggests that the precision and recall for the gained results using single content descriptor is not significant. The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes.

Cognition System of Bolt Hole Using Template Matching

2011 ◽  
Author(s):  
Gwan Ryong Baek ◽  
Yung Hak Mo ◽  
Jae Sik Jeong ◽  
Jung Min Park ◽  
Myo Taeg Lim
Keyword(s):  
Template Matching
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