Matching of Affine Transformed Images by Using Similarity Based on Local Concentric Features

1997 ◽  
Vol 9 (2) ◽  
pp. 126-131 ◽  
Author(s):  
Atsushi Sakai ◽  
◽  
Seiji Ujifuku ◽  
Yoshihiko Nomura ◽  
Keyword(s):  
Fourier Transform ◽  
Pattern Matching ◽  
Affine Transformation ◽  
Feature Points ◽  
Matching Method ◽  
Corner Points ◽  
Local Areas

This paper proposes a pattern matching method based on concentric features calculated over local areas close to feature points. For such feature points, the corner points of images are used, and images generated by complex-log mapping and Fourier transform are used as the concentric features. The procedures are as follows: (1) Similarity is found based on the concentric features in neighborhoods of corner points; (2) in consideration of the uniqueness of correspondence and removal of pseudo-correspondence, correspondence is obtained from this similarity; (3) with correspondence as a weight, the parameters of the affine transformation are estimated. By conducting experiments, the robustness of the proposed technique against deformations and noises is shown.

scholarly journals A novel optimal multi-pattern matching method with wildcards for DNA sequence

2021 ◽  
Vol 29 ◽  
pp. 115-124
Author(s):  
Xinlu Wang ◽  
Ahmed A.F. Saif ◽  
Dayou Liu ◽  
Yungang Zhu ◽  
Jon Atli Benediktsson
Keyword(s):  
Dna Sequence ◽  
Pattern Matching ◽  
Health Informatics ◽  
State Of The Art ◽  
Machine Language ◽  
Data Sets ◽  
Fundamental Issue ◽  
Matching Method ◽  
Dna Sequence Alignment ◽  
The Given

BACKGROUND: DNA sequence alignment is one of the most fundamental and important operation to identify which gene family may contain this sequence, pattern matching for DNA sequence has been a fundamental issue in biomedical engineering, biotechnology and health informatics. OBJECTIVE: To solve this problem, this study proposes an optimal multi pattern matching with wildcards for DNA sequence. METHODS: This proposed method packs the patterns and a sliding window of texts, and the window slides along the given packed text, matching against stored packed patterns. RESULTS: Three data sets are used to test the performance of the proposed algorithm, and the algorithm was seen to be more efficient than the competitors because its operation is close to machine language. CONCLUSIONS: Theoretical analysis and experimental results both demonstrate that the proposed method outperforms the state-of-the-art methods and is especially effective for the DNA sequence.

Vision for mobile robots

1992 ◽  
Vol 337 (1281) ◽  
pp. 341-350 ◽  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Structure From Motion ◽  
Optic Flow ◽  
Image Sequences ◽  
Additional Constraint ◽  
Feature Points ◽  
Scene Structure ◽  
Corner Points ◽  
Edge Points

Localized feature points, particularly corners, can be computed rapidly and reliably in images, and they are stable over image sequences. Corner points provide more constraint than edge points, and this additional constraint can be propagated effectively from corners along edges. Implemented algorithms are described to compute optic flow and to determine scene structure for a mobile robot using stereo or structure from motion. It is argued that a mobile robot may not need to compute depth explicitly in order to navigate effectively.

scholarly journals Handwritten tifinagh character recognition using simple geometric shapes and graphs

2019 ◽  
Vol 13 (2) ◽  
pp. 598
Author(s):  
Youssef Ouadid ◽  
Abderrahmane Elbalaoui ◽  
Mehdi Boutaounte ◽  
Mohamed Fakir ◽  
Brahim Minaoui
Keyword(s):  
Character Recognition ◽  
Graph Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Features Extraction ◽  
Experimental Results ◽  
Feature Points ◽  
Matching Method ◽  
Extraction Algorithm ◽  
Novel Algorithm

<p>In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.</p>

scholarly journals Research on the Relative Positions-Constrained Pattern Matching Method for Underwater Gravity-Aided Inertial Navigation

2015 ◽  
Vol 68 (5) ◽  
pp. 937-950 ◽  
Author(s):  
Lin Wu ◽  
Hubiao Wang ◽  
Hua Chai ◽  
Houtse Hsu ◽  
Yong Wang
Keyword(s):  
South China Sea ◽  
Pattern Matching ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Success Rates ◽  
Simulation Test ◽  
Matching Method ◽  
Contour Matching ◽  
Simulation Results ◽  
Straight Lines

A Relative Positions-Constrained pattern Matching (RPCM) method for underwater gravity-aided inertial navigation is presented in this paper. In this method the gravity patterns are constructed based on the relative positions of points in a trajectory, which are calculated by Inertial Navigation System (INS) indications. In these patterns the accumulated errors of INS indicated positions are cancelled and removed. Thus the new constructed gravity patterns are more accurate and reliable while the process of matching can be constrained, and the probability of mismatching also can be reduced. Two gravity anomaly maps in the South China Sea were chosen to construct a simulation test. Simulation results show that with this RPCM method, the shape of the trajectory in gravity-aided navigation is not as restricted as that in traditional Terrain Contour Matching (TERCOM) algorithms. Moreover, the performance included matching success rates and position accuracies are highly improved in the RPCM method, especially for the trajectories that are not in straight lines. Thus the proposed method is effective and suitable for practical navigation.

Recognition of Chemical Entities using Pattern Matching and Functional Group Classification

2016 ◽  
Vol 12 (4) ◽  
pp. 21-44 ◽  
Author(s):  
R. Hema ◽  
T. V. Geetha
Keyword(s):  
Pattern Matching ◽  
Functional Group ◽  
Named Entity Recognition ◽  
Chemical Compounds ◽  
Chemical Entity ◽  
Entity Recognition ◽  
Matching Method ◽  
Named Entity ◽  
One Way Anova

The two main challenges in chemical entity recognition are: (i) New chemical compounds are constantly being synthesized infinitely. (ii) High ambiguity in chemical representation in which a chemical entity is being described by different nomenclatures. Therefore, the identification and maintenance of chemical terminologies is a tough task. Since most of the existing text mining methods followed the term-based approaches, the problems of polysemy and synonymy came into the picture. So, a Named Entity Recognition (NER) system based on pattern matching in chemical domain is developed to extract the chemical entities from chemical documents. The Tf-idf and PMI association measures are used to filter out the non-chemical terms. The F-score of 92.19% is achieved for chemical NER. This proposed method is compared with the baseline method and other existing approaches. As the final step, the filtered chemical entities are classified into sixteen functional groups. The classification is done using SVM One against All multiclass classification approach and achieved the accuracy of 87%. One-way ANOVA is used to test the quality of pattern matching method with the other existing chemical NER methods.

Fracture Surface Matching Method for Terracotta Based on Feature Points

2018 ◽  
Vol 55 (4) ◽  
pp. 041005
Author(s):  
赵夫群 Zhao Fuqun ◽  
耿国华 Geng Guohua
Keyword(s):  
Fracture Surface ◽  
Surface Matching ◽  
Feature Points ◽  
Matching Method
Sign in / Sign up

Export Citation Format

Share Document