EXTRACTION AND MATCHING OF SYMBOLIC CONTOUR GRAPHS

We describe an object recognition system based on symbolic contour graphs. The image to be analyzed is transformed into a graph with object corners as vertices and connecting contours as edges. Image corners are determined using a robust multiscale corner detector. Edges are constructed by line-following between corners based on evidence from the multiscale Gabor wavelet transform. Model matching is done by finding subgraph isomorphisms in the image graph. The complexity of the algorithm is reduced by labeling vertices and edges, whereby the choice of labels also makes the recognition system invariant under translation, rotation and scaling. We provide experimental evidence and theoretical arguments that the matching complexity is below O(#V3), and show that the system is competitive with other graph-based matching systems.

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A New Recongnition System Based on Gabor Wavelet Transform for Shockable Electrocardiograms

Journal of Applied Life Sciences International ◽

10.9734/jalsi/2020/v23i1230203 ◽

2021 ◽

pp. 40-51

Author(s):

Takayuki Okai ◽

Shonosuke Akimoto ◽

Hidetoshi Oya ◽

Kazushi Nakano ◽

Hiroshi Miyauchi ◽

...

Keyword(s):

Cardiac Arrest ◽

Wavelet Transform ◽

Recognition Performance ◽

Sudden Cardiac Arrest ◽

Recognition System ◽

Gabor Wavelet ◽

Huge Number ◽

Gabor Wavelet Transform ◽

Feature Parameters ◽

Spectrum Feature

This paper presents a new recognition system for shockable arrhythmias for patients suffering from sudden cardiac arrest. In order to develop the recognition system, lots of electrocardiogram (ECGs) have been analyzed by using gabor wavelet transform (GWT). Although, there is a huge number of spectrum feature parameters, recognition performance for all combinations for spectrum feature parameters are evaluated, and on the basis of the evaluation results, useful and effective spectrum features for ECGs are extracted. As a result, the proposed recognition system based on the selected effective spectrum feature parameters can achieved good performance comparing with the existing results.

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Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

BioMed Research International ◽

10.1155/2021/6621540 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Sulayman Ahmed ◽

Mondher Frikha ◽

Taha Darwassh Hanawy Hussein ◽

Javad Rahebi

Keyword(s):

Feature Extraction ◽

Feature Selection ◽

Particle Swarm Optimization ◽

Deep Learning ◽

Wavelet Transform ◽

Recognition System ◽

Gabor Wavelet ◽

Swarm Optimization ◽

Gabor Wavelet Transform ◽

Face Recognition System

In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.

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