Embedded Implementation of Template Matching Using Correlation and Particle Swarm Optimization

2016 ◽  
pp. 530-539 ◽  
Author(s):  
Yuri Marchetti Tavares ◽  
Nadia Nedjah ◽  
Luiza de Macedo Mourelle
Keyword(s):  
Particle Swarm Optimization ◽  
Template Matching ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Embedded Implementation

Tracking Patterns with Particle Swarm Optimization and Genetic Algorithms

2021 ◽  
pp. 534-549
Author(s):  
Yuri Marchetti Tavares ◽  
Nadia Nedjah ◽  
Luiza de Macedo Mourelle
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Template Matching ◽  
Processing Time ◽  
Particle Swarm ◽  
The Other ◽  
Swarm Optimization ◽  
Frame Sequence ◽  
Image Pixels ◽  
The One

The template matching is an important technique used in pattern recognition. The goal is to find a given pattern, of a prescribed model, in a frame sequence. In order to evaluate the similarity of two images, the Pearson's Correlation Coefficient (PCC) is used. This coefficient is calculated for each of the image pixels, which entails an operation that is computationally very expensive. In order to improve the processing time, this paper proposes two implementations for template matching: one using Genetic Algorithms (GA) and the other using Particle Swarm Optimization (PSO) considering two different topologies. The results obtained by the proposed methodologies are compared to those obtained by the exhaustive search in each pixel. The comparison indicates that PSO is up to 236x faster than the brute force exhausted search while GA is only 44x faster, for the same image. Also, PSO based methodology is 5x faster than the one based on GA.

Tracking Patterns with Particle Swarm Optimization and Genetic Algorithms

2017 ◽  
Vol 8 (2) ◽  
pp. 34-49 ◽  
Author(s):  
Yuri Marchetti Tavares ◽  
Nadia Nedjah ◽  
Luiza de Macedo Mourelle
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Template Matching ◽  
Processing Time ◽  
Particle Swarm ◽  
Brute Force ◽  
Swarm Optimization ◽  
Frame Sequence ◽  
Image Pixels ◽  
The One

The template matching is an important technique used in pattern recognition. The goal is to find a given pattern, of a prescribed model, in a frame sequence. In order to evaluate the similarity of two images, the Pearson's Correlation Coefficient (PCC) is used. This coefficient is calculated for each of the image pixels, which entails an operation that is computationally very expensive. In order to improve the processing time, this paper proposes two implementations for template matching: one using Genetic Algorithms (GA) and the other using Particle Swarm Optimization (PSO) considering two different topologies. The results obtained by the proposed methodologies are compared to those obtained by the exhaustive search in each pixel. The comparison indicates that PSO is up to 236x faster than the brute force exhausted search while GA is only 44x faster, for the same image. Also, PSO based methodology is 5x faster than the one based on GA.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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