A Fast Logo Recognition Algorithm in Noisy Document Images

2011 ◽  
Author(s):  
Sina Hassanzadeh ◽  
Hossein Pourghassem
Keyword(s):  
Recognition Algorithm ◽  
Document Images ◽  
Logo Recognition

A vehicle logo recognition algorithm based on the improved SIFT feature

2018 ◽  
Vol 26 (5) ◽  
pp. 1267-1274
Author(s):  
耿庆田 GENG Qing-tian ◽  
赵浩宇 ZHAO Hao-yu ◽  
王宇婷 WANG Yu-ting ◽  
赵宏伟 ZHAO Hong-wei
Keyword(s):  
Recognition Algorithm ◽  
Sift Feature ◽  
Logo Recognition ◽  
Vehicle Logo Recognition

scholarly journals Research and Implementation of Vehicle Target Detection and Information Recognition Technology Based on NI myRIO

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1765 ◽  
Author(s):  
Hongliang Wang ◽  
Shuang He ◽  
Jiashan Yu ◽  
Luyao Wang ◽  
Tao Liu
Keyword(s):  
Information Extraction ◽  
Target Detection ◽  
Information Acquisition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Algorithm ◽  
License Plate ◽  
Logo Recognition ◽  
Vehicle Information ◽  
High Recognition Accuracy

A vehicle target detection and information extraction scheme based on NI (National Instruments) myRIO is designed in this paper. The vehicle information acquisition and processing method based on image recognition is used to design a complete vehicle detection and information extraction system. In the LabVIEW programming environment, the edge detection method is used to realize the vehicle target detection, the pattern matching method is used to realize the vehicle logo recognition, and the Optical Character Recognition (OCR) character recognition algorithm is used to realize the vehicle license plate recognition. The feasibility of the design scheme in this paper is verified through the actual test and analysis. The scheme is intuitive and efficient, with the high recognition accuracy.

SVM-based global vision system of sports competition and action recognition

2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Task Assignment ◽  
Recognition Algorithm ◽  
Control Factor ◽  
Training Time ◽  
Adaboost Algorithm ◽  
Advantages And Disadvantages ◽  
Time Control ◽  
Correlation Degree

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

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