An adaptive recognition method for take-off action images of back-style high jump based on feature extraction

2021 ◽  
Author(s):  
Lijie Zhai ◽  
Haisheng Duan ◽  
Donghui Chen
Keyword(s):  
Feature Extraction ◽  
Recognition Method ◽  
Adaptive Recognition

Research on Adaptive Face Gender Recognition Based on Compressive Sensing

2014 ◽  
Vol 989-994 ◽  
pp. 4187-4190 ◽  
Author(s):  
Lin Zhang
Keyword(s):  
Feature Extraction ◽  
Compressive Sensing ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Low Frequency ◽  
Face Image ◽  
Gender Recognition ◽  
Recognition Method ◽  
Frequency Information ◽  
Learning Machine

An adaptive gender recognition method is proposed in this paper. At first, do multiwavlet transform to face image and get its low frequency information, then do feature extraction to the low frequency information using compressive sensing (CS), use extreme learning machine (ELM) to achieve gender recognition finally. In the process of feature extraction, we use genetic algorithm (GA) to get the number of measurements of CS in order to gain the highest recognition rate, so the method can adaptive access optimal performance. Experimental results show that compared with PDA and LDA, the new method improved the recognition accuracy substantially.

Combining Block DCV and Support Vector Machine for Ear Recognition

2016 ◽  
Vol 8 (2) ◽  
pp. 36-44 ◽  
Author(s):  
Zhao Hailong ◽  
Yi Junyan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Construction Method ◽  
Experimental Results ◽  
Support Vector ◽  
Recognition Method ◽  
Ear Recognition ◽  
Human Ear ◽  
Better Than

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

scholarly journals Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

2018 ◽  
Vol 1004 ◽  
pp. 012022 ◽  
Author(s):  
Ming Zhang ◽  
Fei Xie ◽  
Jing Zhao ◽  
Rui Sun ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Recognition Method

Human Posture Recognition Based on DAG-SVMS

2014 ◽  
Vol 1042 ◽  
pp. 117-120 ◽  
Author(s):  
Shuang Mei Wang ◽  
Yi Gao ◽  
Li Luo
Keyword(s):  
Feature Extraction ◽  
Moving Objects ◽  
Feature Vector ◽  
Moving Object ◽  
Recognition Method ◽  
Human Posture ◽  
Feature Parameters ◽  
Posture Recognition

A posture feature extraction and recognition method in monitoring environment is proposed in this paper which can recognize human shapes and analyze human postures. First contours of moving objects are extracted from two frames of a consecutive monitoring video. Then feature parameters are calculated from boundary contours to construct feature vector. In order to classify moving object and human and analyze postures, a DAG-SVMS is constructed by training 100 sample images. Results demonstrate the validity of this method.

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