Pulse Feature Extraction Based on Improved Gaussian Model

2014 ◽  
Author(s):  
Guangming Lu ◽  
Zhixing Jiang ◽  
Liying Ye ◽  
Yaotian Huang
Keyword(s):  
Feature Extraction ◽  
Gaussian Model

scholarly journals Research on the Application of Artificial Intelligence Machine Learning Technology in Improving the Accuracy of Engineering Image Processing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042007
Author(s):  
Xiaowen Liu ◽  
Juncheng Lei
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Image Recognition ◽  
Recognition Performance ◽  
Color Space ◽  
Gaussian Model ◽  
Image Feature ◽  
Layer By Layer ◽  
Image Information

Abstract Image recognition technology mainly includes image feature extraction and classification recognition. Feature extraction is the key link, which determines whether the recognition performance is good or bad. Deep learning builds a model by building a hierarchical model structure like the human brain, extracting features layer by layer from the data. Applying deep learning to image recognition can further improve the accuracy of image recognition. Based on the idea of clustering, this article establishes a multi-mix Gaussian model for engineering image information in RGB color space through offline learning and expectation-maximization algorithms, to obtain a multi-mix cluster representation of engineering image information. Then use the sparse Gaussian machine learning model on the YCrCb color space to quickly learn the distribution of engineering images online, and design an engineering image recognizer based on multi-color space information.

scholarly journals A New Object Tracking Framework for Interest Point Based Feature Extraction Algorithms

2020 ◽  
Vol 26 (1) ◽  
pp. 63-71
Author(s):  
Zafer Guler ◽  
Ahmet Cinar ◽  
Erdal Ozbay
Keyword(s):  
Feature Extraction ◽  
Object Tracking ◽  
Gaussian Model ◽  
Second Step ◽  
Interest Point ◽  
Position Information ◽  
Feature Extracting ◽  
Object Modelling ◽  
Dbscan Algorithm ◽  
Extraction Algorithm

This paper presents a novel object tracking framework for interest point based feature extracting algorithms. The proposed framework uses the feature extracting algorithm without making any changes and it relies on outlier detection, object modelling, and object tracking. At first, the keypoints are extracted by using a feature extraction algorithm. Then, incorrect keypoint matches are detected by the DBScan algorithm. The second step of our tracking framework is object modelling. The object model is defined as a bounding box. The box model has six points and each of these points has its own Gaussian model. Finally, the Gaussian model is performed for object tracking. In object tracking, the old five values are retained to detect incorrect position information. Thus, while the object movements are softened, the instant deviations are eliminated also. Our interest point based object tracking framework (IPBOT) works with any interest point based feature extracting algorithm. Thus, a new algorithm can be added to the object tracking framework with a short integration process. The experiment results show that the proposed tracker significantly improves the success rate of the object tracking.

Recognition of Persian Sign Language Alphabet Using Gaussian Distribution, Radial Distance and Centroid-Radii

2020 ◽  
Vol 13 ◽  
Author(s):  
Asra Abdolmalaki ◽  
Abdulbaghi Ghaderzadeh ◽  
Vafa Maihami
Keyword(s):  
Feature Extraction ◽  
Sign Language ◽  
Gaussian Model ◽  
Radial Distance ◽  
Recognition System ◽  
Ordinary People ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Region ◽  
Deaf Persons

: The sign language is the main communication method of deaf persons with ordinary people. Ordinary people learn and understand the written language by using the visual representation of the language. Instead there is no correspondence between speech and writing for the deaf persons and the letters are only symbols that have no meaning for them. Since most ordinary people are not familiar with the sign language, a sign language recognition system can be useful in symptoms recognition. In this paper, a novel approach is presented to identify Persian static symptoms. The proposed sign language recognition system consists of two segmentation and feature extraction phases. In the segmentation phase, the hand region is separated by an effective segmentation method from the original image at first. This method is based on the unique Gaussian model in the YCbCr color space. The Bayes rule is used to precisely identify the hand region too. In the feature extraction phase, the radial model is used to obtain a one-dimensional function to display the hand region boundary and to compute the combined feature vector. In order to normalize this method, the Fourier Transformation method is applied. The proposed system does not use any kind of gloves or sensors. The system was trained and tested using 480 image samples of Persian sign language characters, 15 images per sign, with the .jpg extension. Extensive experimental evaluations indicate that the proposed recognition system is less susceptible to displacement, scale, and rotation, and can detect symptoms at an accuracy of 95.62%.

Automatic Detection of Operator without Safety Helmet

2014 ◽  
Vol 602-605 ◽  
pp. 1670-1674 ◽  
Author(s):  
Yu Fan ◽  
Kang Xiong Yu ◽  
Xiao Qing Tang ◽  
He Ping Zheng ◽  
Li Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Monitoring System ◽  
Background Subtraction ◽  
Gaussian Model ◽  
Automatic Detection ◽  
Human Head ◽  
Moving Target ◽  
Real Time Monitoring ◽  
Extraction Algorithm

It is very important to protect the safety of the human head with helmet. Traditional detection for helmet wearing mainly relies on manual approach, which was more subjective that a missing condition may happen caused by fatigue and other factors. Owing to this situation, this paper proposed a method for automatic detection of operator without helmet in real-time. Firstly, Gaussian model for background subtraction is used to detect moving target. Secondly, HOG feature extraction can be used to classify the human target from vehicle. Then, a color feature extraction algorithm is proposed for helmet recognition. The algorithm has been applied into the real time monitoring system and verified with higher accuracy.

scholarly journals EEG Signal Classification Using Manifold Learning and Matrix-Variate Gaussian Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lei Zhu ◽  
Qifeng Hu ◽  
Junting Yang ◽  
Jianhai Zhang ◽  
Ping Xu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Recognition Accuracy ◽  
Structural Information ◽  
Gaussian Model ◽  
Extraction Methods ◽  
Data Sets ◽  
Two Dimensional ◽  
Eeg Signals ◽  
Linear Discriminant

In brain-computer interface (BCI), feature extraction is the key to the accuracy of recognition. There is important local structural information in the EEG signals, which is effective for classification; and this locality of EEG features not only exists in the spatial channel position but also exists in the frequency domain. In order to retain sufficient spatial structure and frequency information, we use one-versus-rest filter bank common spatial patterns (OVR-FBCSP) to preprocess the data and extract preliminary features. On this basis, we conduct research and discussion on feature extraction methods. One-dimensional feature extraction methods like linear discriminant analysis (LDA) may destroy this kind of structural information. Traditional manifold learning methods or two-dimensional feature extraction methods cannot extract both types of information at the same time. We introduced the bilinear structure and matrix-variate Gaussian model into two-dimensional discriminant locality preserving projection (2DDLPP) algorithm and decompose EEG signals into spatial and spectral parts. Afterwards, the most discriminative features were selected through a weight calculation method. We tested the method on BCI competition data sets 2a, data sets IIIa, and data sets collected by our laboratory, and the results were expressed in terms of recognition accuracy. The cross-validation results were 75.69%, 70.46%, and 54.49%, respectively. The average recognition accuracy of new method is improved by 7.14%, 7.38%, 4.86%, and 3.8% compared to those of LDA, two-dimensional linear discriminant analysis (2DLDA), discriminant locality property projections (DLPP), and 2DDLPP, respectively. Therefore, we consider that the proposed method is effective for EEG classification.

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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