An Automatic Classification Method for Involuntary and Two Types of Voluntary Blinks

2016 ◽  
Vol 136 (9) ◽  
pp. 1350-1358 ◽  
Author(s):  
Hironobu Sato ◽  
Kiyohiko Abe ◽  
Shoichi Ohi ◽  
Minoru Ohyama
Keyword(s):  
Automatic Classification ◽  
Classification Method

scholarly journals Snake classification from images

2017 ◽  
Author(s):  
Alex James
Keyword(s):  
Parallel Computing ◽  
Real Time ◽  
Automatic Classification ◽  
Classification Method ◽  
Computing Environment ◽  
Snake Bites ◽  
Major Species ◽  
King Cobra ◽  
Wild Life

Incorrect snake identification from the observable visual traits is a major reason of death resulting from snake bites. So far no automatic classification method has been proposed to distinguish snakes by deciphering the taxonomy features of snake for the two major species of snakes i.e. Elapidae and Viperidae. We present a parallel processed inter-feature product similarity fusion based automatic classification of Spectacled Cobra, Russel's Viper, King Cobra, Common Krait, Saw Scaled Viper, Hump nosed Pit Viper. We identify 31 different taxonomically relevant features from snake images for automated snake classification studies. The scalability and real-time implementation of the classifier is analyzed through GPU enabled parallel computing environment. The developed systems finds application in wild life studies, analysis of snake bites and in management of snake population.

An Automatic Classification Method for Adolescent Idiopathic Scoliosis Based on U-net and Support Vector Machine

2019 ◽  
Vol 63 (6) ◽  
pp. 60502-1-60502-13
Author(s):  
Zhiqiang Tan ◽  
Kai Yang ◽  
Yu Sun ◽  
Bo Wu ◽  
Shibo Li ◽  
...  
Keyword(s):  
Adolescent Idiopathic Scoliosis ◽  
Idiopathic Scoliosis ◽  
Automatic Classification ◽  
Classification Method ◽  
Support Vector ◽  
New Techniques ◽  
Fast Detection ◽  
Measurement Results ◽  
Identification Approach ◽  
The One

Abstract The traditional manual method for adolescent idiopathic scoliosis diagnosis suffers from observer variability. Doctors need an objective, accurate and fast detection method which would help to overcome the problem encountered by the traditional classification. This study introduces new techniques, including automatic radiograph segmentation, scoliosis measurement and classification, based on artificial intelligence. Firstly, the vertebral region in the radiograph was segmented by U-net and the scoliosis measurement was performed on the segmented image. Secondly, SVM classification was conducted by extracting the curve features in posteroanterior images and supplementary parameters in lateral and bending images. Finally, the results of automatic scoliosis measurement were compared with the one made by surgeons and the accuracy of the proposed automatic classification method was verified by a test set. The U-net segmentation model was successfully established to segment the vertebrae and the differences between the measurement results obtained by the automatic and manual measurement method were less than one degree and the accuracy of the automatic curve identification approach was found to be 100%.

A deep learning automatic classification method for clogging pervious pavement

2021 ◽  
Vol 309 ◽  
pp. 125195
Author(s):  
Zhongze Zhang ◽  
Jianing Xue ◽  
Jiong Zhang ◽  
Mingqiang Yang ◽  
Bowen Meng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Classification ◽  
Classification Method ◽  
Pervious Pavement

scholarly journals An automatic aerosol classification for earlinet: application and results

2018 ◽  
Vol 176 ◽  
pp. 09012 ◽  
Author(s):  
Nikolaos Papagiannopoulos ◽  
Lucia Mona ◽  
Vassilis Amiridis ◽  
Ioannis Binietoglou ◽  
Giuseppe D’Amico ◽  
...  
Keyword(s):  
Air Quality ◽  
Mahalanobis Distance ◽  
Automatic Classification ◽  
Classification Method ◽  
Weather System ◽  
Network Measurements ◽  
Aerosol Classification ◽  
Aerosol Sources ◽  
Mahalanobis Distance Classifier ◽  
The Impact

Aerosol typing is essential for understanding the impact of the different aerosol sources on climate, weather system and air quality. An aerosol classification method for EARLINET (European Aerosol Research Lidar Network) measurements is introduced which makes use the Mahalanobis distance classifier. The performance of the automatic classification is tested against manually classified EARLINET data. Results of the application of the method to an extensive aerosol dataset will be presented.

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