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.

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.

Automatic Classification of Biological Targets in a Tidal Channel Using a Multibeam Sonar

2020 ◽  
Vol 37 (8) ◽  
pp. 1437-1455 ◽  
Author(s):  
Emma Cotter ◽  
Brian Polagye
Keyword(s):  
Real Time ◽  
Automatic Classification ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Hill Climbing ◽  
Support Vector ◽  
Tidal Channel ◽  
Multibeam Sonar ◽  
Biological Targets

AbstractMultibeam sonars are widely used for environmental monitoring of fauna at marine renewable energy sites. However, they can rapidly accrue vast volumes of data, which poses a challenge for data processing. Here, using data from a deployment in a tidal channel with peak currents of 1–2 m s−1, we demonstrate the data-reduction benefits of real-time automatic classification of targets detected and tracked in multibeam sonar data. First, we evaluate classification capabilities for three machine learning algorithms: random forests, support vector machines, and k-nearest neighbors. For each algorithm, a hill-climbing search optimizes a set of hand-engineered attributes that describe tracked targets. The random forest algorithm is found to be most effective—in postprocessing, discriminating between biological and nonbiological targets with a recall rate of 0.97 and a precision of 0.60. In addition, 89% of biological targets are correctly classified as either seals, diving birds, fish schools, or small targets. Model dependence on the volume of training data is evaluated. Second, a real-time implementation of the model is shown to distinguish between biological targets and nonbiological targets with nearly the same performance as in postprocessing. From this, we make general recommendations for implementing real-time classification of biological targets in multibeam sonar data and the transferability of trained models.

Unobtrusive and Automatic Classification of Multiple People’s Abnormal Respiratory Patterns in Real Time Using Deep Neural Network and Depth Camera

2020 ◽  
Vol 7 (9) ◽  
pp. 8559-8571 ◽  
Author(s):  
Yunlu Wang ◽  
Menghan Hu ◽  
Yuwen Zhou ◽  
Qingli Li ◽  
Nan Yao ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Deep Neural Network ◽  
Automatic Classification ◽  
Depth Camera

scholarly journals Automatic classification of diseases from free-text death certificates for real-time surveillance

2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Bevan Koopman ◽  
Sarvnaz Karimi ◽  
Anthony Nguyen ◽  
Rhydwyn McGuire ◽  
David Muscatello ◽  
...  
Keyword(s):  
Real Time ◽  
Automatic Classification ◽  
Free Text ◽  
Death Certificates ◽  
Classification Of Diseases

Interpreting HVEM of muscle-cell impulse networks

1992 ◽  
Vol 50 (1) ◽  
pp. 126-127
Author(s):  
Paul DeCosta ◽  
Kyugon Cho ◽  
Stephen Shemlon ◽  
Heesung Jun ◽  
Stanley M. Dunn
Keyword(s):  
Structural Analysis ◽  
Muscle Cell ◽  
Electron Micrographs ◽  
Relative Size ◽  
Automatic Classification ◽  
Nerve Fibers ◽  
Analysis Algorithm ◽  
Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

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