Automatic classification of images on the Web

2001 ◽  
Author(s):  
Alexander Hartmann ◽  
Rainer W. Lienhart
Keyword(s):  
Automatic Classification ◽  
Classification Of Images ◽  
The Web

scholarly journals Visual and Automatic Classification of Galaxy Images

1984 ◽  
Vol 78 ◽  
pp. 129-131
Author(s):  
Allan Wirth
Keyword(s):  
Classification System ◽  
Automatic Classification ◽  
Classification Techniques ◽  
Advantages And Disadvantages ◽  
Classification Of Images

AbstractThe requirements for a classification system for galaxy images are discussed and the advantages and disadvantages of both visual and automatic classification techniques are assessed. The results of some preliminary experiments in the automatic classification of images are also presented.

Técnicas De Inteligencia Artificial Aplicadas A Pruebas Capilaroscopicas Para La Detección De Enfermedades Autoinmunes Que Comprometen La Circulación Sanguínea

2021 ◽  
Author(s):  
◽  
M. Alvarado
Keyword(s):  
Artificial Intelligence ◽  
Transfer Learning ◽  
Automatic Classification ◽  
Diseased Subject ◽  
Classification Of Images

The main purpose of this paper is the development of an artificial intelligence model for the automatic classification of images, in order to optimize the detection of pathologies through capillaroscopy tests of the nail fold, this technique allows obtaining images of the morphology of the capillaries in the proximal nail fold of the hands. We used a database that consists of 300 images of capillaries corresponding to the nail fold. These images were labeled as healthy or diseased subject depending on the patterns of the capillaries. The method used to classify the images into two classes was transfer learning from a MobileNet V2 base model. The results show that the network is capable of detecting the presence of pathological patterns in the capillaries with a precision of 96.667%.

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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