Automatic Classification of Microseismic Signals Related to Mining Activities by Supervised Learning

2019 ◽  
Author(s):  
W.C. Choi ◽  
C. Kim ◽  
D. Cheon ◽  
S. Pyun
Keyword(s):  
Supervised Learning ◽  
Automatic Classification ◽  
Mining Activities

Supervised Neural Networks for the Automatic Classification of Leukocytes in Blood Microscope Images

2013 ◽  
Vol 479-480 ◽  
pp. 491-495 ◽  
Author(s):  
Sheng Fuu Lin ◽  
Chien Hao Tseng ◽  
Chung I Huang
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Supervised Learning ◽  
Automatic Classification ◽  
Learning System ◽  
Automated Segmentation ◽  
Classification Learning ◽  
Microscope Images ◽  
Traditional Pattern

In this paper, the application of the supervised learning system to automatic classification of leukocytes processing for the microscopic images analysis is presented. The traditional pattern classification in cellular images is typically made by experienced operators. Such procedures may present a non-standard and unstable accuracy when it depends on the operator’s capabilities and tiredness. In this study, we propose the supervised learning system to achieve an automated segmentation and classification of leukocytes based on supervised neural networks and image processing methods. The experimental results show that the proposed automatic classification learning system can effectively classify the five types of the leukocytes in microscopic cell images, as well as to compare the classification results to those obtained by the medical experts.

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).

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

An embedded system for automatic classification of neonatal cry

2013 ◽  
Author(s):  
Biswanath Saha ◽  
Parimal Kumar Purkait ◽  
Jayanta Mukherjee ◽  
Arun Kumar Majumdar ◽  
Bandana Majumdar ◽  
...  
Keyword(s):  
Embedded System ◽  
Automatic Classification
Sign in / Sign up

Export Citation Format

Share Document