Perception-based automatic classification of impulsive-source active sonar echoes

2007 ◽  
Vol 122 (3) ◽  
pp. 1502-1517 ◽  
Author(s):  
Victor W. Young ◽  
Paul C. Hines
Keyword(s):  
Automatic Classification ◽  
Active Sonar ◽  
Sonar Echoes ◽  
Impulsive Source

Classification of impulsive-source active-sonar echoes based on a model of auditory perception

2007 ◽  
Vol 122 (5) ◽  
pp. 3088
Author(s):  
Jason E. Summers ◽  
Charles F. Gaumond ◽  
Derek Brock ◽  
Ralph N. Baer
Keyword(s):  
Auditory Perception ◽  
Active Sonar ◽  
Sonar Echoes ◽  
Impulsive Source

Aural classification of impulsive‐source active sonar echoes

2006 ◽  
Vol 119 (5) ◽  
pp. 3394-3395 ◽  
Author(s):  
James Pitton ◽  
Scott Philips ◽  
Les Atlas ◽  
James Ballas ◽  
Derek Brock ◽  
...  
Keyword(s):  
Active Sonar ◽  
Sonar Echoes ◽  
Impulsive Source

Perceptual dimensions of impulsive‐source active sonar echoes

2006 ◽  
Vol 120 (5) ◽  
pp. 3125-3125
Author(s):  
Jason E. Summers ◽  
Derek Brock ◽  
Brian McClimens ◽  
Charles F. Gaumond ◽  
Ralph N. Baer
Keyword(s):  
Active Sonar ◽  
Sonar Echoes ◽  
Impulsive Source

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.

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