Skip to main navigation
Skip to search
Skip to main content
Penn State Home
Help & FAQ
Home
Researchers
Research output
Research units
Equipment
Grants & Projects
Prizes
Activities
Search by expertise, name or affiliation
Automated visual inspection and classification using neural networks
Taioun Kim,
Soundar R.T. Kumara
Marcus Department of Industrial and Manufacturing Engineering
Institute for Computational and Data Sciences (ICDS)
Center for Interdisciplinary Mathematics
Research output
:
Contribution to conference
›
Paper
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Automated visual inspection and classification using neural networks'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Neural Network
100%
Automated Visual Inspection
100%
Visual Classification
100%
Hopfield Network
33%
Energy Function
33%
Neural Network Model
33%
Fitting Algorithm
33%
Circular Boundary
33%
Circular Fitting
33%
Continuous Type
33%
Classification Problem
33%
Defect Classification
33%
Pattern Classifier
33%
Traditional Pattern
33%
Classification Scheme
33%
Multi-layer Perception
33%
Computer Science
Visual Inspection
100%
Neural Network
100%
Hopfield Network
33%
Fitting Algorithm
33%
Classification Problem
33%
Energy Function
33%
Neural Network Model
33%
Classification Scheme
33%
Engineering
Visual Inspection
100%
Defect Classification
50%
Classification Problem
50%
Energy Function
50%
Network Model
50%
Classification Scheme
50%