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Framework for automatic condition assessment of culverts
Jay N. Meegoda
, Thomas M. Juliano
,
Amit Banerjee
School of Science, Engineering & Technology (Harrisburg)
Research output
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Contribution to journal
›
Article
›
peer-review
10
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Scopus citations
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Computer Science
Depth Information
100%
Memory Requirement
100%
Segmentation Process
100%
Segmentation Model
100%
Shape Information
100%
Image Segmentation
100%
Real-Time Application
100%
Point Condition
100%
Circuit Television
100%
Fuzzy Clustering
100%
Consecutive Frame
100%
System Requirement
100%
Keyphrases
Condition Assessment
100%
Culvert
100%
Fuzzy Clustering
33%
Time Monitoring
33%
Manual Inspection
33%
Innovative Approach
33%
Condition State
33%
Damage Area
33%
Pixel Space
33%
Elimination Method
33%
Region-based
33%
Memory Requirements
33%
Assessment Methodology
33%
Human Evaluation
33%
Time Application
33%
Image Frame
33%
Video Inspection
33%
Image Segmentation
33%
Assessment Scale
33%
System Requirements
33%
Defect Shape
33%
Internal Defects
33%
Background Removal
33%
Closed-circuit Television
33%
Adaptive Adjustment
33%
Automated Monitoring
33%
Point Conditions
33%
Defect Depth
33%
Shape Detection
33%
Step Detection
33%
Depth Information
33%
Engineering
Condition Assessment
100%
System Requirement
33%
Consecutive Frame
33%
Memory Requirement
33%
Damage Area
33%
Closed Circuit
33%
State Condition
33%
Frame Analysis
33%