Skip to main navigation
Skip to search
Skip to main content
Montclair State University Home
Help & FAQ
Home
Profiles
Research units
Core Facilities
Grants/Projects
Research output
Prizes
Press/Media
Search by expertise, name or affiliation
Closed-loop object recognition using reinforcement learning
Jing Peng
, Bir Bhanu
Computer Science
Research output
:
Contribution to journal
›
Conference article
›
peer-review
3
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Closed-loop object recognition using reinforcement learning'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Reinforcement Learning
100%
Object Recognition
100%
Closed-loop
100%
Image Segmentation
100%
Segmentation Parameters
100%
Confidence Level
50%
System Performance
50%
Color Image
50%
Model Matching
50%
Reinforcement Signal
50%
Real-world Application
50%
Evaluation Function
50%
Filter Type
50%
Object Recognition Algorithm
50%
External Conditions
50%
Automatic Generation
50%
Loop Type
50%
Computer Vision System
50%
Learning Automata
50%
Robust Performance
50%
Recognition Algorithm
50%
Computer Science
Object Recognition
100%
Image Segmentation
100%
Reinforcement Learning
100%
Recognition Algorithm
100%
Vision Systems
50%
Systems Performance
50%
Automaton
50%
Matching Model
50%
World Application
50%
Automatic Generation
50%
Confidence Level
50%
Recognition Strategy
50%
Evaluation Function
50%
Computer Vision
50%
Engineering
Reinforcement Learning
100%
Object Recognition
100%
Closed Loop
100%
Computervision
50%
Open Loop
50%
Learning Automaton
50%
Color Image
50%
Matching Model
50%
Reinforcement Signal
50%
Confidence Level
50%
Real World Application
50%
Input Image
50%
Systems Performance
50%
External Condition
50%
Filter Type
50%
Chemical Engineering
Reinforcement Learning
100%