Accession Number:

ADA379426

Title:

Multistategy Learning for Computer Vision

Descriptive Note:

Final rept 1 Jul 95-30 Jun 98

Corporate Author:

CALIFORNIA UNIV RIVERSIDE COLL OF ENGINEERING

Personal Author(s):

• Bhanu, Bir

Report Date:

1998-09-28

Pagination or Media Count:

11.0

Abstract:

Current IU algorithms and systems lack the robustness to successfully process imagery acquired under real-world scenario. They do not provide the necessary consistency, reliability and predictability of results. Robust 3-D object recognition, in practical applications, remains one of the important but elusive goals of IU research. With the goal of achieving robustness, our research at UCR is directed towards learning parameters, feedback, contexts, features, concepts, and strategies of IU algorithms for model-based object recognition. Our multi strategy learning-based approach is to selectively apply machine learning techniques at multiple levels to achieve robust recognition performance. At each level, appropriate evaluation criteria are employed to monitor the performance and self-improvement of the system. The results of our research are being applied in automatic target recognition, autonomous navigation, and image and video databases.

Descriptors:

• *IMAGES
• *DATA ACQUISITION
• *COMPUTER VISION
• *COMPUTER AIDED INSTRUCTION
• DATA BASES
• ALGORITHMS
• SCENARIOS
• METHODOLOGY
• PREDICTIONS
• PARAMETERS
• TARGET RECOGNITION
• LEARNING MACHINES
• RELIABILITY
• RECOGNITION
• AUTONOMOUS NAVIGATION
• VIDEO SIGNALS
• LEARNING
• AUTOMATIC

Subject Categories:

• Computer Programming and Software
• Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE