Accession Number:

ADA280642

Title:

Spatio-Temporal Pattern Recognition Using Hidden Markov Models

Descriptive Note:

Doctoral thesis

Corporate Author:

AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH

Personal Author(s):

• Fielding, Kenneth H.

Report Date:

1994-06-01

Pagination or Media Count:

134.0

Abstract:

A new spatio-temporal method for identifying 3D objects found in 2D image sequences is presented. The Hidden Markov Model technique is used as a spatio-temporal classification algorithm to identify 3D objects by the temporal changes in observed shape features. A new information theoretic argument is developed that proves identifying objects based on image sequences can lead to higher classification accuracies than single look methods. A new distance measure is proposed that analyzes the performance of Hidden Markov Models in a multi-class pattern recognition problem. A three class problem identifying moving light display objects provides experimental verification of the sequence processing argument. Individual frames of a MLD image sequence contain very little spatial information. The single look classification rate for the moving light display imagery was observed to be near 50. In contrast, the Hidden Markov Model classification rate was above 93 . The alternate nearest neighbor multiple frame technique classification rate was 20 below the Hidden Markov Models. A one sided t-test revealed a highly statistically significant difference between the Hidden Markov Model and multiple frame technique at a 0. 01 level of significance. A five class problem consisting of tactical military ground vehicles is considered to provide verification using imagery with both spatial and temporal information. Results confirmed the new spatio-temporal pattern recognition method produces superior results by accessing the temporal information in the image sequences. A prototype automatic target recognition system is demonstrated. Hidden Markov model, Pattern recognition, Motion analysis, Signal processing.

Descriptors:

• *TARGET RECOGNITION
• *PATTERN RECOGNITION
• TEST AND EVALUATION
• ALGORITHMS
• SIGNAL PROCESSING
• IMAGE PROCESSING
• THESES
• OPTICAL IMAGES
• THREE DIMENSIONAL
• IMAGE MOTION COMPENSATION
• AUTOMATIC
• FRAMES
• GROUND VEHICLES
• SEQUENTIAL ANALYSIS
• PROTOTYPES
• SHAPE
• SPATIAL DISTRIBUTION
• VERIFICATION
• STATISTICAL TESTS

Subject Categories:

• Cybernetics
• Optics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE