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


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a280642.pdf


Report Date : Jun 1994


Pagination or Media Count : 134


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