Fundamentals of speech recognition
Fundamentals of speech recognition
A hidden markov model based approach for facial expression recognition in image sequences
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
On graph-associated matrices and their eigenvalues for optical character recognition
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Spectral graph features for the classification of graphs and graph sequences
Computational Statistics
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In this paper, the classification of human activities based on sequences of camera images utilizing hidden Markov models is investigated. In the first step of the proposed data processing procedure, the locations of the person's body parts (hand, head, etc.) and objects (table, cup, etc.) which are relevant for the classification of the person's activity have to be estimated for each camera image. In the next processing step, the distances between all pairs of detected objects are computed and the eigenvalues of this Euclidean distance matrix are calculated. This set of eigenvalues built the input for a single camera image and serve as the inputs to Gaussian mixture models, which are utilized to estimate the emission probabilities of hidden Markov models. It could be demonstrated, that the eigenvalues are powerful features, which are invariant with respect to the labeling of the nodes (if they are utilized sorted by size) and can also deal with graphs, which differ in the number of their nodes.