Markov process in pattern recognition
Using the Markov random process, we developed two new approaches to pattern recognition: (1) Hidden Markov model for modeling spectral features for recognizing 2D shapes. This is because Fourier spectra are suitable for describing 2D shapes of simple closed contours and probabilistic models are capable of coping with random variations in object shapes. We will analyze the properties of spectral features derived from contours of 2D shapes and use these features in 2D pattern recognition. (2) Markov random fields for modeling 2D structural and statistical features. We will give a theoretic analysis of this approach, discuss the issues in the design of neighborhood system and cliques for Markov random field models, and analyze the properties of the models.
We have applied the proposed approach to the recognition of unconstrained handwritten numerals and 2D shapes. Our extensive experimental results show that the proposed approach can achieve a higher performance than that reported recently in the literature.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
Repository Staff Only: item control page