Sequential Monte Carlo Video Text Segmentation

This paper presents a probabilistic algorithm for segmenting text embedded in video based on Monte Carlo sampling. The algorithm approximates the posterior of segmentation thresholds of video text by a set of weighted samples, referred to as particles. The set of samples is initialized by applying a traditional segmentation algorithm on the first video frame and further refined by random sampling under a temporal Bayesian framework. Results on a database of 6944 images demonstrated the validity of the algorithm.


Published in:
ICIP
Presented at:
ICIP
Year:
2003
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 Record created 2006-03-10, last modified 2018-03-17

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