Abstract
We propose a novel statistical approach to detect defects in
digitized archive film by using temporal information across
a number of frames modeled with an HMM. The HMM is
trained for normal observation sequences and then applied
within a framework to detect defective pixels by examining
each new observation sequence and its subformations via a
leave-one-out process. We compare against state-of-the-art
results to demonstrate that the proposed method achieves better
detection rates, with fewer false alarms.
| Translated title of the contribution | Archive Film Defect Detection Based on a Hidden Markov Model |
|---|---|
| Original language | English |
| Title of host publication | Proceedings of the 10th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2009) |
| Publication status | Published - 2009 |
Bibliographical note
Other page information: -Conference Proceedings/Title of Journal: Proceedings of the 10th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2009)
Other identifier: 2001027