Archive Film Defect Detection Based on a Hidden Markov Model

Wang Xiaosong, Majid Mirmehdi

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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 contributionArchive Film Defect Detection Based on a Hidden Markov Model
Original languageEnglish
Title of host publicationProceedings of the 10th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2009)
Publication statusPublished - 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

Fingerprint Dive into the research topics of 'Archive Film Defect Detection Based on a Hidden Markov Model'. Together they form a unique fingerprint.

Cite this