Abstract
We propose a novel probabilistic approach to detect defects in digitized archive film,
by combining temporal and spatial information across a number of frames. An 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. A two-stage false alarm elimination process is then applied on the
resulting defect maps, comprising MRF modelling and localised feature tracking, which
impose spatial and temporal constraints respectively. The proposed method is compared
against state-of-the-art and industry-standard methods to demonstrate its superior detection
rate.
Translated title of the contribution | HMM based Archive Film Defect Detection with Spatial and Temporal Constraints |
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Original language | English |
Title of host publication | Proceedings of the 20th British Machine Vision Conference, Winner of the Best Industrial Paper Prize |
Publication status | Published - 2009 |
Bibliographical note
Other page information: -Conference Proceedings/Title of Journal: Proceedings of the 20th British Machine Vision Conference, Winner of the Best Industrial Paper Prize
Other identifier: 2001028