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|
|Title of host publication||Proceedings of the 20th British Machine Vision Conference, Winner of the Best Industrial Paper Prize|
|Publication status||Published - 2009|
Bibliographical noteOther 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