Paper Info
Title
Trainable blotch detection on high resolution archive films minimizing the human interaction
Abstract
Film archives are continuously in need of automatic restoration tools to accelerate the correction of film artifacts and to decrease the costs. Blotches are a common type of film degradation and their correction needs a lot of manual interaction in traditional systems due to high false detection rates and the huge amount of data of high resolution images. Blotch detectors need reliable motion estimation to avoid the false detection of uncorrupted regions. In case of erroneous detection, usually an operator has to remove the false alarms manually, which significantly decreases the efficiency of the restoration process. To reduce manual intervention, we developed a two-step false alarm reduction technique including pixel- and object-based methods as post-processing. The proposed pixel-based algorithm compensates motion, decreasing false alarms at low computational cost, while the following object based method further reduces the residual false alarms by machine learning techniques. We introduced a new quality metric for detection methods by measuring the required amount of manual work after the automatic detection. In our novel evaluation technique, the ground truth is collected from digitized archive sequences where defective pixel positions are detected in an interactive process.
Year
DOI
Venue
2010
10.1007/s00138-007-0106-y
Mach. Vis. Appl.
Keywords
Field
DocType
Digital film restoration,Blotch detection,Object classification,Motion estimation
Computer vision,Residual,False alarm,Pattern recognition,Computer science,Human interaction,Ground truth,Artificial intelligence,Pixel,Defective pixel,Motion estimation,Detector
Journal
Volume
Issue
ISSN
21
5
0932-8092
Citations 
PageRank 
References 
3
0.48
11
Authors
3
Name
Order
Citations
PageRank
Attila Licsár1544.80
Sziranyi, T.239544.76
László Czuni36813.41