3 research outputs found
Detection of dirt impairments from archived film sequences : survey and evaluations
Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research
Change detection in optical aerial images by a multilayer conditional mixed Markov model
In this paper we propose a probabilistic model for detecting relevant changes in registered aerial image pairs taken with the time differences of several years and in different seasonal conditions. The introduced approach, called the Conditional Mixed Markov model (CXM), is a combination of a mixed Markov model and a conditionally independent random field of signals. The model integrates global intensity statistics with local correlation and contrast features. A global energy optimization process ensures simultaneously optimal local feature selection and smooth, observation-consistent segmentation. Validation is given on real aerial image sets provided by the Hungarian Institute of Geodesy, Cartography and Remote Sensing and Google Earth
Trainable blotch detection on high resolution archive films minimizing the human interaction
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