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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
A SEMIAUTOMATIC ANOMALOUS CHANGE DETECTION METHOD FOR
MONITORING AIMS
G.Artese*, V. Achilli?, M. Fabris", M.Perrelli*
* Land Planning Dept. - University of Calabria - Ponte Bucci cubo 45B - 87036 - Rende - Italy
(g.artese, marcello.perrelli)@unical.it
5 s9** Dept. - University of Padua - Via Marzolo **
Commission VII, WG VII/5
KEY WORDS: Change Detection, Image Processing, Registration, Cultural Heritage, Camera Phone.
ABSTRACT:
In the framework of the development of a web site devoted to the documentation and monitoring of the cultural heritage (above all
monumental buildings), a semiautomatic method for anomalous change detection has been set up. The method uses the grouping of
the image difference values, to detect both small and diffused changes. Three tests are described to evaluate the performances of the
method. The results show that good performances are obtained in case of cloudy days, while the presence of shadows requires the
interpretation of an operator to distinguish true and false changes.
1. INTRODUCTION
In the framework of the development of a web site devoted to
the documentation and monitoring of the cultural heritage
(above all monumental buildings), with particular regard to
emergency management, anomaly detection and early warning
(Artese,G., Gencarelli,M., 2008), a semiautomatic method for
anomalous change detection has been set up. The system uses
images captured and sent by camera phones; these images are
compared with archived ones to detect anomalous changes and,
consequently, to activate an early warning procedure.
The availability of high resolution digital cameras combined
with the possibilities offered by today's computers, both for
camera calibration and image processing, allows the execution
of controls and monitoring by using change detection
techniques, based on the comparison of frames acquired at
different times.
The use of these techniques is widespread in various sectors,
ranging from security to traffic monitoring, to the processing of
radiographic images, etc.. .
The issues involved range from the calibration of the cameras
used, to the feature extraction, registration, and actual change
detection.
Some authors have proposed techniques that do not require
prior calibration and optimal resampling (registration) and make
use of the classification process of pixels (Theiler, J., Perkins, S.,
2006).
1.1 Calibration
Many algorithms have been proposed for automatic calibration
of the images. For example,
Cronk et al. (Cronk, S., 2006) have proposed an effective
method in the case of many converging acquisitions. Another
possibility is to consider non-rigid geometric deformations, due
to lens distortion; for such a case Arsigny et al. (Arsigny, V.,
2006) have proposed a general methodology to parameterize the
deformation of the image with a finite number of rigid or affine
components, while maintaining the reversibility of the global
deformation.
Considering the presence of flat surfaces and straight lines,
different strategies can be followed. Habib et al. (Habib, AS,
2002, Habib, AS, 2004, Habib, AS, 2005) have proposed a
method for both calibration and registration, based on the use of
straight lines.
In the case of monitoring, internal and external orientation
parameters are known, at least for a base image.
In our work, straight lines were used during calibration of the
camera lens to eliminate distortion, after having obtained the
outlines with classical technique (Canny, J., 1986).
1.2 Identification of interest points
To obtain a good image registration, you must choose a set of
interest points, which must be visible, and whose image
coordinates must be known or measurable. For monitoring
aims, one has in general the availability of one or more images
in which interest points are detected through automatic or
manual techniques. The classical operators Forstner (Forstner,
W., 1987) Harris (Harris, CG, 1988) and Moravec (Moravec,
HP, 1979) can be used effectively. In our work we used a
semiautomatic technique, along with the operators of Harris and
Moravec.
1.3 Cross-correlation
To perform registration, of fundamental importance for change
detection, it is essential to find, in the images obtained at later
dates, the interest points which were chosen in the base
frames. For this purpose you can use the matching
techniques. Among these, the cross-correlation is simple and
effective (Jaehne, B., 1989). The cross-correlation allows to
obtain the correspondence between two digital images, based on
two assumptions: the images differ geometrically only due to a
translation and radiometrically only for brightness and
contrast. The accuracy of the cross-correlation decreases rapidly
when the geometric assumptions are not met, especially when
you have rotations greater than 20? or differences in scale
greater than 30% (Forstner, W., 1984).
The different perspectives of the images to compare cause both
translations and rotations. For the buildings, it's in general