possible to individuate target points on vanishing lines; in this
case you can use a coarse cross-correlation to derive the
transformation of the known targets (if you have four points and
two vanishing lines). Thus, after processing of the second
image, the cross-correlation can be performed with subpixel
accuracy (Artese, G., 20072).
1.4 Image processing and Change Detection
Many widespread programs allow processing of images,
varying the brightness and contrast, and resampling after
rotations. Regarding the resolution, the optimum depends on the
minimum size of features that identify a change. In the case of
cracks on the walls of buildings, it is essential a very high
resolution, whereas in case of monitoring of degradation of the
external plaster, it is preferable to have a normal resolution, to
minimize computation time and memory use.
For the change detection several techniques can be used. The
simplest one you can effectively use is image difference. There
are many factors that influence the result of a change detection:
among the main ones we remember the average resolution of
the images to compare, the not uniform resolutions in different
parts of images, the differences due to different points of view,
the environmental conditions at the time of the shots (variations
in brightness, shadows, height of the sun, etc. ..), the presence of
hidden parts.
The ideal situation is with the same camera, point of the shot
and identical environmental conditions. It is obvious that this
situation almost never occurs. Even if the camera is fixed to a
support, the image registration is necessary.
Strictly speaking, one should speak of anomalous change
detection, that is of changes non diffused in the entire image due
to various causes (focus, different lighting conditions, etc..). In
fact, the number of variations which one is really interested in
identifying, is generally low, and the variations involve a few
pixels. Most of pervasive differences are not interesting; often
this differences are due to misregistration.
An overview of anomalous change detection methods is given
by Theiler (Theiler, J., 2008).
2. PREPROCESSING AND MUTUAL INFORMATION
REGISTRATION
Due to the different characteristics of the images, a registration
process has to be previously done. For this aims, a semi-
automatic processing of the archived image is performed: i) the
Canny filter is applied, for edge extraction, ii) Harris (Harris,
C.G., Stephens, M., 1988), Moravec (Moravec, H.P., 1979) and
Forstner (Forstner, W., Guelch, E., 1987) operators are used to
obtain corners and interest points, iii) Straight lines are selected,
iiii) a 'divide and conquer' strategy is applied and a Constrained
Delaunay Triangulation is performed. In this way, every
triangular zone of the image corresponds to a plane region of a
building facade.
It is presumed that every camera phone shot is obtained from a
point of view close to the one of the corresponding archived
image. On the camera phone images, after the application of the
Canny filter, the straight lines are used for the calibration.
Through a cross-correlation, the triangles are found,
corresponding to the ones obtained in the archived image, by
using the constrained Delaunay triangulation.
To the sent image, a piecewise affine transformation is applied.
To detect the real anomalous changes, a radiometric registration
should be performed, followed by the evaluation of the image
differences. The registration is obtained using the well known
mutual information value (Shannon, C.E., 1948):
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
IMgs(r,s) — sum ( Pas (r,s) log (Pas (rs)! Pg (r) Ps(s)) (1)
where r and s are the pixel values from R and S images, PR(r)
and PS(s) the probability distributions of r and s in each image,
and PRS(r,s) the joint probability distribution of r and s.
Thus, the minimum anomalousness registration can be used
(Wohlberg, B., Theiler, J., 2009, Wohlberg, B., Theiler, J.,
2010).
This procedure is generally sufficient in case of concentrated
changes (e.g. cracks on the walls). It works not properly when
the changes are diffused, like in case of deterioration of a plaster
zone in a facade. In this cases, a different strategy must be
followed.
3. SEMIAUTOMATIC CHANGE DETECTION
If we simply take into account small outliers, we can think of an
approach as Wohlberg and Theiler (Wohlberg, B., Theiler, J.,
2010): you can compare the two images to be analyzed after
subtracting the mean value for each pixel of the image, or
portion of the image considered.
This is not good, obviously, in case of differences that affect
large areas, e.g. a large area of deteriorated plaster with a solid
color: the anomalous change could then consist of a widespread
change of an entire area, better described, e.g., by a linear
regression, as reported by Heo and FitzHugh (Heo, J,
FitzHugh, T., 2000).
To obtain the two coefficients (intercept and slope) one should
have some sample areas; in which case you must know how
various conditions of sun radiation and position (season, time
and weather) influence radiometric properties and cause
shadows.
If sample areas are not available, one can think to generate a
georeferenced 3D model of the buildings, thus obtaining the
shadows and the light intensity in the case of sunny days, but
there is an unknown due to weather conditions (cloud cover
may be thick or just a haze, etc. ..). In practice this is a very
difficult way.
In this work, a recursive procedure has been used, to find the
radiometric parameters, for which the maximum number of
pixel with equal radiometric values is obtained. In this way,
changes characterized by small values, but diffused on a large
area are detected.
The image difference (new - base) is performed; the difference
values are grouped thus obtaining a histogram for a grey scale
image, or three histograms for a colour image. The shape of the
histogram shows, in general, one or more peaks; once obtained
the radiometric value of the maximum peak, a shift equal to this
value is applied to the pixels of the new image. The procedure
can be repeated for the red, green and blue channels of a colour
image
The difference between the resampled new image and the base
image is then performed and a non maxima suppression (high
pass filter) is applied. The remaining non zero pixels are
considered as changes in the original image; a BW image is
then obtained, where the white pixels correspond to the
changes. After morphological operations (Canny filter,
dilatation, filling and erosion) a segmentation is performed and
the anomalous areas are classified. For every area, several
properties can be extracted (centroid, area in pixels, etc..);
among these, area and eccentricity can be useful for our aims. In
fact, areas with less than 100 pixels can be generally considered
as noises and eliminated; for a crack this isn’t true, but in this
case the value of the eccentricity is very high, generally greater
than 0.95. The filtering is then performed by eliminating the
areas with less than 100 pixels and an eccentricity less than
0.96.
