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Change Detection, Differencing.
This work at Control Data Corp. took two real images as input,
warped one to corresponds to the other spatially, and transformed
the intensity values to account for wide area variations.
Subtraction of the images indicated regions of changes. This work
involved the development of real-time special purpose systems to
perform the matching, warping, and differencing for change detection
in a variety of imagery domains (X-ray, radar, and visible light).
Also transform regions of the image based
on intensity and contrast.
The basic algorithm:
(1) For each point on a regular grid in the data base image,
find the maximum correlation value for its neighborhood in the
input image. This system assumes that the images are already
approximately registered, so that the search for the exact matching point is
in a limited area. The processing begins on one edge of the image
and steps across the image, allowing a linkage between adjacent grid
points to determine approximate matches within featureless areas.
Match locations are interpolated to find the
maximum correlation position with accuracy much better than one pixel.
(2) Four grid points forming a square in the data base image map to
four points forming a quadrilateral in the input image.
The points within the quadrilateral are transformed to fit the input
square by interpolation. This basic technique can be refined to find
matches along the sides of the quadrilateral.
(3) A two-dimensional histogram plotting the image intensity value
of an individual pixel in one image versus the value in the second
image (assuming that the two images are rectified spatially) should
lie along the 45o axis. If the mass of points lie along a different
angle, then the intensity values are adjusted. This intensity
rectification is applied over local areas of the image rather than
globally to account for local, but large-scale variations in intensity.
Small anomalies will still appear, but these should
correspond to true differences in the two images, and thus to
changes in the scene.
(4) By subtracting the rectified image from the data base image,
changes between the two views are apparent. An analysis of the
two-dimensional histogram, as used for the intensity rectification,
indicates the type of changes that have occurred (objects added
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Ulstad, M.S.,
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differencing, a non-linear spatial warp and a match of intensity
statistics are computed. This allows for global (or local to a
large area) changes in the contrast and intensity in addition to the
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Stanford AIMemo 144.
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matching, but locate feature points in the first image to limit the
possibilities. Warp the image based on the matching points for
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that match points on a grid in the first image using
correlation values to determine the match.
(2) Globally warp the second image to correspond to the first image.
(3) Subtract the two images to indicate changes and find highlight regions.
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orientations which are not allowed by the early CDC work
(
See also Techniques for Change Detection. and Allen).
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0208
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0301
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WWW Version.
PDF Version.
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Earlier:
BMVC97(212-221).
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Urban Land-Cover Change Detection through Sub-Pixel Imperviousness
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PhEngRS(69), No. 9, September 2003, pp. 1003-1010.
An approach was developed to detect urban land-cover changes by
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IVC(22), No. 2, 1 February 2004, pp. 117-125.
WWW Version.
0402Using graphics model, change detection with large illumination changes.
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WWW Version.
0501
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0501
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0509
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Al-Khudhairy, D.H.A.,
Caravaggi, I.,
Giada, S.,
Structural Damage Assessments from Ikonos Data Using Change Detection,
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PhEngRS(71), No. 7, July 2005, pp. 825-838.
Classical change detection methods, object-oriented image
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WWW Version.
0509
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Chadwick, J.[John],
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0509
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Bovolo, F.,
Bruzzone, L.,
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GeoRS(43), No. 12, December 2005, pp. 2963-2972.
WWW Version.
0512
BibRef
Earlier:
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Multitemporal SAR Images,
ICIP05(I: 665-668).
WWW Version.
0512
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Schmid, T.,
Koch, M.,
Gumuzzio, J.,
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0512
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Ranney, K.I.,
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0601
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Carincotte, C.,
Derrode, S.,
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0603
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0604
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Lakshmi, V.,
Jackson, T.J.,
High-Resolution Change Estimation of Soil Moisture Using L-Band
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WWW Version.
0606
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Gamba, P.,
Dell'Acqua, F.,
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Change Detection of Multitemporal SAR Data in Urban Areas Combining
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Serpico, S.B.,
Generalized Minimum-Error Thresholding for Unsupervised Change
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0609
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Mercier, G.,
Moser, G.,
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Conditional Copulas for Change Detection in Heterogeneous Remote
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0804
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Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
2-D Points with 2-D Structures, Point Matching .