Abstract
We describe two image matching techniques that owe their success to a combination of geometric and photometric constraints. In the first, images are matched under similarity transformations by using local intensity invariants and semi-local geometric constraints. In the second, 3D curves and lines are matched between images using epipolar geometry and local photometric constraints. Both techniques are illustrated on real images.
We show that these two techniques may be combined and are complementary for the application of image retrieval from an image database. Given a query image, local intensity invariants are used to obtain a set of potential candidate matches from the database. This is very efficient as it is implemented as an indexing algorithm. Curve matching is then used to obtain a more significant ranking score. It is shown that for correctly retrieved images many curves are matched, whilst incorrect candidates obtain very low ranking.
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References
T.O. Binford and T.S. Levitt. Quasi-invariants: Theory and exploitation. In darpa Image Understanding Workshop, pages 819–829, 1993.
R.C. Bolles and R. Horaud. 3DPO: A three-dimensional Part Orientation system. The International Journal of Robotics Research, 5(3):3–26, 1986.
R.C.K. Chung and R. Nevatia. Use of monocular groupings and occlusion analysis in a hierarchical stereo system. In Computer Vision and Pattern Recognition, pages 50–56, 1991.
L.M.T. Florack, B. ter Haar Romeny, J.J Koenderink, and M.A. Viergever. General intensity transformation and differential invariants. Journal of Mathematical Imaging and Vision, 4(2):171–187, 1994.
B.V. Funt and G.D. Finlayson. Color constant color indexing. Transactions on Pattern Analysis and Machine Intelligence, 17(5):522–529, 1995.
C. Harris and M. Stephens. A combined corner and edge detector. In Alvey Vision Conference, pages 147–151, 1988.
P. Havaldar and G. Medioni. Segmented shape descriptions from 3-view stereo. In International Conference on Computer Vision, pages 102–108, 1995.
A. Huertas and R. Nevatia. Detecting changes in aerial views of man-made structures. In International Conference on Computer Vision, pages 73–80, 1998.
J.J. Koenderink and A.J. van Doorn. Representation of local geometry in the visual system. Biological Cybernetics, 55:367–375, 1987.
Y. Lamdan and H.J. Wolfson. Geometrich ashing: a general and efficient modelbased recognition scheme. In International Conference on Comuter Vision, pages 238–249, 1988.
K. Nagao. Recognizing 3D objects using photometric invariant. In International Conference on Computer Vision, pages 480–487, 1995.
S.B. Pollard, J.E.W. Mayhew, and J.P. Frisby. PMF: A stereo correspondence algorithm using a disparity gradient constraint. Perception, 14:449–470, 1985.
C. Schmid. Appariement d’images par invariants locaux de niveaux de gris. Thèse de doctorat, Institut National Polytechnique de Grenoble, 1996.
C. Schmid and R. Mohr. Local grayvalue invariants for image retrieval. Transactions on Pattern Analysis and Machine Intelligence, 19(5):530–534, May 1997.
C. Schmid, R. Mohr, and Ch. Bauckhage. Comparing and evaluating interest points. In International Conference on Computer Vision, 1998.
C. Schmid and A. Zisserman. Automatic line matching across views. In Conference on Computer Vision and Pattern Recognition, pages 666–671, 1997.
C. Schmid and A. Zisserman. The geometry and matching of curves in multiple views. In European Conference on Computer Vision, 1998.
M.J. Swain and D.H. Ballard. Color indexing. International Journal of Computer Vision, 7(1):11–32, 1991.
P. H. S. Torr and D. W. Murray. Outlier detection and motion segmentation. In Proc SPIE Sensor Fusion VI, pages 432–443, Boston, September 1993.
P. H. S. Torr and A. Zisserman. Robust computation and parameterization of multiple view relations. In International Conference on Computer Vision, pages 727–732, 1998.
A.P. Witkin. Scale-space filtering. In International Joint Conference on Artificial Intelligence, pages 1019–1023, 1983.
G. Xu and Z. Zhang. Epipolar Geometry in Stereo, Motion and Object Recognition. Kluwer Academic Press, 1996.
Y. Zhang and J.J. Gerbrands. Method for matching general stereo planar curves. Image and Vision Computing, 13(8):645–655, October 1995.
Z. Zhang, R. Deriche, O. Faugeras, and Q.T. Luong. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence, 78:87–119, 1995.
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© 1999 Springer-Verlag Berlin Heidelberg
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Schmid, C., Mohr, R., Zisserman, A. (1999). Integrating Geometric and Photometric Information for Image Retrieval. In: Shape, Contour and Grouping in Computer Vision. Lecture Notes in Computer Science, vol 1681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46805-6_13
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DOI: https://doi.org/10.1007/3-540-46805-6_13
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