Experiments on Robust Image Registration Using a Markov-Gibbs Appearance Model
Conference paper
Abstract
A new approach to align an image of a textured object with a given prototype under its monotone photometric and affine geometric transformations is experimentally compared to more conventional registration algorithms. The approach is based on measuring similarity between the image and prototype by Gibbs energy of characteristic pairwise co-occurrences of the equalized image signals. After an initial alignment, the affine transformation maximizing the energy is found by gradient search. Experiments confirm that our approach results in more robust registration than the search for the maximal mutual information or similarity of scale-invariant local features.
Keywords
Image Registration Scale Invariant Feature Transform Appearance Model Gradient Search Texture Object
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References
- 1.Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar
- 2.Holm, M.: Towards automatic rectification of satellite images using feature based matching. In: Proc. Int. Geoscience and Remote Sensing Symp., IGARSS 1991, Espoo, Finland, pp. 2439–2442 (1991)Google Scholar
- 3.Hsieh, J.W., Liao, H.Y.M., Fan, K.C., Ko, M.T., Hung, Y.P.: Image registration using a new edge-based approach. Computer Vision and Image Understanding 67, 112–130 (1997)CrossRefGoogle Scholar
- 4.Sester, M., Hild, H., Fritsch, D.: Definition of ground control features for image registration using GIS data. In: Proc. Symp. on Object Recognition and Scene Classification from Multispectral and Multisensor Pixels, CD-ROM, Columbus, Ohio (1998)Google Scholar
- 5.Roux, M.: Automatic registration of SPOT images and digitized maps. In: Proc. IEEE Int. Conf. on Image Processing ICIP 1996, Lausanne, Switzerland, pp. 625–628 (1996)Google Scholar
- 6.Hsieh, Y.C., McKeown, D.M., Perlant, F.P.: Performance evaluation of scene registration and stereo matching for cartographic feature extraction. IEEE Trans. Pattern Analysis and Machine Intelligence 14, 214–237 (1992)CrossRefGoogle Scholar
- 7.Dai, X., Khorram, S.: Development of a feature-based approach to automated image registration for multitemporal and multisensor remotely sensed imagery. In: Proc. Int. Geoscience and Remote Sensing Symp., IGARSS 1997, Singapore, pp. 243–245 (1997)Google Scholar
- 8.Shin, D., Pollard, J.K., Muller, J.P.: Accurate geometric correction of ATSR images. IEEE Trans. Geoscience and Remote Sensing 35, 997–1006 (1997)CrossRefGoogle Scholar
- 9.Mendoza, E.H., Santos, J.R., Rosa, A.N.C.S., Silva, N.C.: Land Use/land Cover Mapping in Brazilian Amazon Using Neural Network with Aster/terra Data. In: Proc. Geo-Imagery Bridging Continents, Istanbul, Turkey, pp. 123–126 (2004)Google Scholar
- 10.Growe, S., Tonjes, R.: A knowledge based approach to automatic image registration. In: Proc. IEEE Int. Conf. on Image Processing ICIP 1997, Santa Barbara, California, pp. 228–231 (1997)Google Scholar
- 11.Ton, J., Jain, A.K.: Registering landsat images by point matching. IEEE Trans. Geoscience and Remote Sensing 27, 642–651 (1989)CrossRefGoogle Scholar
- 12.Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. J. of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
- 13.Pope, Theiler, J.: Automated Image Registration (AIR) of MTI Imagery. In: Proc. SPIE 5093, vol. 27, pp. 294–300 (2003)Google Scholar
- 14.Foroosh, H., Zerubia, J.B., Berthod, M.: Extension of phase correlation to subpixel registration. IEEE Trans. Image Processing 11, 188–200 (2002)CrossRefGoogle Scholar
- 15.Viola, P.: Alignment by Maximization of Mutual Information. Ph.D. dissertation, MIT, Cambridge, MA (1995)Google Scholar
- 16.Thevenaz, P., Unser, M.: Alignment An efficient mutual information optimizer for multiresolution image registration. In: Proc. IEEE Int. Conf. on Image Processing ICIP 1998, Chicago, USA, pp. 833–837 (1998)Google Scholar
- 17.Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32, 71–86 (1999)CrossRefGoogle Scholar
- 18.Gimelfarb, G., Farag, A.A.: Texture Analysis by accurate identification of simple Markov models. Cybernetics and Systems Analysis 41(1), 37–49 (2005)Google Scholar
- 19.Gimelfarb, G., Farag, A.A., El-Baz, A.: Expectation-Maximization for a linear combination of Gaussians. In: Proc. of 18th IAPR Int. Conf. on Pattern Recognition (ICPR 2004), Cambridge, UK, pp. 422–425 (August 2004)Google Scholar
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