Abstract
In this paper, the well-known SIFT detector is extended with a bivariate feature localization. This is done by using function models that assume a Gaussian feature shape for the detected features. As function models we propose (a) a bivariate Gaussian and (b) a Difference of Gaussians. The proposed detector has all properties of SIFT, but provides invariance to affine transformations and blurring. It shows superior performance for strong viewpoint changes compared to the original SIFT. Compared to the most accurate affine invariant detectors, it provides competitive results for the standard test scenarios while performing superior in case of motion blur in video sequences.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pollefeys, M., Gool, L.V.V., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., Koch, R.: Visual modeling with a hand-held camera. International Journal of Computer Vision (IJCV) 59, 207–232 (2004)
Brown, M., Lowe, D.G.: Invariant features from interest point groups. In: British Machine Vision Conference (BMVC), pp. 656–665 (2002)
Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 8, 679–698 (1986)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–151 (1988)
Lindeberg, T.: Feature detection with automatic scale selection. International Journal of Computer Vision (IJCV) 30, 79–116 (1998)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (IJCV) 60, 91–110 (2004)
Lindeberg, T., Garding, J.: Shape-adapted smoothing in estimation of 3-d shape cues from affine deformations of local 2-d brightness structure. Image and Vision Computing 15, 415–434 (1997)
Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)
Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal of Computer Vision (IJCV) 60, 63–86 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 27, 1615–1630 (2005)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. International Journal of Computer Vision (IJCV) 65, 43–72 (2005)
Yu, G., Morel, J.M.: A fully affine invariant image comparison method. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Washington, DC, USA, pp. 1597–1600. IEEE Computer Society, Los Alamitos (2009)
Köser, K., Koch, R.: Perspectively invariant normal features. In: IEEE International Conference on Computer Vision (ICCV), pp. 1–8 (2007)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference (BMVC), vol. 1, pp. 384–393 (2002)
Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. In: Foundations and Trends in Computer Graphics and Vision, vol. 3 (2008)
Ke, Y., Sukthankar, R.: Pca-sift: A more distinctive representation for local image descriptors. In: International Conference on Computer Vision and Pattern Recognition (ICCV), pp. 506–513 (2004)
Schmid, C., Mohr, R., Bauckhage, C.: Comparing and evaluating interest points. In: IEEE International Conference on Computer Vision and Pattern Recognition (ICCV), pp. 230–235 (1998)
Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. International Journal of Computer Vision (IJCV) 37, 151–172 (2000)
Mikolajczyk, K., Leibe, B., Schiele, B.: Multiple object class detection with a generative model. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 26–36 (2006)
Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering objects and their location in images. In: IEEE International Conference on Computer Vision, vol. 1, pp. 370–377 (2005)
Vatis, Y., Ostermann, J.: Adaptive interpolation filter for h.264/avc. IEEE Transactions on Circuits and Systems for Video Technology 19, 179–192 (2009)
Cordes, K., Müller, O., Rosenhahn, B., Ostermann, J.: Half-sift: High-accurate localized features for sift. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, Miami Beach, USA, pp. 31–38 (2009)
Fischler, R.M.A., Bolles, C.: Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography. Communications of the ACM 24, 381–395 (1981)
Thormählen, T., Hasler, H., Wand, M., Seidel, H.P.: Merging of feature tracks for camera motion estimation from video. In: 5th European Conference on Visual Media Production (CVMP), pp. 1–8 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cordes, K., Müller, O., Rosenhahn, B., Ostermann, J. (2010). Bivariate Feature Localization for SIFT Assuming a Gaussian Feature Shape. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-17289-2_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17288-5
Online ISBN: 978-3-642-17289-2
eBook Packages: Computer ScienceComputer Science (R0)