article

SIFER: Scale-Invariant Feature Detector with Error Resilience

Authors:

Pradip Mainali,

Gauthier Lafruit,

Rudy LauwereinsAuthors Info & Claims

International Journal of Computer Vision, Volume 104, Issue 2

Pages 172 - 197

https://doi.org/10.1007/s11263-013-0622-3

Published: 01 September 2013 Publication History

Abstract

We present a new method to extract scale-invariant features from an image by using a Cosine Modulated Gaussian (CM-Gaussian) filter. Its balanced scale-space atom with minimal spread in scale and space leads to an outstanding scale-invariant feature detection quality, albeit at reduced planar rotational invariance. Both sharp and distributed features like corners and blobs are reliably detected, irrespective of various image artifacts and camera parameter variations, except for planar rotation. The CM-Gaussian filters are approximated with the sum of exponentials as a single, fixed-length filter and equal approximation error over all scales, providing constant-time, low-cost image filtering implementations. The approximation error of the corresponding digital signal processing is below the noise threshold. It is scalable with the filter order, providing many quality-complexity trade-off working points. We validate the efficiency of the proposed feature detection algorithm on image registration applications over a wide range of testbench conditions.

References

[1]

Aanæs, H., Dahl, A., & Steenstrup Pedersen, K. (2011). Interesting interest points: A comparative study of interest point performance on a unique data set. International Journal of Computer Vision, 97(1), 18-35.

[2]

Alahi, A., Ortiz, R., & Vandergheynst, P. (2012). FREAK: Fast Retina Keypoint. In IEEE conference on computer vision and pattern recognition, Providence, RI, USA.

[3]

Bay, H. (2011). SURF implementation. http://www.vision.ee.ethz.ch/~surf/. Accessed 15 Jan 2012.

[4]

Bay, H., Andreas, E., Tuytelaars, T., & Van Gool, L. (2008). Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 110(3), 346-359.

Digital Library

[5]

Beaudet, P. (1978). Rotational invariant image operators. In International conference on pattern recognition, Kyoto, Japan, pp. 579-583.

[6]

Bendale, P., Triggs, B., & Kingsbury, N. (2010). Multiscale keypoint analysis based on complex wavelets. In Proceedings of the British machine vision conference, Aberystwyth, pp. 49.1-49.10.

[7]

Brown, M., & Lowe, D. (2002). Invariant features from interest point groups. In British machine vision conference, Cardiff, pp. 656-665.

[8]

Cornelis, N., & Van Gool, L. (2008). Fast scale invariant feature detection and matching on programmable graphics hardware. In IEEE computer society conference on computer vision and pattern recognition workshops, 2008 (CVPRW'08), Anchorage, AK, USA, pp. 1-8.

[9]

Crete, F., Dolmiere, T., Ladret, P., & Nicolas, M. (2007). The blur effect: perception and estimation with a new no-reference perceptual blur metric. In Human vision and electronic imaging XII (Vol. 6492, p. 64920I). San Jose: Proceedings of the SPIE.

[10]

Daugman, J. (1988). Complete discrete 2-d gabor transforms by neural networks for image analysis and compression. IEEE Transactions on Acoustics, Speech and Signal Processing, 36(7), 1169-1179.

[11]

Deng, H., Zhang, W., Mortensen, E., Dietterich, T., & Shapiro, L. (2007). Principal curvature-based region detector for object recognition. In IEEE conference on computer vision and pattern recognition, Minneapolis, MN, USA, pp. 1-8.

[12]

Deriche, R. (1993). Recursively implementing the gaussian and its derivatives. INRIA: Tech. rep.

[13]

Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395.

Digital Library

[14]

Forstner, W. (1994). A framework for low level feature extraction. In Proceedings of the third European conference, Volume II on computer vision, Stockholm, Sweden: Springer-Verlag New York, Inc, pp. 383-394.

[15]

Gao, X., Sattar, F., & Venkateswarlu, R. (2007). Multiscale corner detection of gray level images based on log-gabor wavelet transform. IEEE Transactions on Circuits and Systems for Video Technology, 17(7), 868-875.

Digital Library

[16]

Harris, C., & Stephens, M. (1988). A combined corner and edge detection. In Proceedings of the fourth alvey vision conference, Manchester, UK, pp. 147-151.

[17]

Hartley, R., & Zisserman, A. (2000). Multiple view geometry in computer vision. (pp. 87-127), Cambridge: Cambridge University Press.

[18]

Horaud, R.P., Skordas, T., & Veillon, F. (1990). Finding geometric and relational structures in an image. In Proceedings of the first European conference on computer vision, Antibes, France, Vol. 427, pp. 374-384.

[19]

Huang, F., Huang, S., Ker, J., & Chen, Y. (2012). High-performance SIFT hardware accelerator for real-time image feature extraction. Circuits and Systems for Video Technology, IEEE Transactions on, 22(3), 340-351.

Digital Library

[20]

Kadir, T., Zisserman, A., & Brady, J. M. (2004). An affine invariant salient region detector. In T. Pajdla & J. Matas (Eds.), European conference on computer vision, Prague, Czech Republic: Springer Berlin Heidelberg.

[21]

Kovesi P (2003) Phase congruency detects corners and edges. In The Australian pattern recognition society conference: DICTA 2003, Sydney, Australia, pp. 309-318.

[22]

Lindeberg, T. (1990). Scale-space for discrete signals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 234-254.

Digital Library

[23]

Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30, 79-116.

[24]

Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91-110.

Digital Library

[25]

Mainali, P., Yang, Q., Lafruit, G., Van Gool, L., & Lauwereins, R. (2010). Robust low complexity corner detector. IEEE Transactions on Circuit and Systems for Video Technology, 21, 87-127.

[26]

Mallat, S. (2008). A wavelet tour of signal processing (3rd ed.). San Diego: Academic Press.

[27]

Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431-441.

[28]

Matas, J., Chum, O., Martin, U., & Pajdla, T. (2002). Robust wide baseline stereo from maximally stable extremal regions. In Proceedings of British machine vision conference Vol. 1, pp. 384-393.

[29]

Maver, J. (2010). Self-similarity and points of interest. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(7), 1211-1226.

Digital Library

[30]

Mikolajczyk, K. (2007). Oxford data set. http://www.robots.ox.ac.uk/ ~vgg/research/affine. Accessed 15 Jan 2012.

[31]

Mikolajczyk, K., & Schmid, C. (2004). Scale and affine invariant interest point detectors. International Journal of Computer Vision, 60(1), 63-86.

Digital Library

[32]

Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 1615-1630.

Digital Library

[33]

Mokhtarian, F., & Suomela, R. (1998). Robust image corner detection through curvature scale space. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12), 1376-1381.

Digital Library

[34]

Moreno, P., Bernardino, A., & Victor, S.J. (2005). Appearance based salient point detection with intrinsic scale-frequency descriptor. In Proc. 5th international conference on visualization, imaging and image processing (VIIP), Benidorm, Spain.

[35]

Neubeck, A., & Van Gool, L. (2006). Efficient non-maximum suppression. In Proc. IEEE international conference on pattern recognition, Hong Kong, Vol. 3, pp. 850-855.

[36]

Rosten, E., Porter, R., & Drummond, T. (2010). Faster and better: A machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1), 105-119.

Digital Library

[37]

Schmid, C., Mohr, R., & Bauckhage, C. (2000). Evaluation of interest point detectors. International Journal of Computer Vision, 37(2), 151-172.

Digital Library

[38]

Shilat, F., Werman, M., & Gdalyahn, Y. (1997). Ridge's corner detection and correspondence. In Proc. IEEE of conference on computer vision and pattern recognition, San Juan, PR, pp. 976-981.

[39]

Smith, S. M., & Brady, J. M. (1997). SUSAN--a new approach to low level image processing. International Journal of Computer Vision, 23(1), 45-78.

Digital Library

[40]

Tack, N., Lambrechts, A., Soussana, P., & Haspeslagh, L. (2012). A compact, high-speed and low-cost hyperspectral imager. In Photonics West, Proc. SPIE Vol. 8266, pp. 82,660Q-82,660Q-13.

[41]

Tola, E., Lepetit, V., & Fua, P. (2008). A fast local descriptor for dense matching. In IEEE conference on computer vision and pattern recognition, Anchorage, AK, pp. 1-8.

[42]

Tomasi, C., & Kanade, T. (1991). Detection and tracking of point features. Tech. Rep. CMU-CS-91-132, Carnegie Mellon University.

[43]

Tuytelaars, T., & Van Gool, L. (2004). Matching widely separated views based on affine invariant regions. International Journal of Computer Vision, 59(1), 61-85.

Digital Library

[44]

Vedaldi, A. (2011). Open source SIFT implementation. http://www.vlfeat.org/~vedaldi/code/siftpp.html. Accessed 15 Jan 2012.

[45]

Young, I., van Vliet, L., & van Ginkel, M. (2002). Recursive gabor filtering. IEEE Transactions on Signal Processing, 50(11), 2798-2805.

Digital Library

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cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 104, Issue 2

September 2013

103 pages

ISSN:0920-5691

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Copyright © Copyright © 2013 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

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Published: 01 September 2013

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https://dl.acm.org/doi/10.1109/TPAMI.2022.3201185
Shen XWang CLi XPeng YHe ZWen CCheng M(2022)Learning scale awareness in keypoint extraction and descriptionPattern Recognition10.1016/j.patcog.2021.108221121:COnline publication date: 1-Jan-2022
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