Abstract
One of the first steps in a myriad of Visual Recognition and Computer Vision algorithms is the detection of keypoints. Despite the large number of works proposing image keypoint detectors, only a few methodologies are able to efficiently use both visual and geometrical information. In this paper we introduce KVD (Keypoints from Visual and Depth Data), a novel keypoint detector which is scale invariant and combines intensity and geometrical data. We present results from several experiments that show high repeatability scores of our methodology for rotations, translations and scale changes and also presents robustness in the absence of either visual or geometric information.
This work is supported by grants from CNPq, CAPES and FAPEMIG.
Chapter PDF
Similar content being viewed by others
Keywords
References
Bay, H., Ess, A., Tuytelaars, T., Gool, L.J.V.: Speeded-up robust features (SURF). Computer Vision and Image Understanding 110(3), 346–359 (2008)
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks, Monterey (1984)
Chen, H., Bhanu, B.: 3D free-form object recognition in range images using local surface patches. Pattern Recognition Letters 28(10), 1252–1262 (2007)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference, AVC, pp. 1–6 (1988)
Holzer, S., Shotton, J., Kohli, P.: Learning to efficiently detect repeatable interest points in depth data. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 200–213. Springer, Heidelberg (2012)
Janoch, A., Karayev, S., Jia, Y., Barron, J.T., Fritz, M., Saenko, K., Darrell, T.: A category-level 3-D object dataset: Putting the kinect to work. In: IEEE International Conference on Computer Vision Workshops, ICCV 2011 Workshops, Barcelona, Spain, November 6–13, 2011, pp. 1168–1174 (2011)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60(1), 63–86 (2004)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.J.V.: A comparison of affine region detectors. International Journal of Computer Vision 65(1–2), 43–72 (2005)
Moravec, H.P.: Towards automatic visual obstacle avoidance. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, p. 584 (1977)
do Nascimento, E.R., Oliveira, G.L., Vieira, A.W., Campos, M.F.M.: On the development of a robust, fast and lightweight keypoint descriptor. Neurocomputing 120, 141–155 (2013)
Rosten, E., Porter, R., Drummond, T.: Faster and better: A machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105–119 (2010)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.R.: ORB: an efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, November 6–13, pp. 2564–2571 (2011)
Rusu, R.B., Cousins, S.: 3D is here: Point cloud library (PCL). In: IEEE International Conference on Robotics and Automation, ICRA (2011)
Steder, B., Rusu, R.B., Konolige, K., Burgard, W.: Point feature extraction on 3D range scans taking into account object boundaries. In: IEEE International Conference on Robotics and Automation, ICRA 2011, Shanghai, China, May 9–13, pp. 2601–2608 (2011)
Sturm, J., Magnenat, S., Engelhard, N., Pomerleau, F., Colas, F., Burgard, W., Cremers, D., Siegwart, R.: Towards a benchmark for RGB-D SLAM evaluation. In: Proc. of the RGB-D Workshop on Advanced Reasoning with Depth Cameras at Robotics: Science and Systems Conf (2011)
Zaharescu, A., Boyer, E., Varanasi, K., Horaud, R.: Surface feature detection and description with applications to mesh matching. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 373–380 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Vasconcelos, L.O., Nascimento, E.R., Campos, M.F.M. (2015). A Scale Invariant Keypoint Detector Based on Visual and Geometrical Cues. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-25751-8_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25750-1
Online ISBN: 978-3-319-25751-8
eBook Packages: Computer ScienceComputer Science (R0)