Repeatability Measurements for 2D Interest Point Detectors on 3D Models
Interest point detectors typically operate on 2D images, yet these frequently constitute projections of real 3D scenes . Analysing and comparing the performance of these detectors as to their utility at tracking points in a 3D space is challenging. This paper demonstrates a virtual 3D environment which can measure the repeatability of detected interest points accurately and rapidly. Real-time 3D transform tools enable easy implementation of complex scene evaluations without the time-cost of a manual setup or mark-up. Nine detectors are tested and compared using evaluation and testing methods based on Schmid . Each detector is tested on 34 textured and untextured models that are either scanned from physical objects or modelled by an artist. Rotation in the X, Y, and Z axis as well as scale transformations are tested on each model, with varying degrees of artificial noise applied. Results demonstrate the performance variability of different interest point detectors under different transformations and may assist researchers in deciding on the correct detector for their computer vision application.
KeywordsInterest Point Rest Position Computer Vision Application Interest Point Detector Feature Point Detector
Unable to display preview. Download preview PDF.
- 1.Beaudet, P.: Rotationally invariant image operators. In: Proc. Intl. Joint Conf. on Pattern Recognition, pp. 579–583 (1978)Google Scholar
- 2.Carlo Tomasi, T.K.: Detection and tracking of point features. In: Carnegie Mellon University Tech. Rpt (1991)Google Scholar
- 3.Förstner, W.: A feature based correspondence algorithms for image matching. Intl. Arch. Photogrammetry and Remote Sensing 24, 160–166 (1986)Google Scholar
- 6.Guillaume Gals, S.C., Crouzil, A.: Complementarity of feature point detectors. Intl. Joint Conf. on Comp. Vis. Theory and App. (2010)Google Scholar
- 7.Harris, C., Stephens, M.: A combined corner and edge detector (1988)Google Scholar
- 8.Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, New York (2003)Google Scholar
- 10.Köthe, U.: Generische programmierung für die bildverarbeitung. PhD Thesis, Universität Hamburg (2000)Google Scholar
- 14.Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: IEEE Intl. Conf. on Comp. Vis., vol. 2, pp. 1508–1511 (October 2005)Google Scholar
- 15.Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: European Conf. on Comp. Vis., vol. 1, pp. 430–443 (May 2006)Google Scholar