Carmina et fragmenta by HERMANN, G.


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One of the main weaknesses of Lowe’s algorithm is the use of just a simple probabilistic hypothesis rejection stage, which cannot successful reduce the number of false positives. Loncomilla and Ruiz-del-Solar (L&R) propose a system that reduces largely the number of false positives by using several hypothesis rejection stages [15][21]. This includes a fast probabilistic hypothesis rejection stage, a linear correlation verification stage, a geometrical distortion verification stage, a pixel correlation verification stage, a transformation fusion procedure, and the use of the RANSAC algorithm and a semi-local constraints test.

IEEE Trans. Pattern Anal. Machine Intell. 27(10), 1615–1630 (2005) 18. : Multi-Spectral Face Recognition - Fusion of Visual Imagery with Physiological Information. In: Face Biometrics for Personal Identification: MultiSensory Multi-Modal Systems, pp. 91–108. Springer, Heidelberg (January 2007) 19. : ‘Face Recognition Beyond the Visible Spectrum. In: Advances in Biometrics: Sensors, Algorithms and Systems, pp. 157–180. Springer, Heidelberg (October 2007) 20. : Face Recognition using Thermal Infrared Images for Human-Robot Interaction Applications: A Comparative Study.

IEE Press, New York (2001) 14. : Distinctive Image Features from Scale-Invariant Keypoints. Int. Journal of Computer Vision 60(2), 91–110 (2004) 15. : A Fast Probabilistic Model for Hypothesis Rejection in SIFT-Based Object Recognition. , Kittler, J. ) CIARP 2006. LNCS, vol. 4225, pp. 696–705. Springer, Heidelberg (2006) 16. : Scale & Affine Invariant Interest Point Detectors. Int. Journal of Computer Vision 60(1), 63–96 (2004) 17. : A performance evaluation of local descriptors. IEEE Trans. Pattern Anal.

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