The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.06 - June (2010 vol.32)
pp: 1141-1147
Marco Loog , Delft University of Technology, Delft
François Lauze , University of Copenhagen, Copenhagen
ABSTRACT
An elementary characterization of the map underlying Harris corners, also known as Harris interest points or key points, is provided. Two principal and basic assumptions made are: 1) Local image structure is captured in an uncommitted way, simply using weighted raw image values around every image location to describe the local image information, and 2) the lower the probability of observing the image structure present in a particular point, the more salient, or interesting, this position is, i.e., saliency is related to how uncommon it is to see a certain image structure, how surprising it is. Through the latter assumption, the axiomatization proposed makes a sound link between image saliency in computer vision on the one hand and, on the other, computational models of preattentive human visual perception, where exactly the same definition of saliency has been proposed. Because of this link, the characterization provides a compelling case in favor of Harris interest points over other approaches.
INDEX TERMS
Interest points, saliency, Harris corners, visual attention, low probability, elementary characterization.
CITATION
Marco Loog, François Lauze, "The Improbability of Harris Interest Points", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 6, pp. 1141-1147, June 2010, doi:10.1109/TPAMI.2010.53
REFERENCES
[1] T. Lindeberg, "Feature Detection with Automatic Scale Selection," Int'l J. Computer Vision, vol. 30, no. 2, pp. 79-116, 1998.
[2] C. Schmid, R. Mohr, and C. Bauckhage, "Evaluation of Interest Point Detectors," Int'l J. Computer Vision, vol. 37, no. 2, pp. 151-172, 2000.
[3] N. Sebe and M. Lew, "Comparing Salient Point Detectors," Pattern Recognition Letters, vol. 24, nos. 1-3, pp. 89-96, 2003.
[4] W. Förstner and E. Gülch, "A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centres of Circular Features," Proc. Int'l Soc. for Photogrammetry and Remote Sensing Intercommission Conf. Fast Processing of Photogrammetric Data, pp. 281-305, 1987.
[5] C. Harris and M. Stephens, "A Combined Corner and Edge Detector," Proc. Fourth Alvey Vision Conf., pp. 147-151, 1988.
[6] B. Triggs, "Detecting Keypoints with Stable Position, Orientation, and Scale under Illumination Changes," Proc. Eighth European Conf. Computer Vision, pp. 100-113, 2004.
[7] L. Rosenthaler, F. Heitger, O. Kubler, and R. von der Heydt, "Detection of General Edges and Keypoints," Proc. Second European Conf. Computer Vision, pp. 78-86, 1992.
[8] D. Lisin, E. Riseman, and A. Hanson, "Extracting Salient Image Features for Reliable Matching Using Outlier Detection Techniques," Proc. Third Int'l Conf. Computer Vision Systems, 2003.
[9] K. Walker, T. Cootes, and C. Taylor, "Locating Salient Object Features," Proc. British Machine Vision Conf., pp. 557-566, 1998.
[10] J. Shi and C. Tomasi, "Good Features to Track," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 593-600, 1994.
[11] B. ter Haar Romeny, Front-End Vision and Multi-Scale Image Analysis. Kluwer Academic Publishing, 2003.
[12] J. Noble, "Finding Corners," Image and Vision Computing, vol. 6, no. 2, pp. 121-128, 1988.
[13] K. Rohr, "Modelling and Identification of Characteristic Intensity Variations," Image and Vision Computing, vol. 10, no. 2, pp. 66-76, 1992.
[14] K. Mikolajczyk and C. Schmid, "Scale & Affine Invariant Interest Point Detectors," Int'l J. Computer Vision, vol. 60, no. 1, pp. 63-86, 2004.
[15] T. Brox, J. Weickert, B. Burgeth, and P. Mrázek, "Nonlinear Structure Tensors," Image and Vision Computing, vol. 24, no. 1, pp. 41-55, 2006.
[16] I. Laptev, "On Space-Time Interest Points," Int'l J. Computer Vision, vol. 64, no. 2, pp. 107-123, 2005.
[17] P. Montesinos, V. Gouet, and R. Deriche, "Differential Invariants for Color Images," Proc. 14th Int'l Conf. Pattern Recognition, pp. 838-840, 1998.
[18] N. Bruce, "Features that Draw Visual Attention: An Information Theoretic Perspective," Neurocomputing, vol. 65, pp. 125-133, 2005.
[19] J. Fecteau and D. Munoz, "Salience, Relevance, and Firing: A Priority Map for Target Selection," Trends in Cognitive Sciences, vol. 10, no. 8, pp. 382-390, 2006.
[20] D. Gao, V. Mahadevan, and N. Vasconcelos, "On the Plausibility of the Discriminant Center-Surround Hypothesis for Visual Saliency," J. Vision, vol. 8, no. 7, pp. 1-18, 2008.
[21] L. Itti and C. Koch, "Computational Modeling of Visual Attention," Nature Rev. Neuroscience, vol. 2, no. 3, pp. 194-203, 2001.
[22] Z. Lingyun, M. Tong, and G. Cottrell, "Information Attracts Attention: A Probabilistic Account of the Cross-Race Advantage in Visual Search," Proc. 29th Ann. Cognitive Science Conf., 2007.
[23] L. Griffin, "The Second Order Local Image-Structure Solid," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 8, pp. 1355-1366, Aug. 2007.
[24] J. Koenderink and A. van Doorn, "Generic Neighborhood Operators," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 6, pp. 597-605, June 1992.
[25] T. Kadir and M. Brady, "Saliency, Scale and Image Description," Int'l J. Computer Vision, vol. 45, no. 2, pp. 83-105, 2001.
[26] M. Loog, "On an Elementary Definition of Visual Saliency [Abstract]," Perception, vol. 37, no. suppl., p. 4, 2008.
[27] L. Itti and P. Baldi, "Bayesian Surprise Attracts Human Attention," Vision Research, vol. 49, pp. 1295-1306, 2009.
[28] R. Rosenholtz, "A Simple Saliency Model Predicts a Number of Motion Popout Phenomena," Vision Research, vol. 39, no. 19, pp. 3157-3163, 1999.
[29] A. Torralba, "Modeling Global Scene Factors in Attention," J. Optical Soc. Am. A, vol. 20, no. 7, pp. 1407-1418, 2003.
[30] G. Heidemann, "Focus-of-Attention from Local Color Symmetries," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 7, pp. 817-830, July 2004.
[31] L. Itti et al., "A Model of Saliency-Based Visual Attention for Rapid Scene Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254-1259, Nov. 1998.
[32] O. Le Meur, P. Le Callet, D. Barba, and D. Thoreau, "A Coherent Computational Approach to Model Bottom-up Visual Attention," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 802-817, May 2006.
[33] L. Matthies, M. Maimone, A. Johnson, Y. Cheng, R. Willson, C. Villalpando, S. Goldberg, A. Huertas, A. Stein, and A. Angelova, "Computer Vision on Mars," Int'l J. Computer Vision, vol. 75, no. 1, pp. 67-92, 2007.
[34] G. Aubert and P. Kornprobst, Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, second ed. Springer, 2006.
[35] L. Florack, Image Structure. Kluwer Academic Publishers, 1997.
[36] W. Rudin, Functional Analysis. McGraw-Hill Int'l Ed., 1991.
[37] M. Markou and S. Singh, "Novelty Detection: A Review-Part 1: Statistical Approaches," Signal Processing, vol. 83, no. 12, pp. 2481-2497, 2003.
[38] T. Lindeberg and J. Gårding, "Shape-Adapted Smoothing in Estimation of 3D Shape Cues from Affine Deformations of Local 2D Brightness Structure," Image and Vision Computing, vol. 15, no. 6, pp. 415-434, 1997.
[39] K. Rohr, "On 3D Differential Operators for Detecting Point Landmarks," Image and Vision Computing, vol. 15, no. 3, pp. 219-233, 1997.
[40] V. Navalpakkam and L. Itti, "Modeling the Influence of Task on Attention," Vision Research, vol. 45, no. 2, pp. 205-231, 2005.
[41] L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell, "SUN: A Bayesian Framework for Saliency Using Natural Statistics," J. Vision, vol. 8, no. 7, pp. 1-20, 2008.
[42] E. Knudsen, "Fundamental Components of Attention," Ann. Rev. Neuroscience, vol. 30, pp. 57-78, 2007.
[43] W. Boothby, An Introduction to Differentiable Manifolds and Riemannian Geometry. Academic Press, 1975.
[44] S. Gallot, D. Hulin, and J. Lafontaine, Riemannian Geometry. Springer-Verlag, 1990.
[45] R. Adams and J. Fournier, Sobolev Spaces. Academic Press, 1975.
26 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool