Abstract
Template matching is widely used in machine vision, digital photogrammetry, and multimedia data mining to search for a target object by similarity between its prototype image (template) and a sensed image of a natural scene containing the target. In the real-world environment, similarity scores are frequently affected by contrast / offset deviations between the template and target signals. Most of the popular least-squares scores presume only simple smooth deviations that can be approximated with a low-order polynomial. This paper proposes an alternative and more general quadratic programming based matching score that extends the conventional least-squares framework onto both smooth and non-smooth signal deviations.
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Aschwanden, P., Guggenbuhl, W.: Experimental results from a comparative study on correlation-type registration algorithms. In: Förstner, W., Ruwiedel, S. (eds.) Robust Computer Vision, pp. 268–289. Karlsruhe, Wichmann (1992)
Basri, R., Jacobs, D., Kemelmacher, I.: Photometric stereo with general, unknown lighting. International Journal of Computer Vision 72(3), 239–257 (2007)
Chen, J., Chen, C., Chen, Y.: Fast algorithm for robust template matching with m-estimators. IEEE Transactions on Signal Processing 51(1), 230–243 (2003)
Crowley, J., Martin, J.: Comparison of correlation techniques. In: Proc. International Conference on Intelligent Autonomous Systems (IAS-4), Karlsruhe, Germany, March 27–30, pp. 86–93. IOS Press, Amsterdam (1995)
D’Esopo, D.: A convex programming procedure. Naval Research Logistics Quarterly 6, 33–42 (1959)
Fitch, A., Kadyrov, A., Christmas, W., Kittler, J.: Fast robust correlation. IEEE Transactions on Image Processing 14(8), 1063–1073 (2005)
Hildreth, C.: A quadratic programming procedure. Naval Research Logistics Quarterly 4, 79–85 (1957)
Lai, S.: Robust image matching under partial occlusion and spatially varying illumination change. Computer Vision and Image Understanding 78(1), 84–98 (2000)
Lai, S., Fang, M.: Method for matching images using spatially-varying illumination change models. US patent 6, 621, 929 (2003)
MIT face database (accessed August 24, 2006), http://vismod.media.mit.edu/pub/images/
Pizarro, D., Peyras, J., Bartoli, A.: Light-invariant fitting of active appearance models. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVIP 2008), Anchorage, Alaska, USA, pp. 1–6 (June 2008)
Silveira, G., Malis, E.: Real-time visual tracking under arbitrary illumination changes. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVIP 2007), Minneapolis, USA, pp. 1–6 (June 2007)
Tombari, F., Di Stefano, L., Mattoccia, S.: A robust measure for visual correspondence. In: Proc. 14th Int. Conf. on Image Analysis and Processing (ICIAP), Modena, Italy, 2007, pp. 376–381 (September 2007)
Wei, S., Lai, S.: Robust and efficient image alignment based on relative gradient matching. IEEE Trans. on Image Processing 15(10), 2936–2943 (2006)
Yang, C., Lai, S., Chang, L.: Robust face image matching under illumination variations. EURASIP Journal on Applied Signal Processing 2004(16), 2533–2543 (2004)
Zhu, G., Zhang, S., Chen, X., Wang, C.: Efficient illumination insensitive object tracking by normalized gradient matching. IEEE Signal Processing Letters 14(12), 944–947 (2007)
Zou, J., Ji, Q., Nagy, G.: A comparative study of local matching approach for face recognition. IEEE Trans. on Image Processing 16(10), 2617–2628 (2007)
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Shorin, A., Gimel’farb, G., Delmas, P., Morris, J. (2008). Image Matching with Spatially Variant Contrast and Offset: A Quadratic Programming Approach. In: da Vitoria Lobo, N., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2008. Lecture Notes in Computer Science, vol 5342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89689-0_17
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DOI: https://doi.org/10.1007/978-3-540-89689-0_17
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