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. Author manuscript; available in PMC: 2010 Jul 8.
Published in final edited form as: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2008 Jun 23;2008:1–8. doi: 10.1109/CVPR.2008.4587808

Figure 2.

Figure 2

Examples of fitting local search responses: (a) is the local search responses in Figure 1(d) using patch experts trained by a linear support vector machine (SVM). (b–d) show the surface fitting results. More specifically, (b) picks the local displacement with the minimum response value in the search window, while (c) and (d) fit the local search response surface by a quadratic kernel in Equation 15 and a quadratic kernel with a robust error function in Equation 16, respectively. The brighter intensity means the smaller matching error between the template and the source image patch. In each search window, the red cross illustrates the ground truth location. As we can see, in most cases, the above three methods can all achieve good performance, while the proposed convex quadratic fitting (CQF) (c) and the robust convex quadratic fitting (RCQF) (d) methods are less sensitive to local minima than the exhaustive local search (ELS) method (b).