Scale-Adaptive NN-Based Similarity for Robust Template Matching

Template matching in unconstrained environment with complex deformation, occlusion, and background clutter is a challenging task. Recently, some measures which are robust to outliers were presented, however, they fix the window size and thus cannot handle large-scale change. In this article, a multiscale template matching method based on nearest neighbor (NN) search is proposed. To discover the effect of scale to the measure, the expectation of the diversity similarity (DIS) is derived by probabilistic analysis. Then, a scale-adaptive measure is provided by extending DIS and penalizing the deformation explicitly. Moreover, for rectangular template, weights are appended to points to suppress the negative effect of background pixels, and for masked template, foreground pixels are separated from the candidate window based on NN field. In addition, a scheme for preselecting the candidate positions of object detection is given. Experiment on the real-world scenario benchmarks and surface-mount component (SMC) positioning shows that the proposed method is robust to scale changes along with other challenging aspects and outperforms the state-of-the-arts using both color and deep features.