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Asymmetric correlation: a noise robust similarity measure for template matching.
| Content Provider | CiteSeerX |
|---|---|
| Author | Elboher, Elhanan Werman, Michael |
| Abstract | Abstract—We present an efficient and noise robust template matching method based on asymmetric correlation (ASC). The ASC similarity function is invariant to affine illumination changes and robust to extreme noise. It correlates the given nonnormalized template with a normalized version of each image window in the frequency domain. We show that this asymmetric normalization is more robust to noise than other cross correlation variants such as the correlation coefficient. Direct computation of ASC is very slow, as a DFT needs to be calculated for each image window independently. To make the template matching efficient, we developed a much faster algorithm which carries out a prediction step in linear time and then computes DFTs for only a few promising candidate windows. We extend the proposed template matching scheme to deal with partial occlusion and spatially varying light change. Experimental results demonstrate the robustness of the proposed ASC similarity measure compared to state of the art template matching methods. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Asymmetric Correlation Template Matching Noise Robust Similarity Measure Image Window Light Change Asc Similarity Measure Direct Computation Cross Correlation Variant Asymmetric Normalization Correlation Coefficient Frequency Domain Nonnormalized Template Partial Occlusion Normalized Version Illumination Change Linear Time Art Template Matching Method Prediction Step Noise Robust Template Asc Similarity Function Promising Candidate Window Template Matching Efficient Experimental Result |
| Content Type | Text |
