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Efficient high-resolution template matching with vector quantized nearest neighbour fields
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DOI：10.1016/j.patcog.2024.110386.png)
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Abstract
En 中文
Template matching is a fundamental problem in computer vision with applications in fields including object detection, image registration, and object tracking. Current methods rely on nearest -neighbour (NN) matching, where the query feature space is converted to NN space by representing each query pixel with its NN in the template. NN -based methods have been shown to perform better in occlusions, appearance changes, and non -rigid transformations; however, they scale poorly with high -resolution data and high feature dimensions. We present an NN -based method that efficiently reduces the NN computations and introduces filtering in the NN fields (NNFs). A vector quantization step is introduced before the NN calculation to represent the template with k features, and the filter response over the NNFs is used to compare the template and query distributions over the features. We show that state-of-the-art performance is achieved in low -resolution data, and our method outperforms previous methods at higher resolution.
Keywords:
Template matching
Vector quantized nearest neighbour field
(VQ-NNF)
Object detection
High-resolution template matching
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