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Segmented Gray-Code Kernels for Fast Pattern Matching
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DOI：10.1109/TIP.2012.2233484.png)
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摘要
En 中文
The gray-code kernels (GCK) family, which has Walsh Hadamard transform on sliding windows as a member, is a family of kernels that can perform image analysis efficiently using a fast algorithm, such as the GCK algorithm. The GCK has been successfully used for pattern matching. In this paper, we propose that the G4-GCK algorithm is more efficient than the previous algorithm in computing GCK. The G4-GCK algorithm requires four additions per pixel for three basis vectors independent of transform size and dimension. Based on the G4-GCK algorithm, we then propose the segmented GCK. By segmenting input data into L-s parts, the SegGCK requires only four additions per pixel for 3L(s) basis vectors. Experimental results show that the proposed algorithm can significantly accelerate the full-search equivalent pattern matching process and outperforms state-of-the-art methods.
Keyword:
Block matching
fast algorithm
feature extraction
pattern matching
template matching
Walsh Hadamard transform
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期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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