Template-based matching using weight maps
Kwie Min Wong
TL;DR
This paper explores enhancing template matching accuracy by adjusting regional weights within templates, tested on face detection tasks using the FERET dataset.
Contribution
It introduces and compares various weight map methods to improve template matching performance in visual recognition tasks.
Findings
Weighted templates outperform uniform ones in eye detection accuracy.
Certain weight map configurations significantly improve detection robustness.
The approach demonstrates potential for broader pattern recognition applications.
Abstract
Template matching is one of the most prevalent pattern recognition methods worldwide. It has found uses in most visual concept detection fields. In this work, we investigate methods for improving template matching by adjusting the weights of different regions of the template. We compare several weight maps and test the methods using the FERET face test set in the context of human eye detection.
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Video Analysis and Summarization