Discriminant distance template matching for image recognition

The recognition of arrow markings in intelligent transportation system can be posed as a template matching problem, because their production has to conform with strict national standards. However, appearance variations often arise when capturing the arrow images due to different eye views. In this paper, we propose a discriminant distance template matching (DDTM) method for image recognition. It is compatible with not only hand-crafted features, but also DCNNs for features auto-learning. DDTM is able to discriminate whether an instance and a template matched. Consequently it can realize the arrow recognition through template matching. We have also shown some traditional image recognition tasks can be solved by DDTM. Experimental results on an arrow and the MNIST datasets validate its advantages compared to classification-based methods.