Fingertip detection based on protuberant saliency from depth image
Yuseok Ban, Minglei Li, Lei Sun, Qiang Huo · 2017
We propose a new approach for detecting a protuberant region from a depth image by leveraging a notion of protuberant saliency which describes how much the protuberant region stands out from its surroundings in the depth image. An intuitive and simple method is designed to calculate protuberant saliency from depth image, which can be used effectively together with the nearness information to detect the tip of a protuberant object such as a fingertip. We evaluate and compare our method with several state-of-the-art saliency methods for fingertip detection. Experimental results demonstrate that our method outperforms the comparing methods in terms of detection accuracy, being more robust against the rotation and isometric deformation of a fingertip region, the scale and depth noise issues, and the low resolution of a depth image.