Weidong Min

TL;DR: A multimodal dynamic sign language recognition method based on a deep 3-dimensional residual ConvNet and bi-directional LSTM networks, which is named as BLSTM-3D residual network (B3D ResNet), which can obtain state-of-the-art recognition accuracy.

TL;DR: This letter investigates how to calculate the CNN feature, and study the way to fuse SIFT and CNN features for remote sensing image registration, demonstrating that the proposed method yields a better registration performance in terms of both the aligning accuracy and the number of correct correspondences.

TL;DR: A new method for human fall detection on furniture using scene analysis based on deep learning and activity characteristics is presented, which not only accurately and effectively detected falls on furniture but also distinguished them from other fall-like activities, such as sitting or lying down, while the existing methods have difficulties to handle these.

TL;DR: A retrieval method based on weighted distance and basic features of convolutional neural network (CNN) is proposed, which is simplified but efficient, significantly improving retrieval performance.

TL;DR: The qualitative and quantitative analysis of the proposed method not only effectively removed the ghost shadows, and improved the detection accuracy and real-time performance, but also was robust to deal with the occlusion of multiple vehicles in various traffic scenes.