Yafeng Deng

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How can a single fully convolutional neural network (FCN) perform on object detection? We introduce DenseBox, a unified end-to-end FCN framework that directly predicts bounding boxes and object class confidences through all locations and scales of an image. Our contribution is twofold. First, we show that a single FCN, if designed and optimized carefully,(More)
—Face Recognition has been studied for many decades. As opposed to traditional hand-crafted features such as LBP and HOG, much more sophisticated features can be learned automatically by deep learning methods in a data-driven way. In this paper, we propose a two-stage approach that combines a multi-patch deep CNN and deep metric learning, which extracts low(More)
Viola et al. have introduced a rapid object detection framework based on a boosted cascade of simple feature classifiers. In this paper we extend their work and achieve two contributions. Firstly, we propose a novel feature definition and introduce a feature shape mask to represent it. The defined features are scale-invariant which means the features can be(More)
本文结合CSMA/CA与TDMA思想的方式,通过利用节点在网络中能感知到其他节点成功传输的特点,使节点选取在上一个虚拟TDMA周期中是第i个成功发送数据的顺序作为在当前虚拟TDMA周期中的发送顺序。由于同一时隙有且仅有一个成功传输的节点,那么所有成功传输的节点都有一个独立的时隙来发送数据,未成功的节点则随机选取剩余的时隙中某一个再次发送数据。这样成功节点间的数据发送不会相互干扰,未成功节点也不会影响到成功节点的传输。并且在一个虚拟TDMA周期中,当所有节点都能成功发送一次数据时就能一直延续下去,实现免碰撞从而获得高吞吐量。
In this paper, we propose a fast and robust face detection method. We train a cascade-structured classifier with boosted haar-like features which uses intensity information only. To speed up the process, we integrate motion energy into the cascade-structured classifier. Motion energy can represent moving the extent of the candidate regions, which is used to(More)
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