Learn More
In recent years, convolutional neural network (CNN) based methods have achieved great success in a large number of applications and have been among the most powerful and widely used techniques in computer vision. However, CNN-based methods are com-putational-intensive and resource-consuming, and thus are hard to be integrated into embedded systems such as(More)
It is generally understood that foxtail millet and broomcorn millet were initially domesticated in Northern China where they eventually became the dominant plant food crops. The rarity of older archaeological sites and archaeobotanical work in the region, however, renders both the origins of these plants and their processes of domestication poorly(More)
Pose estimation of object is one of the key problems for the automatic-grasping task of robotics. In this paper, we present a new vision-based robotic grasping system, which can not only recognize different objects but also estimate their poses by using a deep learning model, finally grasp them and move to a predefined destination. The deep learning model(More)
We report here on the isotopic analysis of the diet of one of the oldest modern humans found in Eurasia, the Tianyuan 1 early modern human dating to approximately 40,000 calendar years ago from Tianyuan Cave (Tianyuandong) in the Zhoukoudian region of China. Carbon and nitrogen isotope analysis of the human and associated faunal remains indicate a diet high(More)
Real-time pedestrian detection and tracking are vital to many applications, such as the interaction between drones and human. However, the high complexity of Convolutional Neural Network (CNN) makes them rely on powerful servers, thus is hard for mobile platforms like drones. In this paper, we propose a CNN-based real-time pedestrian detection and tracking(More)
  • 1