PPT-Pose Estimation for non-cooperative Spacecraft

Rendevous using CNN Ryan McKennonKelly Sharma Sumant Connor Beierle and Simone DAmico Pose Estimation for NonCooperative Spacecraft Rendezvous Using Convolutional Neural Networks September 19 2018 . berkeleyedu lubomirfbcom Figure 1 An example of an image where part detectors based solely on strong contours and edges will fail to detect the upper and lower parts of the arms Abstract We propose a novel approach for human pose estimation in realwo Our approach is examplebased it reduces the problem of recovering the pose to a database search under in the embedding space which is carried out extremely fast using LSH The embedding is constructed based on edge direction histograms using the algo Eichner M MarinJimenez A Zisserman V Ferrari Abstract We present a technique for estimating the spatial layout of humans in still images the position of the head torso and arms The theme we explore is that once a per son is localized using an upper Tracking . and Head Pose Estimation for Gaze Estimation. Ankan Bansal. Salman Mohammad. CS365 Project. Guide - Prof. . Amitabha Mukerjee. Motivation. Human Computer Interaction. Information about interest of the subject, e.g. advertisement research. Yao Li . Fei-Fei. Computer Science Department, Stanford University, USA. Modeling Mutual Context of Object and Human Pose. in Human-Object Interaction Activities. Introduction. Modeling mutual context of object and pose. Hamed Pirsiavash and Deva . Ramanan. Department of Computer Science. UC Irvine . 2. Deformable . part models . (DPM). Human pose estimation. Face pose estimation. Object detection. Felzenszwalb. , . Girshick. Transductive. Regression Forests . Tsz-Ho. Yu. Danhang. . Tang. T-K. Kim. Sponsored by . 2. Motivation. Multiple cameras with invserse kinematics. [Bissacco et al. CVPR2007]. [Yao et al. IJCV2012]. Leonid . Pishchulin. . . Arjun. Jain. . Mykhaylo. . Andriluka. Thorsten . Thorm¨ahlen. . Bernt. . Schiele. Max . Planck Institute for Informatics, . Saarbr¨ucken. , Germany. Introduction. Generation of novel training . Used by Kinect. Accurate when the pose closely matches a stored pose. Inaccurate when novel poses are made. Can often produce shaky movement due to pose snapping. 3d Pose Tracking. Calculate poses based on previous poses and current data. Bangpeng Yao and Li Fei-Fei. Computer Science Department, Stanford University. {bangpeng,feifeili}@cs.stanford.edu. 1. Robots interact with objects. Automatic sports commentary. “Kobe is dunking the ball.”. Embedded Flat Surfaces. From. . Scaled. . Orthographic. Image Data. Ricardo Ferreira. PhD. . Thesis. . Presentation. May 2010. Reconstruction of a . Sheet. of . Paper. From. Photos. Ricardo Ferreira. Determination . I. Fall . 2015. Professor Brandon A. Jones. Lecture 40: Elements of Attitude Estimation. Exam . 3 . In. -class Students: Due December 11 by 5pm. CAETE Students: Due 11. :. 59pm (Mountain) on 12/13. Bangpeng Yao and Li Fei-Fei. Computer Science Department, Stanford University. {bangpeng,feifeili}@cs.stanford.edu. 1. Robots interact with objects. Automatic sports commentary. “Kobe is dunking the ball.”. Bo-Sheng Chen. 1. ,. . Chi-Han Peng. 1. 1. National Yang Ming . Chiao. Tung University (NYCU), Taiwan. Estimating the relative camera poses between two . sparse. 360° panoramas is very difficult for both traditional 8-point methods and neural networks. However, .