P
Pavan Turaga
Researcher at Arizona State University
Publications - 141
Citations - 5669
Pavan Turaga is an academic researcher from Arizona State University. The author has contributed to research in topics: Computer science & Activity recognition. The author has an hindex of 33, co-authored 121 publications receiving 4994 citations. Previous affiliations of Pavan Turaga include Mitsubishi Electric Research Laboratories & Indian Institute of Technology Guwahati.
Papers
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Journal ArticleDOI
Machine Recognition of Human Activities: A Survey
TL;DR: A comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications is presented.
Proceedings ArticleDOI
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements
TL;DR: A novel convolutional neural network architecture which takes in CS measurements of an image as input and outputs an intermediate reconstruction which is fed into an off-the-shelf denoiser to obtain the final reconstructed image, ReconNet.
Journal ArticleDOI
Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition
TL;DR: This paper discusses how commonly used parametric models for videos and image sets can be described using the unified framework of Grassmann and Stiefel manifolds, and derives statistical modeling of inter and intraclass variations that respect the geometry of the space.
Proceedings ArticleDOI
Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision
TL;DR: It is shown how accurate statistical characterization that reflects the geometry of these manifolds allows us to design efficient algorithms that compare favorably to the state of the art in these very different applications.
Book ChapterDOI
Domain adaptive dictionary learning
TL;DR: A function learning framework for the task of transforming a dictionary learned from one visual domain to the other, while maintaining a domain-invariant sparse representation of a signal.