We plan to provide links to code from various projects completed in our group . Please note disclaimer at the bottom of the page.
- Perturbed Topological Signatures of Persistence Diagrams
Anirudh Som, Kowshik Thopalli, Karthikeyan N. Ramamurthy, Vinay Venkataraman, Ankita Shukla, Pavan Turaga, “Perturbation Robust Representations of Topological Persistence Diagrams”, in ECCV 2018.
- Elastic functional-codes for Riemannian Trajectories
Rushil Anirudh, Pavan Turaga, Jingyong Su, & Anuj Srivastava, “Elastic functional coding of Riemannian trajectories”, in IEEE PAMI 2017.
- Deep-net based compressive light-field Recovery
(Pls contact us if you need the coded mask simulation)
Mayank Gupta, Arjun Jauhari, Kuldeep Kulkarni, Suren Jayasuriya, Alyosha Molnar, Pavan Turaga, “Compressive Light Field Reconstructions using Deep Learning”, Computational Cameras and Displays Workshop (in conjunction with CVPR), 2017.
- ReconNet: CNNs for Compressive Image Recovery
Kuldeep Kulkarni, Suhas Lohit, Pavan Turaga, Ronan Kerviche, Amit Ashok , “ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
- Riemannian analysis of Topological Persistence Diagrams
Rushil Anirudh, Vinay Venkataraman, Karthikeyan Natesan Ramamurthy, Pavan K. Turaga, “A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams”, 2nd International Workshop on Differential Geometry in Computer Vision and Machine Learning (Diff-CVML) 2016, in conjunction with CVPR 2016.
- Statistics on Stiefel and Grassmann Manifolds
P. Turaga, A. Veeraraghavan, A. Srivastava, R. Chellappa,”Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition”, IEEE PAMI Nov 2011.
Permission to use, copy, or modify these software and their documentation for educational and research purposes only and without fee is hereby granted, provided this copyright notice appears on all copies and supporting documentation.
The programs are provided on an ‘as is’ basis without any express or implied warranty of any kind including warranties of merchantability, noninfringement of intellectual property, or fitness for any particular purpose. In no event shall the authors be liable for any damages whatsoever (including, without limitation, damages for loss of profits, business interruption, loss of information) arising out of the use of or inability to use these programs, even if the author has been advised of the possibility of such damages.
The authors may make changes to these materials at any time without notice. The authors make no commitment to update the materials. Each program is provided ‘as is’, without any express or implied warranty, without even the warranty of fitness for a particular purpose.