chinmay hegde


  • EMD-CS Toolbox (developed by Ludwig Schmidt)
    Includes a Matlab/C implementation of EMD-CS, a method for efficient compressive recovery of signal ensembles with correlated supports.

C. Hegde, P. Indyk, L. Schmidt
Approximation-Tolerant Model-Based Compressive Sensing, ACM Symposium on Discrete Algorithms (SODA), January 2014.

  • NuMax Toolbox
    Includes a Matlab implementation of NuMax, a highly efficient algorithm for constructing near-isometric linear embeddings for arbitrary datasets.

C. Hegde, A. C. Sankaranarayanan, W. Yin, and R. G. Baraniuk,
A Convex Approach for Learning Near-Isometric Linear Embeddings
Preprint, November 2012.

  • SPIN Toolbox
    Includes Matlab implementations for compressive sensing recovery for incoherent manifold models.
    Also includes some useful toy image manifold data.

C. Hegde and R. G. Baraniuk,
Signal Recovery on Incoherent Manifolds
Preprint, January 2012.

R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde,
Model-Based Compressive Sensing
IEEE Transactions on Information Theory, vol. 56, p1982-2001, April 2010.


WARNING : Large files alert.

Joint Manifolds datasets: provides data from multi-camera acquisition of low-dimensional object articulations.

Both archived folders contain small Matlab scripts that read in the images, downsample them, and store the image data in a mat-file.
For details, please refer to our paper on joint manifolds:

M. A. Davenport, C. Hegde, M. F. Duarte, and R. G. Baraniuk, Joint Manifolds for Data Fusion
IEEE Transactions on Image Processing, vol. 19, no. 10, p2580-2594, October 2010.