Member Bibliography/Bibliografía de Miembros

This page collects all the personal publications that CienciaPR members have uploaded to their profiles. You can also see all of the authors collected in these publications or all the keywords contained in these publications.

Found 11 results
Author Title Type [ Year(Asc)]
Filters: Author is Castrodad, Alexey  [Clear All Filters]
2012
Z. Tang, Castrodad, A., Tepper, M., and Sapiro, G., Are you imitating me? unsupervised sparse modeling for group activity analysis from a single video, arXiv preprint arXiv:1208.5451, 2012.
Z. Xing, Zhou, M., Castrodad, A., Sapiro, G., and Carin, L., Dictionary learning for noisy and incomplete hyperspectral images, SIAM Journal on Imaging Sciences, vol. 5, pp. 33–56, 2012.
A. Castrodad, Khuon, T., Rand, R., and Sapiro, G., Sparse modeling for hyperspectral imagery with LiDAR data fusion for subpixel mapping, in Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, 2012, pp. 7275–7278.
A. Castrodad and Sapiro, G., Sparse modeling of human actions from motion imagery, International journal of computer vision, vol. 100, pp. 1–15, 2012.
2010
A. Castrodad, Xing, Z., Greer, J., Bosch, E. H., Carin, L., and Sapiro, G., Discriminative sparse representations in hyperspectral imagery, in Image Processing (ICIP), 2010 17th IEEE International Conference on, 2010, pp. 1313–1316.
2009
T. R. Braun and Castrodad, A., Data reduction via segmentation for hyperspectral imagery, in SPIE Defense, Security, and Sensing, 2009, p. 73340Y–73340Y.
A. Castrodad, Graph-based denoising and classification of hyperspectral imagery using nonlocal operators, in SPIE Defense, Security, and Sensing, 2009, p. 73340E–73340E.
2007
M. Velez-Reyes, Rosario-Torres, S., Goodman, J. A., Alvira, E. M., and Castrodad, A., Hyperspectral image unmixing over benthic habitats, in ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY XIII, 2007, vol. 6565.
A. Castrodad, Bosch, E. H., and Resmini, R., Hyperspectral imagery transformations using real and imaginary features for improved classification, in Defense and Security Symposium, 2007, p. 65651B–65651B.