V. Saligrama
Behavior Subtraction.
Fri, 10/16/2009 - 17:25 — SomNambulAuthors: V. Saligrama, P. M. Jodoin, J. Konrad
Subjects: Computer Vision and Pattern Recognition
AbstractBackground subtraction has been a driving engine for many computer vision and
video analytics tasks. Although its many variants exist, they all share the
underlying assumption that photometric scene properties are either static or
exhibit temporal stationarity. While this works in some applications, the model
fails when one is interested in discovering {\it changes in scene dynamics}
rather than those in a static background; detection of unusual pedestrian and
motor traffic patterns is but one example.Compressed Blind De-convolution.
Fri, 10/02/2009 - 09:41 — SomNambulAuthors: V. Saligrama, M. Zhao
Subjects: Information Theory
AbstractSuppose the signal x is realized by driving a k-sparse signal u through an
arbitrary unknown stable discrete-linear time invariant system H. These types
of processes arise naturally in Reflection Seismology. In this paper we are
interested in several problems: (a) Blind-Deconvolution: Can we recover both
the filter $H$ and the sparse signal $u$ from noisy measurements? (b)
Compressive Sensing: Is x compressible in the conventional sense of compressed
sensing? Namely, can x, u and H be reconstructed from a sparse set of
measurements.Compressed Blind De-convolution.
Fri, 10/02/2009 - 09:41 — SomNambulAuthors: V. Saligrama, M. Zhao
Subjects: Information Theory
AbstractSuppose the signal x is realized by driving a k-sparse signal u through an
arbitrary unknown stable discrete-linear time invariant system H. These types
of processes arise naturally in Reflection Seismology. In this paper we are
interested in several problems: (a) Blind-Deconvolution: Can we recover both
the filter $H$ and the sparse signal $u$ from noisy measurements? (b)
Compressive Sensing: Is x compressible in the conventional sense of compressed
sensing? Namely, can x, u and H be reconstructed from a sparse set of
measurements.
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