  Object Classification is a key direction of research in signal and image
processing, computer vision and artificial intelligence. The goal is to come up
with algorithms that automatically analyze images and put them in predefined
categories. This dissertation focuses on the theory and application of sparse
signal processing and learning algorithms for image processing and computer
vision, especially object classification problems. A key emphasis of this work
is to formulate novel optimization problems for learning dictionary and
structured sparse representations. Tractable solutions are proposed
subsequently for the corresponding optimization problems.
  An important goal of this dissertation is to demonstrate the wide
applications of these algorithmic tools for real-world applications. To that
end, we explored important problems in the areas of:
  1. Medical imaging: histopathological images acquired from mammalian tissues,
human breast tissues, and human brain tissues.
  2. Low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture
radar: detecting bombs and mines buried under rough surfaces.
  3. General object classification: face, flowers, objects, dogs, indoor
scenes, etc.
