@conference {16275,
	title = {Inexact Local Alignment Search over Suffix Arrays},
	booktitle = {IEEE International Conference on Bioinformatics and Biomedicine, 2009. BIBM {\textquoteright}09},
	year = {2009},
	month = {2009/11/01/4},
	pages = {83 - 87},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We describe an algorithm for finding approximate seeds for DNA homology searches. In contrast to previous algorithms that use exact or spaced seeds, our approximate seeds may contain insertions and deletions. We present a generalized heuristic for finding such seeds efficiently and prove that the heuristic does not affect sensitivity. We show how to adapt this algorithm to work over the memory efficient suffix array with provably minimal overhead in running time. We demonstrate the effectiveness of our algorithm on two tasks: whole genome alignment of bacteria and alignment of the DNA sequences of 177 genes that are orthologous in human and mouse. We show our algorithm achieves better sensitivity and uses less memory than other commonly used local alignment tools.},
	keywords = {bacteria, Bioinformatics, biology computing, Computational Biology, Costs, DNA, DNA homology searches, DNA sequences, Educational institutions, generalized heuristic, genes, Genetics, genome alignment, Genomics, human, inexact local alignment search, inexact seeds, local alignment, local alignment tools, memory efficient suffix array, microorganisms, molecular biophysics, mouse, Organisms, Sensitivity and Specificity, sequences, suffix array, USA Councils},
	isbn = {978-0-7695-3885-3},
	doi = {10.1109/BIBM.2009.25},
	author = {Ghodsi,M. and Pop, Mihai}
}
@conference {12000,
	title = {Eye design in the plenoptic space of light rays},
	booktitle = {Ninth IEEE International Conference on Computer Vision, 2003. Proceedings},
	year = {2003},
	month = {2003/10/13/16},
	pages = {1160-1167 vol.2 - 1160-1167 vol.2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Natural eye designs are optimized with regard to the tasks the eye-carrying organism has to perform for survival. This optimization has been performed by the process of natural evolution over many millions of years. Every eye captures a subset of the space of light rays. The information contained in this subset and the accuracy to which the eye can extract the necessary information determines an upper limit on how well an organism can perform a given task. In this work we propose a new methodology for camera design. By interpreting eyes as sample patterns in light ray space we can phrase the problem of eye design in a signal processing framework. This allows us to develop mathematical criteria for optimal eye design, which in turn enables us to build the best eye for a given task without the trial and error phase of natural evolution. The principle is evaluated on the task of 3D ego-motion estimation.},
	keywords = {3D ego-motion estimation, Assembly, B-splines, Camera design, CAMERAS, captured image, compound eyes, Computer vision, data mining, eye, eye-carrying organism, Eyes, filter optimization, image representation, image resolution, Information geometry, Laboratories, light field reconstruction, light gathering power, light rays, mixed spherical-Cartesian coordinate system, Motion estimation, natural evolution process, natural eye designs, natural image statistics, optical nanotechnology, Optical signal processing, optimal eye design mathematical criteria, Organisms, plenoptic image formation, plenoptic space, plenoptic video geometry, sampling operators, sensory ecology, Signal design, Signal processing, signal processing framework, signal processing tool, square-summable sequences, visual acuity},
	isbn = {0-7695-1950-4},
	doi = {10.1109/ICCV.2003.1238623},
	author = {Neumann, J. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
