Computational imaging and automated identification for aqueous environments
Citable URI
https://hdl.handle.net/1912/4752DOI
10.1575/1912/4752Abstract
Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods.
Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi fication with bag-of-words models and multi-stage boosting for rock sh detection.
Methods for extracting images of sh from videos of longline operations are demonstrated.
A prototype digital holographic imaging device is designed and tested for quantitative
in situ microscale imaging. Theory to support the device is developed, including particle
noise and the effects of motion. A Wigner-domain model provides optimal settings and
optical limits for spherical and planar holographic references.
Algorithms to extract the information from real-world digital holograms are created.
Focus metrics are discussed, including a novel focus detector using local Zernike moments.
Two methods for estimating lateral positions of objects in holograms without reconstruction
are presented by extending a summation kernel to spherical references and using a local
frequency signature from a Riesz transform. A new metric for quickly estimating object
depths without reconstruction is proposed and tested. An example application, quantifying
oil droplet size distributions in an underwater plume, demonstrates the efficacy of the
prototype and algorithms.
Description
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011
Suggested Citation
Thesis: Loomis, Nicholas C., "Computational imaging and automated identification for aqueous environments", 2011-06, DOI:10.1575/1912/4752, https://hdl.handle.net/1912/4752Related items
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