Loomis Nicholas C.

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Loomis
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Nicholas C.
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  • Thesis
    Computational imaging and automated identification for aqueous environments
    (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2011-06) Loomis, Nicholas C.
    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.
  • Preprint
    Focus detection from digital in-line holograms based on spectral l1 norms
    ( 2007-05-16) Li, Weichang ; Loomis, Nicholas C. ; Hu, Qiao ; Davis, Cabell S.
    In this paper a rapid focus detection technique is developed for objects imaged using digital in-line holograms. It differs from previous approaches in that it is based directly on the spectral content of the object images and does not need a full reconstruction of the actual images. It is based on new focus metrics defined as the l1 norms of the object spectral components associated with the real and imaginary parts of the reconstruction kernel. Furthermore, these l1 norms can be computed efficiently in the frequency domain using a polar coordinate system, yielding a drastic speedup of about two orders of magnitude compared with image-based focus detection. The subsequent reconstruction, when done selectively over these detected focus distances, leads to significant computational savings. Focus detection results from holograms of plankton are demonstrated showing that the technique is both accurate and robust.