| Abstract: |  Repetitive patterns typically arise from man-made objects and are
ubiquitous in image collections. Because of their highly repetitive
structure, imaged patterns violate statistical assumptions typically
made in scene understanding algorithms, often negatively impacting
algorithm efficacy. Conversely, repetitive patterns could be detected
and modeled so that their distinctive structures are leveraged to
uniquely characterize the scenes in which they are present. With that
goal in mind, this thesis presents a novel method for the automated
detection and sparse 3-D reconstruction of imaged coplanar repetitive
patterns. The proposed method applies to a very general class of
patterns that encompasses nearly all man-made patterns. Particular
contributions include a new set of geometric constraints to eliminate
the geometric ambiguity between the imaged and scene pattern, a method
to reconstruct the pattern's motif, and a robust framework that
successfully detects and reconstructs patterns in the presence of
clutter and imaged from lens distorted cameras.
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