THESIS
2013
Abstract
In this thesis, we present a two-stage framework for extracting what we define as a quasi-regular
structure in facade images. A quasi-regular structure is a rectangular grid representing
the placements of repetitive architectural objects, for example, windows in a
facade. Such a structure generalizes a perfect single lattice structure with manageable
complexity. In our two-stage framework we first propose to formulate window detection
using an object-oriented Marked Point Process model with an efficient MCMC sampler.
This leads to an initial structure map which indicates potential window locations. Then
in the second stage, we propose a regularization scheme based on the initial structure
map to recover the complete structure. This stage takes advantage of previously obtained...[
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In this thesis, we present a two-stage framework for extracting what we define as a quasi-regular
structure in facade images. A quasi-regular structure is a rectangular grid representing
the placements of repetitive architectural objects, for example, windows in a
facade. Such a structure generalizes a perfect single lattice structure with manageable
complexity. In our two-stage framework we first propose to formulate window detection
using an object-oriented Marked Point Process model with an efficient MCMC sampler.
This leads to an initial structure map which indicates potential window locations. Then
in the second stage, we propose a regularization scheme based on the initial structure
map to recover the complete structure. This stage takes advantage of previously obtained
object information and the intrinsic low rank constraint of the quasi-regular structure and
can thus reliably extract underlying structures from arbitrary shaped windows. We have
extensively evaluated our method over a large variety of facade images, and demonstrated
both the efficiency and robustness of our two-stage framework. In addition, both qualitative
and quantitative comparisons with other state-of-the-art methods are presented to
show the superiority of our framework.
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