Quasi-regular facade structure extraction

Tian Han, Chun Liu, Chiew Lan Tai, Long Quan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this paper we present a novel two-stage framework for extracting what we define as a quasi-regular structure in facade images. A quasi-regular structure is an irregular rectangular grid representing the placements of repetitive structural architecture objects, e.g., windows, in a facade. Such a structure generalizes a perfect lattice structure generated by the 2D symmetry groups, studied by the previous work. First, we propose to formulate the quasi-regular structure detection in an object-oriented Marked Point Process framework by treating the architectural elements as objects. This leads to an initial quasi-regular structure map which serves as an indicator map of potential object locations. Then, we propose a regularization scheme to recover the complete quasi-regular structures from the initial incomplete structure. This stage takes advantage of the intrinsic low rank constraint of the quasi-regular structure representing a regularized facade. By applying such a regularization, the complete quasi-regular facade structure is obtained. We have extensively tested our method on a large variety of facade images, and demonstrated both the effectiveness and the robustness of our two-stage framework.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2012 - 11th Asian Conference on Computer Vision, Revised Selected Papers
Pages552-564
Number of pages13
EditionPART 4
DOIs
StatePublished - 2013
Event11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
Duration: 5 Nov 20129 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume7727 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th Asian Conference on Computer Vision, ACCV 2012
Country/TerritoryKorea, Republic of
CityDaejeon
Period5/11/129/11/12

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