XXIX-B3, 2012
natic Line Matching
p. 666—671.
construction methods
1, Photogrammetric
Vo: 7, 705-714.
'ercity modeller. In:1
"mote Sensing
h 2006.
D building model
ground plans. In:
and Remote Sensing,
GESTALT GROUPING ON FACADE TEXTURES FROM IR IMAGE SEQUENCES:
COMPARING DIFFERENT PRODUCTION SYSTEMS
E. Michaelsen !, D. Iwaszczuk., L. Hoegner”, BB. Sirmacek?, U. Stilla?
! Fraunhofer-IOSB, Gutleuthausstrasse 1, 76275 Ettlingen, Germany, eckart.michaelsen(g)iosb.fraunhofer.de
? Technische Universitaet Muenchen (TUM), Photogrammetry and Remote Sensing, 80333 Muenchen, Germany
? German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 Wessling, Germany
Commission III, WG III/4
KEY WORDS: Facade recognition, thermal imagery, production systems
ABSTRACT:
The façades of buildings are almost always organized according to Gestalt principles such as good continuation, repetition in
similarity, or symmetry etc. Coding such principles in production systems yields a very flexible frame to explore the usefulness of
such principles in automatic facade understanding. Capturing images and image sequences of façades in the thermal domain and
understanding such data is of importance e.g. for energy saving. In this contribution two different production systems are compared
using the same data and interpreter.
1. INTRODUCTION
1.1 Thermal Textures
Thermal textures on facades of buildings are of growing
interest. In thermal infrared (IR) images damages and weak
spots in building hull (especially in building insulation) and
heat waste can be observed. Thanks to combination with a 3D
building model the spatial reference of the IR images is given.
In urban areas with narrow streets it is often not possible to
capture a whole fagade in one frame. Then the picture can be
stitched from a video. Figure 1 shows such image which
actually results from texturing a 3D building model by
projecting suitable image information from a vehicle mounted
thermal video camera (Hoegner & Stilla U, 2007).
Figure 1. A thermal façade texture
Of course artefacts from stitching cannot be completely
avoided. But, obviously in such textures heat leakages can be
detected and the heat bridges can be stored together with 3D
building data. Our goal is to proceed in automation of this
process.
There are two reasons why windows should be detected in IR
textures and excluded for the inspection. First, in thermal
Imagery glass reflects the surrounding, e.g. sky, a neighbouring
building and trees, and shows false results for the temperature
measurements. Second, windows can influence automatic heat
leakage detection (Hoegner & Stilla U, 2009) and lead to false
results. Therefore a method for window detection in IR images
is needed.
1.2 Gestalt Grouping
Façades are man-made objects and display strong gestalt
structure, such as ordering according to lattice or symmetry
principles. This also holds for the thermal spectral domain. The
laws of Gestalt grouping are known for about hundred years,
namely “good continuation”, “repetition in similarity",
“symmetry — mirror or rotational” and proximity (Wertheimer,
1923). Automatic Gestalt grouping can e.g. be performed by
tensor voting or accumulator methods. The state-of-the-art has
e.g. been presented at the symmetry competition along with the
CVPR (Liu & Rauschert, 2011).
1.3 Related Work
In the last decade facades classification has drawn particular
attention. Pu & Vosselman (2009) fused laser data and close-
range images to reconstruct building facade details. They
extracted windows and doors in both close-range optical and
laser images by using Hough accumulation of lines. Detected
windows and doors helped them to register close-range optical
and laser images. That shows the importance of the facade
classification study in three-dimensional city modeling.
Burochin et al. (2009) proposed a segmentation method to
detect repetitive structures like windows in close-range optical
images. For segmentation they defined a model by considering
shape and reflectance of a window. Then they applied matching
process to find correspondence between model and image. In
(Ali et al., 2007), a summary of the researches on window
detection has been given. They also proposed a window
detection system based on cascade classifiers. In a following
study, Ali et al. proposed a system to detect windows in laser
scanner data. They use depth variations to detect windows (Ali
et al., 2008). Lee & Nevatia (2004) proposed a robust system to
detect windows in optical images. They extracted window
