CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: IMPROVING IMAGE SEGMENTATION USING MULTIPLE VIEW ANALYSIS Martin Drauschke, Ribana Roscher, Thomas Läbe, Wolfgang Förstner

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 211 IMPROVING IMAGE SEGMENTATION USING MULTIPLE VIEW ANALYSIS Martin Drauschke, Ribana Roscher, Thomas Labe, Wolfgang Forstner Department of Photogrammetry, Institute of Geodesy and Geoinformation, University of Bonn martin.drauschke@uni-bonn.de, rroscher@uni-bonn.de, laebe@ipb.uni-bonn.de, wf@ipb.uni-bonn.de KEY WORDS: Image Segmentation, Aerial Image, Urban Scene, Reconstruction, Building Detection ABSTRACT In our contribution, we improve image segmentation by integrating depth information from multi-view analysis. We assume the object surface in each region can be represented by a low order polynomial, and estimate the best fitting pa rameters of a plane using those points of the point cloud, which are mapped to the specific region. We can merge adjacent image regions, which cannot be distinguished geometrically. We demonstrate the approach for finding spatially planar regions on aerial images. Furthermore, we discuss the possibilities of extending of our approach towards segmenting terrestrial facade images. 1 INTRODUCTION The interpretation of images showing building scenes is a challenging task, due to the complexity of the scenes and the great variety of building structures. As far as human perception is understood today, humans can easily group visible patterns and use their shape to recognize objects, cf. (Hoffman and Richards, 1984) and (Treisman, 1986). Segmentation, understood as image partitioning often is the first step towards finding basic image patterns. Early image segmentation techniques are discussed in (Pal and Pal, 1993). Since then, many other algorithms have been proposed within the image analysis community: The data- driven approaches often define grouping criteria based on the color contrast between the regions or on textural infor mation. Model-driven approaches often work well only on simple scenes e. g. simple building structures with a flat or gabled roof. However, they are limited when analyzing more complex scenes. Since we are interested in identifying entities of more than two classes as e.g. buildings, roads and vegetation objects, we cannot perform a image division into fore- and back ground as summarized in (Sahoo et al., 1988). Our seg mentation scheme partitions the image into several regions. It is very difficult to divide an image into regions if some regions are recognizable by a homogenous color, others have a significant texture, and others are separable based on the saturation or the intensity, e. g. (Fischer and Buh- mann, 2003) and (Martin et al., 2004). However, often such boundaries are not consistent with geometric bound aries. According to (Binford, 1981), there are seven classes of boundaries depending on illumination, geometry and re flectivity. Therefore, geometric information should be in tegrated into the segmentation procedure. Our approach is motivated by the interpretation of building images, aerial and terrestrial, where many surface patches can be represented by low order polynomials. We assume a multi-view setup with one reference image and its intensity based segmentation, which is then improved by exploiting the 3D-information from the depth image derived from all images. Using the determined orientation data, we are able to map each 3D point to an unique region. Assuming, ob ject surfaces are planar in each region, we can estimate a plane through the selected points. The adjacent regions are merged together if they have similar planes. Finally, we obtain an image partition with regions representing dom inant object surfaces as building parts or ground. We are convinced that the derived regions are much better for an object-based classification than the regions of the initial segmentation, because many regions have simple, charac teristic shapes. The paper is structured as followed. In sec. 2 we discuss recent approaches of combining images and point cloud information, mostly with the focus on building reconstruc tion. Then in sec. 3 we briefly sketch our approach for deriving a dense point cloud from three images. So far, our approach is semi-automatic due to the setting of the point cloud’s scale, but we discuss the possibility of automatiza tion for all its steps. In sec. 4 we present how we estimate the most dominant plane in the dense point cloud restricted on those points, which are mapped to pixels of the same re gion. The merging strategy is presented in sec. 5. Here we only study the segmentation of aerial imagery and present our results in sec. 6. Adaptations for segmenting facade images are discussed in each step separately. We summa rize our contribution in the final section. 2 COMBINING POINT CLOUDS AND IMAGES The fusion of imagery with LIDAR data has successfully be done in the field of building reconstruction. In (Rotten steiner and Jansa, 2002) regions of interests for building extraction are detected in the set of laser points, and pla nar surfaces are estimated in each region. Then the color information of the aerial image is used to merge adjacent coplanar point cloud parts. Contrarily, in (Khoshelham, 2005) regions are extracted from image data, and the spa tial arrangement of corresponding points of a LIDAR point cloud is used as a property for merging adjacent regions. In (Sohn, 2004) multispectral imagery is used to classify vegetation in the LIDAR point cloud using a vegetation in dex. The advantage of using LIDAR data is to work with high-precision positioned points and a very limited portion of outliers. The disadvantage is its expensive acquisition, especially for aerial scenes. Hence, we prefer to derive a point cloud from multiple image views of an object.

Access restriction

Copyright

fullscreen: CMRT09

Monograph

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation