Olof Henricsson

Automatic Extraction of Man-Made Objects from Aerial and Space Images (II)

General Topics and Scene Reconstruction.- An Overview of DARPAs Research Program in Automatic Population of Geospatial Databases.- A Testbed for the Evaluation of Feature Extraction Techniques in a Time Constrained Environment.- The Role of Artificial Intelligence in the Reconstruction of Man-Made Objects from Aerial Images.- Scene Reconstruction Research - Towards an Automatic System.- Semantic Modelling of Man-Made Objects by Production Nets.- From Large-Scale DTM Extraction to Feature Extraction.- Building Detection and Reconstruction.- 3-D Building Reconstruction with ARUBA: A Qualitative and Quantitative Evaluation.- A System for Building Detection from Aerial Images.- On the Reconstruction of Urban House Roofs from Aerial Images.- Image-Based Reconstruction of Informal Settlements.- A Model Driven Approach to Extract Buildings from Multi-View Aerial Imagery.- Automated Building Extraction from Digital Stereo Imagery.- Application of Semi-Automatic Building Acquisition.- On the Integration of Object Modeling and Image Modeling in Automated Building Extraction from Aerial Images.- TOBAGO - A Topology Builder for the Automated Generation of Building Models.- Crestlines Constribution to the Automatic Building Extraction.- Recognizing Buildings in Aerial Image.- Above-Ground Objects in Urban Scenes from Medium Scale Aerial Imagery.- Digital Surface Models for Building Extraction.- Extracting Artificial Surface Objects from Airborne Laser Scanner Data.- Interpretation of Urban Surface Models using 2D Building Information.- Least Squares Matching for Three Dimensional Building Reconstruction.- Assessment of the Effects of Resolution on Automated DEM and Building Extraction.- Road Extraction.- The Role of Grouping for Road Extraction.- Artificial Intelligence in 3-D Feature Extraction.- Updating Road Maps by Contextual Reasoning.- Fast Robust Tracking of Curvy Partially Occluded Roads in Clutter in Aerial Images.- Linear Feature Extraction with 3-D LSB-Snakes.- Context-Supported Road Extraction.- Map/GIS-Based Methods.- Three-Dimensional Description of Dense Urban Areas using Maps and Aerial Images.- MOSES: A Structural Approach to Aerial Image Understanding.- An Approach for the Extraction of Settlement Areas.- Extraction of Polygonal Features from Satellite Images for Automatic Registration: The ARCHANGEL Project.- Visualisation.- A Set of Visualization Data Needs in Urban Environmental Planning & Design for Photogrammetric Data.- A Virtual Reality Model of a Major International Airport.- Managing Large 3D Urban Database Contents Supporting Phototexture and Levels of Detail.- List of Workshop Participants.- Author Index.

Automatic Extraction of Generic House Roofs from High Resolution Aerial Imagery

We present a technique to extract complex suburban roofs from sets of aerial images. Because we combine 2-D edge information, photometric and chromatic attributes and 3-D information, we can deal with complex houses. Neither do we assume the roofs to be flat or rectilinear nor do we require parameterized building models. From only one image, 2-D edges and their corresponding attributes and relations are extracted. Using a segment stereo matching based on all available images, the 3-D location of these edges are computed. The 3-D segments are then grouped into planes and 2-D enclosures are extracted, thereby allowing to infer adjoining 3-D patches describing roofs of houses. To achieve this, we have developed a hierarchical procedure that effectively pools the information while keeping the combinatorics under control. Of particular importance is the tight coupling of 2-D and 3-D analysis.

3-D Building Reconstruction with ARUBA: A Qualitative and Quantitative Evaluation

Reliable and accurate 3-D reconstruction of man-made objects is essential for many applications using digital 3-D city models. Manual reconstruction of buildings from aerial images is time consuming and requires skilled personnel, hence large efforts are being directed towards the automation of building detection and reconstruction. In this paper we present ARUBA — a framework for automated 3-D building reconstruction. After highlighting our strategy and concisely describing the framework and its modules, we evaluate the reconstructed roofs relative to accurate reference data based on three criteria: completeness, geometric accuracy and shape similarity. Finally, we interpret the results of the performance evaluation and make suggestions for improvements.

The Role of Color Attributes and Similarity Grouping in 3-D Building Reconstruction

This paper addresses two major issues: 3-D building reconstruction and the role of color attributes and similarity grouping. We present ARUBA, a general framework for automated 3-D building reconstruction from multiple color aerial images. After highlighting the strategy and concisely describing the framework and its 2-D and 3-D processing modules, we will evaluate the reconstructed roofs with respect to accurate reference data. The second part of the paper shows that geometry, although important, should not be the only source of information exploited in the reconstruction process. The main objectives are to demonstrate that (1) color is a very important cue in reconstructing a general class of objects, (2) it is crucial to retain all information during the entire processing chain, (3) a general class of objects parts can be efficiently extracted by grouping edges and lines by means of similarity, and (4) a mutual interaction between 2-D and 3-D processing is important.

The role of key-points in finding contours

This paper describes a method for aggregating local edge evidences into coherent pieces of contour. An independent representation of corner and junction features provides suitable stop-conditions for the aggregation process and allows to divide contours into meaningful substrings, right from the beginning. The active role of corner and junction points makes the Contours converge onto them and greatly reduces the problems associated with purely edge-based methods. A second stage is concerned with completing established contours across regions that are less well-defined by contrast. The algorithm suggested uses the attributes of established structures (e.g. direction of termination) as well as local orientation and edge evidences to constrain possible completions in a rigorous way.

Inferring Homogeneous Regions from Rich Image Attributes

Image segmentation is an important part in any computer vision framework. However, the transition from local low-level representations to useful structures and relations in the intermediate levels has turned out to be a truly difficult problem. This paper addresses the difficult transition from low-level into intermediate-level vision, where the latter deals with producing a description of image and scene attributes in which more global relations are made explicit. We propose to combine a rich attributed contour representation with very general geometric contour relations. The implemented geometric relations, which are proximity, curvilinearity, parallelism and corner-like relations, allow to handle general man-made objects whose projected surfaces can be described by combinations of the defined relations. The combination of rich image attributes and geometric relations allows to discriminate between strong and weak contour relations. Strong relations require that not only the geometrical constraints are met but also that the contour attributes (e.g. photometric) are in agreement. We describe the approach and show some preliminary results.