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3D Content-Based Model Matching using Geometric Features
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2006 (English)Report (Other academic)
Abstract [en]

We present an approach that utilizes efficient geometric feature extraction and a matching method that takes articulation into account. It is primarily applicable for man-made objects. First the object is analyzed to extract geometric features, dimensions and rotation are estimated and typical parts, so-called functional parts, are identified. Examples of functional parts are a box's lid, a building's chimney, or a battle tank's barrel. We assume a model library with full annotation. The geometric features are matched with the model descriptors, to gain fast and early rejection of non-relevant models. After this pruning the objectis matched with relevant, usually few, library models. We propose a sequential matching, where the number of functional parts increases in each iteration. The division into parts increases the possibility for correct matching result when several similar models are available. The approach is exemplifi…ed with an vehicle recognition application, where some vehicles have functional parts.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2006. , 8 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2726
Keyword [en]
Geometric features, Early rejection, Content-based matching, Least squares
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-14121ISRN: LITH-ISY-R-2726OAI: diva2:22674
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2014-08-28Bibliographically approved
In thesis
1. Ground Object Recognition using Laser Radar Data: Geometric Fitting, Performance Analysis, and Applications
Open this publication in new window or tab >>Ground Object Recognition using Laser Radar Data: Geometric Fitting, Performance Analysis, and Applications
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis concerns detection and recognition of ground object using data from laser radar systems. Typical ground objects are vehicles and land mines. For these objects, the orientation and articulation are unknown. The objects are placed in natural or urban areas where the background is unstructured and complex. The performance of laser radar systems is analyzed, to achieve models of the uncertainties in laser radar data.

A ground object recognition method is presented. It handles general, noisy 3D point cloud data. The approach is based on the fact that man-made objects on a large scale can be considered be of rectangular shape or can be decomposed to a set of rectangles. Several approaches to rectangle fitting are presented and evaluated in Monte Carlo simulations. There are error-in-variables present and thus, geometric fitting is used. The objects can have parts that are subject to articulation. A modular least squares method with outlier rejection, that can handle articulated objects, is proposed. This method falls within the iterative closest point framework. Recognition when several similar models are available is discussed.

The recognition method is applied in a query-based multi-sensor system. The system covers the process from sensor data to the user interface, i.e., from low level image processing to high level situation analysis.

In object detection and recognition based on laser radar data, the range value’s accuracy is important. A general direct-detection laser radar system applicable for hard-target measurements is modeled. Three time-of-flight estimation algorithms are analyzed; peak detection, constant fraction detection, and matched filter. The statistical distribution of uncertainties in time-of-flight range estimations is determined. The detection performance for various shape conditions and signal-tonoise ratios are analyzed. Those results are used to model the properties of the range estimation error. The detector’s performances are compared with the Cramér-Rao lower bound.

The performance of a tool for synthetic generation of scanning laser radar data is evaluated. In the measurement system model, it is possible to add several design parameters, which makes it possible to test an estimation scheme under different types of system design. A parametric method, based on measurement error regression, that estimates an object’s size and orientation is described. Validations of both the measurement system model and the measurement error model, with respect to the Cramér-Rao lower bound, are presented.

Place, publisher, year, edition, pages
Institutionen för systemteknik, 2006
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1055
Laser radar, object detection, object recognition, performance, least squares, ICP, Cramér-Rao
National Category
Signal Processing
urn:nbn:se:liu:diva-7685 (URN)91-85643-53-X (ISBN)
Public defence
2006-11-17, BL32-Nobel, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2009-02-24

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