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Approaches to Object/Background Segmentation and Object Dimension Estimation
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-4434-8055
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2006 (English)Report (Other academic)
Abstract [en]

In this paper, optimization approaches for object/background segmentation and object dimension/orientation estimation are studied. The data sets are collected with a laser radar or are simulated laser radar data. Three cases are defined: 1) Segmentation of the data set into object and background data. When there are several objects present in the scene, data from each object is also separated into different clusters. Bayesian hypothesis testing of two classes is studied. 2) Estimation of the object’s dimensions and orientation using object data only. 3) Estimation of the object’s dimensions and orientation using both object and background data. The dimension and orientation estimation problem is formulated using non-convex optimization, least squares and, convex optimization expressions. The performance of the methods are investigated in simulations.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2006. , 25 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2746
Keyword [en]
Segmentation, Bayes, Rectangle estimation, Least squares, Optimization
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-14125ISRN: LiTH-ISY-R-2746OAI: oai:DiVA.org:liu-14125DiVA: diva2:22678
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2016-08-31Bibliographically 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
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1055
Keyword
Laser radar, object detection, object recognition, performance, least squares, ICP, Cramér-Rao
National Category
Signal Processing
Identifiers
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)
Opponent
Supervisors
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2016-08-31

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Grönwall, ChristinaGustafsson, Fredrik

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