A computational approach to edge detection
Readings in computer vision: issues, problems, principles, and paradigms
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Active shape models—their training and application
Computer Vision and Image Understanding
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Fast Anisotropic Smoothing of Multi-Valued Images using Curvature-Preserving PDE's
International Journal of Computer Vision
Parallelogram Detection in a Digital Image with the Use of the Hough Transform
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
3D free-form object recognition in range images using local surface patches
Pattern Recognition Letters
Robust Real-Time Ellipse Detection by Direct Least-Square-Fitting
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
Robust edge extraction for Swissranger SR-3000 range images
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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In this article we present an approach for localizing planar parts of furniture in depth data from range cameras. It estimates both their six-degree-of-freedom poses and their dimensions. The system has been designed for enabling robots to autonomously manipulate furniture. Range cameras are a promising sensor category for this application. As many of them provide data with considerable noise and distortions, detecting objects, for example, using canonical methods for range data segmentation or feature extraction, is complicated. In contrast, our approach is able to overcome these issues. This is done by combining concepts of 2D and 3D computer vision as well as integrating intensity and range information in multiple steps of our processing chain. Therefore it can be employed on range sensors with both low and high signal-to-noise ratios and in particular on time-of-flight cameras. This concept can be adapted to various object shapes. It has been implemented for object parts with shapes similar to ellipses as a proof-of-concept. For this, a state-of-the-art ellipse detection method has been enhanced regarding our application.