/Boundary Detection Based on Supervised Learning

Boundary Detection Based on Supervised Learning

Kiho Kwak, Daniel Huber, Jeongsook Chae and Takeo Kanade
Conference Paper, Proceedings of the International Conference on Robotics and Automation (ICRA), May, 2010

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Detecting the boundaries of objects is a key step in separating foreground objects from the background, which is useful for robotics and computer vision applications, such as object detection, recognition, and tracking. We propose a new method for detecting object boundaries using planar laser scanners (LIDARs) and, optionally, co-registered imagery. We formulate boundary detection as a classification problem, in which we estimate whether a boundary exists in the gap between two consecutive range measurements. Features derived from the LIDAR and imagery are used to train a support vector machine (SVM) classifier to label pairs of range measurements as boundary or non-boundary. We compare this approach to an existing boundary detection algorithm that uses dynamically adjusted thresholds. Experiments show that the new method performs better even when only LIDAR features are used, and additional improvement occurs when image-based features are included, too. The new algorithm performs better on difficult boundary cases, such as obliquely viewed objects.

BibTeX Reference
@conference{Kwak-2010-10450,
author = {Kiho Kwak and Daniel Huber and Jeongsook Chae and Takeo Kanade},
title = {Boundary Detection Based on Supervised Learning},
booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA)},
year = {2010},
month = {May},
}
2017-09-13T10:40:45+00:00