Next Article in Journal
A Reference Point Construction Method Using Mobile Terminals and the Indoor Localization Evaluation in the Centroid Method
Previous Article in Journal
Error and Congestion Resilient Video Streaming over Broadband Wireless
Article Menu

Export Article

Open AccessArticle

Semi-Automatic Image Labelling Using Depth Information

Department of Computing Science, Umeå University, Umeå, SE-901 87, Sweden
Australian National University, Acton ACT 2601, Australia
Author to whom correspondence should be addressed.
Academic Editor: Aaron Quigley
Computers 2015, 4(2), 142-154;
Received: 11 June 2013 / Accepted: 14 April 2015 / Published: 6 May 2015
PDF [2614 KB, uploaded 6 May 2015]


Image labeling tools help to extract objects within images to be used as ground truth for learning and testing in object detection processes. The inputs for such tools are usually RGB images. However with new widely available low-cost sensors like Microsoft Kinect it is possible to use depth images in addition to RGB images. Despite many existing powerful tools for image labeling, there is a need for RGB-depth adapted tools. We present a new interactive labeling tool that partially automates image labeling, with two major contributions. First, the method extends the concept of image segmentation from RGB to RGB-depth using Fuzzy C-Means clustering, connected component labeling and superpixels, and generates bounding pixels to extract the desired objects. Second, it minimizes the interaction time needed for object extraction by doing an efficient segmentation in RGB-depth space. Very few clicks are needed for the entire procedure compared to existing, tools. When the desired object is the closest object to the camera, which is often the case in robotics applications, no clicks at all are required to accurately extract the object. View Full-Text
Keywords: image labelling; image segmentation; depth information; labelling tools; RGBD data; Microsoft Kinect; object detection; robot vision image labelling; image segmentation; depth information; labelling tools; RGBD data; Microsoft Kinect; object detection; robot vision
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Pordel, M.; Hellström, T. Semi-Automatic Image Labelling Using Depth Information. Computers 2015, 4, 142-154.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Computers EISSN 2073-431X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top