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Computers 2015, 4(2), 142-154; doi:10.3390/computers4020142

Semi-Automatic Image Labelling Using Depth Information

1
Department of Computing Science, Umeå University, Umeå, SE-901 87, Sweden
2
Australian National University, Acton ACT 2601, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Aaron Quigley
Received: 11 June 2013 / Accepted: 14 April 2015 / Published: 6 May 2015
View Full-Text   |   Download PDF [2614 KB, uploaded 6 May 2015]   |  

Abstract

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
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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).

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

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