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Semi-Automatic Image Labelling Using Depth Information
Umeå University, Faculty of Science and Technology, Department of Computing Science. Australian National University.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2015 (English)In: Computers, ISSN 2073-431X, E-ISSN 2073-431X, Vol. 4, no 2, 142-154 p.Article in journal (Refereed) PublishedText
Abstract [en]

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.

Place, publisher, year, edition, pages
2015. Vol. 4, no 2, 142-154 p.
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:umu:diva-112769DOI: 10.3390/computers4020142ISI: 000358280100004OAI: diva2:882849
Available from: 2015-12-15 Created: 2015-12-14 Last updated: 2015-12-15Bibliographically approved

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Pordel, MostafaHellström, Thomas
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