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Paper TuC110.2

Blum, Manuel (Albert-Ludwigs-Universitaet Freiburg), Springenberg, Jost Tobias (Albert-Ludwigs-University of Freiburg), Wülfing, Jan (Albert-Ludwigs-Universitaet Freiburg), Riedmiller, Martin (Albert-Ludwigs-Universitaet Freiburg)

A Learned Feature Descriptor for Object Recognition in RGB-D Data

Scheduled for presentation during the Interactive Session "Interactive Session TuC-1" (TuC110), Tuesday, May 15, 2012, 14:30−15:00, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 13, 2017

Keywords Recognition, Visual Learning, Computer Vision for Robotics and Automation

Abstract

In this work we address the problem of feature extraction for object recognition in the context of cameras providing RGB and depth information (RGB-D data). We consider this problem in a bag of features like setting and propose a new, learned, local feature descriptor for RGB-D images, the convolutional k-means descriptor. The descriptor is based on recent results from the machine learning community. It automatically learns feature responses in the neighborhood of detected interest points and is able to combine all available information, such as color and depth into one, concise representation. To demonstrate the strength of this approach we show its applicability to different recognition problems. We evaluate the quality of the descriptor on the RGB-D Object Dataset where it is competitive with previously published results and propose an embedding into an image processing pipeline for object recognition and pose estimation.

 

 

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