NDLI: Improved local shape feature stability through dense model tracking

Content Provider	IEEE Xplore Digital Library
Author	Canelhas, D.R. Stoyanov, T. Lilienthal, A.J.
Copyright Year	2013
Description	Author affiliation: Center of Appl. Autonomous Sensor Syst. (AASS), Orebro Univ., Orebro, Sweden (Canelhas, D.R.; Stoyanov, T.; Lilienthal, A.J.)
Abstract	In this work we propose a method to effectively remove noise from depth images obtained with a commodity structured light sensor. The proposed approach fuses data into a consistent frame of reference over time, thus utilizing prior depth measurements and viewpoint information in the noise removal process. The effectiveness of the approach is compared to two state of the art, single-frame denoising methods in the context of feature descriptor matching and keypoint detection stability. To make more general statements about the effect of noise removal in these applications, we extend a method for evaluating local image gradient feature descriptors to the domain of 3D shape descriptors. We perform a comparative study of three classes of such descriptors: Normal Aligned Radial Features, Fast Point Feature Histograms and Depth Kernel Descriptors; and evaluate their performance on a real-world industrial application data set. We demonstrate that noise removal enabled by the dense map representation results in major improvements in matching across all classes of descriptors as well as having a substantial positive impact on keypoint detection reliability.
Sponsorship	IEEE Ind. Electron. Soc.
Starting Page	3203
Ending Page	3209
File Size	1909239
Page Count	7
File Format	PDF
ISSN	21530858
DOI	10.1109/IROS.2013.6696811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Vectors Noise Cameras Detectors Robot sensing systems Kernel
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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