NDLI: Time-of-flight based multi-sensor fusion strategies for hand gesture recognition

Content Provider	IEEE Xplore Digital Library
Author	Kopinski, Thomas Malysiak, Darius Gepperth, Alexander Handmann, Uwe
Copyright Year	2014
Description	Author affiliation: ENSTA ParisTech 828 Blvd des Maréchaux 91762 Palaiseau, France (Gepperth, Alexander) \|\| Hochschule Ruhr West Computer Science Institute Bottrop, Germany (Kopinski, Thomas; Malysiak, Darius; Handmann, Uwe)
Abstract	Building upon prior results, we present an alternative approach to efficiently classifying a complex set of 3D hand poses obtained from modern Time-Of-Flight-Sensors (TOF). We demonstrate it is possible to achieve satisfactory results in spite of low resolution and high noise (inflicted by the sensors) and a demanding outdoor environment. We set up a large database of pointclouds in order to train multilayer perceptrons as well as support vector machines to classify the various hand poses. Our goal is to fuse data from multiple TOF sensors, which observe the poses from multiple angles. The presented contribution illustrates that real-time capability can be maintained with such a setup as the used 3D descriptors, the fusion strategy as well as the online confidence measures are computationally efficient.
Starting Page	243
Ending Page	248
File Size	417748
Page Count	6
File Format	PDF
ISBN	9781479953387
DOI	10.1109/CINTI.2014.7028683
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-19
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Three-dimensional displays Tof sensors Efficient classification Databases Neural networks Gesture recognition Cameras Sensors Kernel
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Time-of-Flight based multi-sensor fusion strategies for hand gesture recognition

Time-of-Flight based multi-sensor fusion strategies for hand gesture recognition

Multi-HMM classification for hand gesture recognition using two differing modality sensors

A real-time applicable 3D gesture recognition system for automobile HMI

Multi-stage 3D segmentation for ToF based gesture recognition system

Multi-modal face recognition

A System to Interact with CAVE Applications Using Hand Gesture Recognition from Depth Data

Kinect Gesture Recognition: SVM vs. RVM

3D gesture classification with convolutional neural networks

Time-of-flight based multi-sensor fusion strategies for hand gesture recognition

Similar Documents

Time-of-Flight based multi-sensor fusion strategies for hand gesture recognition

Time-of-Flight based multi-sensor fusion strategies for hand gesture recognition

Multi-HMM classification for hand gesture recognition using two differing modality sensors

A real-time applicable 3D gesture recognition system for automobile HMI

Multi-stage 3D segmentation for ToF based gesture recognition system

Multi-modal face recognition

A System to Interact with CAVE Applications Using Hand Gesture Recognition from Depth Data

Kinect Gesture Recognition: SVM vs. RVM

3D gesture classification with convolutional neural networks

Time-of-flight based multi-sensor fusion strategies for hand gesture recognition