Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/649586
Title: Low-Complexity Hand Gesture Recognition System for Continuous Streams of Digits and Letters
Authors: Stergios Poularakis;Ioannis Katsavounidis
subject: Gesture recognition|gesture spotting|low-complexity algorithm
Year: 2016
Publisher: IEEE
Abstract: In this paper, we propose a complete gesture recognition framework based on maximum cosine similarity and fast nearest neighbor (NN) techniques, which offers high-recognition accuracy and great computational advantages for three fundamental problems of gesture recognition: 1) isolated recognition; 2) gesture verification; and 3) gesture spotting on continuous data streams. To support our arguments, we provide a thorough evaluation on three large publicly available databases, examining various scenarios, such as noisy environments, limited number of training examples, and time delay in system's response. Our experimental results suggest that this simple NN-based approach is quite accurate for trajectory classification of digits and letters and could become a promising approach for implementations on low-power embedded systems.
URI: http://localhost/handle/Hannan/139075
http://localhost/handle/Hannan/649586
ISSN: 2168-2267
2168-2275
volume: 46
issue: 9
Appears in Collections:2016

Files in This Item:
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Title: Low-Complexity Hand Gesture Recognition System for Continuous Streams of Digits and Letters
Authors: Stergios Poularakis;Ioannis Katsavounidis
subject: Gesture recognition|gesture spotting|low-complexity algorithm
Year: 2016
Publisher: IEEE
Abstract: In this paper, we propose a complete gesture recognition framework based on maximum cosine similarity and fast nearest neighbor (NN) techniques, which offers high-recognition accuracy and great computational advantages for three fundamental problems of gesture recognition: 1) isolated recognition; 2) gesture verification; and 3) gesture spotting on continuous data streams. To support our arguments, we provide a thorough evaluation on three large publicly available databases, examining various scenarios, such as noisy environments, limited number of training examples, and time delay in system's response. Our experimental results suggest that this simple NN-based approach is quite accurate for trajectory classification of digits and letters and could become a promising approach for implementations on low-power embedded systems.
URI: http://localhost/handle/Hannan/139075
http://localhost/handle/Hannan/649586
ISSN: 2168-2267
2168-2275
volume: 46
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7219407.pdf2.68 MBAdobe PDFThumbnail
Preview File
Title: Low-Complexity Hand Gesture Recognition System for Continuous Streams of Digits and Letters
Authors: Stergios Poularakis;Ioannis Katsavounidis
subject: Gesture recognition|gesture spotting|low-complexity algorithm
Year: 2016
Publisher: IEEE
Abstract: In this paper, we propose a complete gesture recognition framework based on maximum cosine similarity and fast nearest neighbor (NN) techniques, which offers high-recognition accuracy and great computational advantages for three fundamental problems of gesture recognition: 1) isolated recognition; 2) gesture verification; and 3) gesture spotting on continuous data streams. To support our arguments, we provide a thorough evaluation on three large publicly available databases, examining various scenarios, such as noisy environments, limited number of training examples, and time delay in system's response. Our experimental results suggest that this simple NN-based approach is quite accurate for trajectory classification of digits and letters and could become a promising approach for implementations on low-power embedded systems.
URI: http://localhost/handle/Hannan/139075
http://localhost/handle/Hannan/649586
ISSN: 2168-2267
2168-2275
volume: 46
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7219407.pdf2.68 MBAdobe PDFThumbnail
Preview File