TY - GEN
T1 - Communication target object recognition for D2D connection with feature size limit
AU - Ok, Jiheon
AU - Kim, Soochang
AU - Kim, Young Hoon
AU - Lee, Chulhee
PY - 2015
Y1 - 2015
N2 - Recently, a new concept of device-to-device (D2D) communication, which is called "point-and-link communication" has attracted great attentions due to its intuitive and simple operation. This approach enables user to communicate with target devices without any pre-identification information such as SSIDs, MAC addresses by selecting the target image displayed on the user's own device. In this paper, we present an efficient object matching algorithm that can be applied to look(point)-and-link communications for mobile services. Due to the limited channel bandwidth and low computational power of mobile terminals, the matching algorithm should satisfy low-complexity, low-memory and realtime requirements. To meet these requirements, we propose fast and robust feature extraction by considering the descriptor size and processing time. The proposed algorithm utilizes a HSV color histogram, SIFT (Scale Invariant Feature Transform) features and object aspect ratios. To reduce the descriptor size under 300 bytes, a limited number of SIFT key points were chosen as feature points and histograms were binarized while maintaining required performance. Experimental results show the robustness and the efficiency of the proposed algorithm.
AB - Recently, a new concept of device-to-device (D2D) communication, which is called "point-and-link communication" has attracted great attentions due to its intuitive and simple operation. This approach enables user to communicate with target devices without any pre-identification information such as SSIDs, MAC addresses by selecting the target image displayed on the user's own device. In this paper, we present an efficient object matching algorithm that can be applied to look(point)-and-link communications for mobile services. Due to the limited channel bandwidth and low computational power of mobile terminals, the matching algorithm should satisfy low-complexity, low-memory and realtime requirements. To meet these requirements, we propose fast and robust feature extraction by considering the descriptor size and processing time. The proposed algorithm utilizes a HSV color histogram, SIFT (Scale Invariant Feature Transform) features and object aspect ratios. To reduce the descriptor size under 300 bytes, a limited number of SIFT key points were chosen as feature points and histograms were binarized while maintaining required performance. Experimental results show the robustness and the efficiency of the proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=84926619558&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84926619558&partnerID=8YFLogxK
U2 - 10.1117/12.2083321
DO - 10.1117/12.2083321
M3 - Conference contribution
AN - SCOPUS:84926619558
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Proceedings of SPIE-IS and T Electronic Imaging - Mobile Devices and Multimedia
A2 - Creutzburg, Reiner
A2 - Akopian, David
PB - SPIE
T2 - Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2015
Y2 - 10 February 2015 through 11 February 2015
ER -