Document Type
Conference Proceeding
Publication Date
11-2012
Abstract
The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data. Emerging solutions are using ontologies for expressive representation of concepts in the domain of sensing and perception, which enable advanced integration and interpretation of heterogeneous sensor data. The computational complexity of OWL, however, seriously limits its applicability and use within resource-constrained environments, such as mobile devices. To overcome this issue, we employ OWL to formally define the inference tasks needed for machine perception – explanation and discrimination – and then provide efficient algorithms for these tasks, using bit-vector encodings and operations. The applicability of our approach to machine perception is evaluated on a smart-phone mobile device, demonstrating dramatic improvements in both efficiency and scale.
Repository Citation
Henson, C. A.,
Thirunarayan, K.,
& Sheth, A. P.
(2012). An Efficient Bit Vector Approach to Semantics-Based Machine Perception in Resource-Constrained Devices. Lecture Notes in Computer Science, 7649, 149-164.
https://corescholar.libraries.wright.edu/knoesis/622
DOI
10.1007/978-3-642-35176-1_10
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
Presented at the 11th International Semantic Web Conference, Boston, MA, November 11-15, 2012.
Attached is the unpublished, author's version of this proceeding. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-35176-1_10.
The presentation that accompanied this proceeding can be found at http://www.slideshare.net/andrewhenson/iswc2012-semanticsbasedmachineperception-15189325.