On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
A New Way to Represent the Relative Position between Areal Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Fingerprint Identification Using Delaunay Triangulation
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Localization from Connectivity in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
WearIT@work: Toward Real-World Industrial Wearable Computing
IEEE Pervasive Computing
The Representation and Matching of Images Using Top Points
Journal of Mathematical Imaging and Vision
Real-time trajectory estimation in mobile ad hoc networks
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Organizing a global coordinate system from local information on an ad hoc sensor network
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Quality of Trilateration: Confidence-Based Iterative Localization
IEEE Transactions on Parallel and Distributed Systems
Algorithm that mimics human perceptual grouping of dot patterns
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
On the Relative and Absolute Positioning Errors in Self-Localization Systems
IEEE Transactions on Signal Processing
Hi-index | 0.00 |
Positioning of things, devices and people is the fundamental technology in ubiquitous computing. However, few literature has discussed the impact of positioning errors due to localization algorithm properties such as ranging noise and deployment of anchors on people's identification of objects. Since several factors such as relative distance, relative angles and grouping of objects are intricately related with each other in such identification, it is not an easy task to investigate its characteristics. In this paper, we propose criteria to assess the "accuracy" of the estimated positions in identifying the objects. The criteria are helpful to design, develop and evaluate localization algorithms that are used to tell people the location of objects. Augmented reality is a typical example that needs such localization algorithms. To model the criteria without ambiguity, we prove that the Delaunay triangulation well-captures natural human behavior of finding similarity between estimated and true positions. We have examined different localization algorithms to observe how the proposed model quantifies the properties of those algorithms. Subjective testing has also been conducted using questionnaires to justify our quantification sufficiently renders human intuition.