Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Outdoors augmented reality on mobile phone using loxel-based visual feature organization
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Indoor Positioning and Navigation with Camera Phones
IEEE Pervasive Computing
AI Goggles: Real-time Description and Retrieval in the Real World with Online Learning
CRV '09 Proceedings of the 2009 Canadian Conference on Computer and Robot Vision
Robust and unobtrusive marker tracking on mobile phones
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
Short Communication: Improving location awareness in indoor spaces using RFID technology
Expert Systems with Applications: An International Journal
Technical Section: Annotation in outdoor augmented reality
Computers and Graphics
Global pose estimation using multi-sensor fusion for outdoor Augmented Reality
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
SenseCam: a retrospective memory aid
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Hi-index | 0.00 |
Persons with Alzheimer's disease (AD) and their caregivers implement diverse strategies to cope with memory loss. A common strategy involves placing tags on drawers or removing cabinet doors to make their contents visible. This study describes the Ambient aNnotation System (ANS), aimed at assisting people suffering from AD and their caregivers with this task. The system has two main modules: The tagging subsystem allows caregivers to create and manage ambient annotations in order to assist people with memory problems. The second subsystem allows people with AD to use a mobile phone to recognize tags in the environment and to receive relevant information in the form of audio, text, or images. The identification of these tags is performed in real time by uploading images from the mobile phone to a server, which uses the SURF algorithm for object recognition. We describe the design and implementation of the system as well as results of the evaluation of its performance and efficiency. ANS can process query images approximately every 2 s and is able to locate users in their homes with a precision of 0.93. A usability study conducted with six subjects determined that audio notifications are more effective than vibrating notifications to alert the user about tags in the environment.