An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Ambient kitchen: designing situated services using a high fidelity prototyping environment
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Wearable context-aware food recognition for calorie monitoring
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
FoodLog: capture, analysis and retrieval of personal food images via web
CEA '09 Proceedings of the ACM multimedia 2009 workshop on Multimedia for cooking and eating activities
FiberBoard: compact multi-touch display using channeled light
Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Rapid specification and automated generation of prompting systems to assist people with dementia
Pervasive and Mobile Computing
Bathroom activity monitoring based on sound
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Analysis of chewing sounds for dietary monitoring
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Finding objects for blind people based on SURF features
BIBMW '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops
The french kitchen: task-based learning in an instrumented kitchen
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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We describe FoodBoard, an instrumented chopping board that uses optical fibers and embedded camera imaging to identify unpackaged ingredients during food preparation on its surface. By embedding the sensing directly, and robustly, in the surface of a chopping board we also demonstrate how surface contact optical sensing can be used to realize the portability and privacy required of technology used in a setting such as a domestic kitchen. FoodBoard was subjected to a close to real-world evaluation in which 12 users prepared actual meals. FoodBoard compared favourably with existing unpackaged food recognition systems, classifying a larger number of distinct food ingredients (12 incl. meat, fruit, vegetables) with an average accuracy of 82.8%.