Accenture
449, Route des Cretes
06902 Sophia Antipolis
FRANCE Research Interests - Real-time sensing, processing and actuation
- Vision and image based recognition
- Mobile computing and robotics
Related fields: sensing, actuation, real-time systems, computer vision, robotics, artificial intelligence, Bayesian networks, sensor fusion. Recent Projects Pocket Supercomputer (project leader): Leverages new high-speed low-latency telecommunications and networking infrastructure along with state-of-the art image and sound processing algorithms to create an experience of an always available, immensely powerful and intelligent 'contextually aware' device that fits in the user's palm or pocket. Current working prototype includes full-framerate visual object recognition, audio/video media streaming and recording, motion-controlled camera steering and 3D viewport control, transparent augmented reality overlays, speech generation, and more, all on a standard camera phone or wifi portable device. 3D Object Map Building (project leader): Using visual simultaneous localization and mapping technologies for handheld cameras (monocular SLAM), a full 3D map of the sensed environment is built and updated in real-time and virtual objects and instructions overlaid on a display depending on the device position and orientation. This allows training and maintenance instructions to be adapted in real-time depending on context such as viewing-angle, object state, and current step in the procedure. Mapping Robot (project leader): Visual object recognition and robot localization technologies enable an automated construction of metric maps denoting the positions of objects within an office or warehouse environment without having to tag any of the objects. The resulting maps can be used in post-disaster situations, for insurance and safety compliance verification, as well as for guided object retrieval, inventory and stock-taking. Tele-operation Table (project leader): Miniature replicas of vehicles are manually positioned and tracked on a table map surface and tele-operation commands are automatically sent to the vehicles to maneuver to these target positions. As the surface is a projected map, it is updated with global information and local sensor information from all the vehicles in real-time, giving a unified and natural control interface for homes, warehouses and larger geographical areas where multi-unit coordination and control is an issue. Remote Boss: As managers spend greater amounts of time traveling, they spend less and less time looking after their teams, thus reducing personal and professional interaction. This project explores the potential of using technology to embody the presence of a manager in a working space while he is away. Providing the manager with the tools to remotely control his counterpart technological representation, we aim at studying if it is feasible and useful to have this type of interaction. Publications Linaker, F. and Ishikawa, M. (2006) Robot Localization Using Vision, In Chen, K. and Wang, L. (Eds.) Trends in Neural Computation, Springer-Verlag, Vol. 35, pp. 483-512. Linaker, F. and Ishikawa, M. (2006). Real-time appearance-based Monte Carlo localization. In Robotics and Autonomous Systems, 54(3), pp. 205-220. Linaker, F. and Ishikawa, M. (2004). Rotation invariant features from omnidirectional camera images using a polar higher-order local autocorrelation feature extractor. In Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), Sendai, Japan, vol. 3, pp. 4026-4031. Linaker, F. and Ishikawa, M. (2004). Real-time appearance-based localization using Sequential Monte Carlo. In Proceedings of the Fourth Postech-Kyutech Joint Workshop on Neuroinformatics, Kitakyushu, Japan, pp. 75-76. Linaker, F. (2003). Unsupervised On-line Data Reduction for Memorisation and Learning in Mobile Robotics, Ph.D. thesis, Computer Science, University of Sheffield, UK. Laurio, K., Linaker, F. and Narayanan, A. (2002). Regular biosequence pattern matching with cellular automata. In Information Sciences, 146, pp. 89-101. Bakker, B., Linaker, F. and Schmidhuber, J. (2002). Reinforcement Learning in Partially Observable Robot Domains Using Unsupervised Event Extraction. In Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2002), Lausanne, Switzerland, vol. 1, pp. 938-943. Linaker, F. and Bergfeldt, N. (2002). Learning default mappings and exception handling. In Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior: From Animals to Animats 7 (SAB2002), pp. 181-182. Bergfeldt, N. and Linaker, F. (2002). Self-Organized Modulation of a Neural Robot Controller. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2002), pp. 495-500. Laurio, K., Linaker, F. and Narayanan, A. (2002). Recognizing PROSITE Patterns with Cellular Automata. In Proceedings of 6th Joint Conference on Information Sciences, pp. 1174-1179. Linaker, F. and Jacobsson, H. (2001). Learning Delayed Response Tasks through Unsupervised Event Extraction. In International Journal of Computational Intelligence and Applications, 1(4), pp. 413-426. Linaker, F. (2001). From time-steps to events and back. In Proceedings of The 4th European Workshop on Advanced Mobile Robots (EUROBOT '01), Lund, Sweden, pp. 147-154. Linaker, F. and Jacobsson, H. (2001). Mobile Robot Learning of Delayed Response Tasks through Event Extraction: A Solution to the Road Sign Problem and Beyond. Seventeenth International Joint Conference on Artificial Intelligence (IJCAI 01), pp. 777-782. Linaker, F. and Laurio, K. (2001). Environment Identification by Alignment of Abstract Sensory Flow Representations. In Advances in Neural Networks and Applications, WSES Press, pp. 229-234. Niklasson, L. F. and Linaker, F. (2000). Distributed Representations for Extended Generalisation, In Connection Science, Carfax Publishers, 12(3/4), pp. 299-314. Linaker, F. and Niklasson, L. (2000). Extraction and Inversion of Abstract Sensory Flow Representations. In Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior: From Animals to Animats 6 (SAB2000), MIT Press, pp. 199-208. Linaker, F. and Niklasson, L. (2000). Time series segmentation using an adaptive resource allocating vector quantization network based on change detection. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000), IEEE Computer Society, pp. 323-328. Linaker, F. and Niklasson, L. (2000). Sensory-flow Segmentation using a Resource Allocating Vector Quantizer. In Advances in Pattern Recognition: Joint IAPR International Workshops SSPR 2000 and SPR 2000 (S+SSPR 2000), Springer, pp. 853-862. Education - Postdoc, Kyushu Institute of Technology, Japan
- Ph.D. in Computer Science, University of Sheffield, UK
- M.Sc. in Computer Science, University of Skovde, Sweden
- B.Sc. in Computer Science, University of Skovde, Sweden
Personal Interests Movies, traveling, enjoying good food, listening to live music, computers. To Top |