All Outputs (3)

FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net (2019)
Book Chapter
Jafari, M., Li, R., Xing, Y., Auer, D., Francis, S., Garibaldi, J., & Chen, X. (2019). FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net. In Image and Graphics: 10th International Conference, ICIG 2019, Beijing, China, August 23–25, 2019, Proceedings, Part II (529-537). Springer Verlag. https://doi.org/10.1007/978-3-030-34110-7_44

© 2019, Springer Nature Switzerland AG. In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation. It is based on a well-established supervised end-to-end DCNN model, known as U-net. U-net is firs... Read More about FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net.

The transfer of evolved artificial immune system behaviours between small and large scale robotic platforms
Book Chapter
Whitbrook, A., Aickelin, U., & Garibaldi, J. M. The transfer of evolved artificial immune system behaviours between small and large scale robotic platforms. In P. Collet, N. Monmarché, P. Legrand, M. Schoenauer, & E. Lutton (Eds.), Artificial evolution: 9th International Conference = Evolution Artificielle, EA 2009: Strasbourg, France, October 26-28, 2009: revised selected papers. Springer

This paper demonstrates that a set of behaviours evolved in simulation on a miniature robot (epuck) can be transferred to a much larger scale platform (a virtual Pioneer P3-DX) that also differs in shape, sensor type, sensor configuration and progra... Read More about The transfer of evolved artificial immune system behaviours between small and large scale robotic platforms.

An idiotypic immune network as a short-term learning architecture for mobile robots
Book Chapter
Whitbrook, A., Aickelin, U., & Garibaldi, J. M. An idiotypic immune network as a short-term learning architecture for mobile robots. In P. Bentley, D. Lee, & S. Jung (Eds.), Artificial immune systems: 7th international conference, ICARIS 2008, Phuket, Thailand, August 10-13, 2008: proceedings. Springer

A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations that use a Genetic Algorith... Read More about An idiotypic immune network as a short-term learning architecture for mobile robots.