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Embedded special
the most basic concepts. To counter this separation ViiHM has developed a triad of Grand Challenges[6]
for
intelligent vision where success can best be achieved by working together. Te overall aim is to produce a general purpose, embodied, integrated, and adaptive visual system for intelligent robots, mobile and wearable technologies. Within this scope the Application Challenge is to augment and enhance vision in both the visually impaired and normally sighted, and to develop cognitive and personal assistants that can help those with low vision, the elderly, or simply the busy executive to deal with everyday tasks. Such aids might extend from wearable technologies that secretly prompt their user, to fully autonomous robots acting as caregivers and personal assistants. Here, it is important that robots think and act like humans, while avoiding the ‘uncanny valley’ effect[7] – where people are repulsed by robots appearing almost, but not exactly, like real humans. Tese applications will be
underpinned by the Technical Challenge of making low-power, small-footprint vision systems. To be acceptable, intelligent visual systems need to run all day on a single charge and be realised in discreet wearable devices. Such power and
REFERENCES [1]
www.viihm.org.uk (accessed
28/3/2017) [2]
Canny, J. (1986) A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine
Intelligence, 8(6):679–698. [3]
Marr, D., Hildreth, E. (1980) Theory of Edge Detection, Proceedings of the Royal Society of London. Series B, Biological Sciences, 207: 187–217,
doi:10.1098/rspb.1980.0020 [4]
Krizhevsky, A., Sutskever, I. and
Hinton, G.E. (2012) ImageNet Classification with Deep Convolutional Neural Networks in Advances in Neural Information Processing 25, MIT Press,
Cambridge, MA [5]
Masquelier T., Thorpe S.J. (2007) Unsupervised learning of visual features through spike timing dependent plasticity, PLoS Comput Biol 3(2): e31.
doi:10.1371/journal. pcbi.0030031 [6]
(accessed 28/3/2017) [7]
Mori, M. (2012) Translated by MacDorman, K. F.; Kageki, Norri. The uncanny valley, IEEE Robotics and Automation. 19 (2): 98–100. doi:10.1109/MRA.2012.2192811
www.viihm.org.uk/grand-challenges/
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space savings can be achieved by learning how biological systems are implemented at the physical, as well as algorithmic, layer. Finally the Teoretical Challenge of general purpose, integrated and adaptive vision will see visual systems that can operate ‘out of the box’ and in the wild, but continuously adapt to and learn from their environment. Learning the behaviours of their
users and co-workers, such systems will be robust and flexible. Tey will, for example, be able to
identify people and places, despite quite gross changes, to safely navigate new and altered environments and learn from experience over very long periods of time, with fixed and limited memory capacities. Tese are tough challenges but biology has shown them to be solvable. O
Andrew Schofield has a BEng in electronics engineering, a post- graduate diploma in psychology and a PhD in neuroscience. He is currently a senior lecturer in psychology at the University of Birmingham and member of the Centre for Computational Neuroscience and Cognitive Robotics. He also leads the ViiHM network. ViiHM is open to new members and is currently seeking industry partners to take part in a grant writing event in July 2017.
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June/July 2017 • Imaging and Machine Vision Europe 39
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