search.noResults

search.searching

note.createNoteMessage

search.noResults

search.searching

orderForm.title

orderForm.productCode
orderForm.description
orderForm.quantity
orderForm.itemPrice
orderForm.price
orderForm.totalPrice
orderForm.deliveryDetails.billingAddress
orderForm.deliveryDetails.deliveryAddress
orderForm.noItems
www.imveurope.com


@imveurope


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/


When you need low cost, high performance and ultra- reliability in a machine vision system, there’s a JAI Go Series camera to help you reach your destination. Choose the perfect combination of imager, resolution, pixel size and interface to fit your exact requirements. From our most affordable 2.35 megapixel GO-2401- PGE, to our newest 5.1-megapixel GO-5100-USB model, these cameras combine small size, light weight (only 46 grams), and MTBF ratings equal to more than 20 years of continuous 24 x 7 operation. Ready to make your system Go? Find your best route at www.jai.com/go


The GO Series...


Small and affordable industrial cameras


Latest CMOS sensors Small size (29 x 29 x 52 mm) MTBF > 200,000 hours


Camera Link, GigE Vision, or USB3 Vision


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.


JAI.COM GO your own way...


GO-24OO 2.35 MP IMX 174


GO-24OO-PGE 48.8 fps


GO-51OO 5.1 MP IMX 25O


GO-5OOO 5.O MP Lince5M


GO-24OO-USB 159 fps


GO-51O1 5.1 MP IMX 264


GO-24OO-PMCL 165.5 fps


GO-24O1 2.35 MP IMX 249


GO-24O1-PGE 41 fps


GO-51O1-PGE 22.7 fps


GO-51OO-PGE 22.7 fps


GO-51OO-USB 74 fps


Europe, Middle East & Africa - JAI A/S camerasales.emea@jai.com / +45 4457 8888


Asia Pacific - JAI Ltd. camerasales.apac@jai.com / +81 45-440-0154


Americas - JAI Inc. camerasales.americas@jai.com / + 1 408 383 0300


www.imveurope.com


@imveurope


June/July 2017 • Imaging and Machine Vision Europe 39


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56