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FRINATEK-Fri prosj.st. mat.,naturv.,tek

Next Generation 3D Machine Vision with Embedded Visual Computing

Alternative title: 3D Maskinsyn med innebygd Visuell Læring

Awarded: NOK 12.0 mill.

This research aims to initiate an automated process deploying high-precision 3D sensing and online (embedded) visual learning engines that operate in natural environments and are able to adapt in a holistic lifetime manner to changes during operations. It targets establishing a groundbreaking technological concept for 3D machine vision as an interdisciplinary initiative linking cost effective and reliable depth sensing with advanced computer vision and embedded artificial intelligence to tackle challenges posed by time-varying visual scene perception in natural environments. The combination of both recent breakthrough developments in 3D sensing (i.e. solid state LiDAR) and in mobile computing (i.e. embedded neural computing engine) is the basis for this research to tackle real-time monitoring and visual analysis using a network of tiny, low-cost and low-power smart 3D cameras, towards digital management services for operations and processes. This research supports building an internationally relevant R&D team on 3D machine vision towards enabling digitalization capabilities. In the past period, we worked along three lines. On the first line, we have built a 3D vision sensor with high resolution and depth precision, based on LIDAR technology and multiple mirrors that concentrate the light into the field of view. On the second line, we worked and are working on building a large database of 3D datasets, using the 3D vision sensor that are annotated for further research. On the third line, we worked on few methods that analyse the data and provide semantics for interpretation and understanding the visual scene. The works resulted into 9 papers published in IEEE transactions and conference proceedings. In the next period, we will continue working on the method and intensify our collaboration with Tohoku University in Japan as the PhD student of UiS will be having a 6-month stay in Japan to develop joint publications.

The overall objective of this research is to give up the currently manual and time-consuming operational processes with well-composed digitalization using real-time 3D vision and learning processes. This research aims to initiate an automated process deploying high-precision 3D sensing and online (embedded) visual learning engines that operate in natural environments and are able to adapt in a holistic lifetime manner to changes during operations. It targets establishing a groundbreaking technological concept for 3D machine vision as an interdisciplinary initiative linking cost effective and reliable depth sensing with advanced computer vision and embedded artificial intelligence to tackle challenges posed by time-varying visual scene perception in natural environments. The combination of both recent breakthrough developments in 3D sensing (i.e. solid state LiDAR) and in mobile computing (i.e. embedded neural computing engine) is the basis for this research to tackle real-time monitoring and visual analysis using a network of tiny, low-cost and low-power smart 3D cameras, towards digital management services for operations and processes. This research has a potential to a radical change in many parts of the value chain in the industry and society and selected scenarios will be designed for impact evaluation and opening up integration opportunities. This activity fits the key strategical development of NORCE and its perspective to be number 1 in Norway within the next 5 years in machine vision innovation. This research supports building an internationally-relevant R&D team on 3D machine vision towards enabling digitalization capabilites. NORCE has the role to develop Norway as leading hub with excellence and high-level of expertise on innovation for 3D machine vision as an enabler for digital processes and services.

Publications from Cristin

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FRINATEK-Fri prosj.st. mat.,naturv.,tek