Sediment grains
New project associated with Visual Intelligence on reduce bottlenecks related to uncertainty estimates in classification by a combined statistical and machine learning approach. The project is on recognition of specific objects at and within the seabed from several image types.
The project is a collaboration between Department of Mathematics and Statistics (IMS host) and Department of Geosciences (IG) at UiT - the Arctic university of Norway.
Project Leader at IMS: Prof. Fred Godtlibsen
PIs at IG: Reseacher Steffen Aagaard Sørensen, prof. Morten Hald
Funding: Research council of Norway, through the IKTPLUSS program and collaborating companies Multiconsult and Argeo.
Summary:
The amount of data collected is rapidly increasing. Artificial intelligence is key to extract crucial information from huge amounts of data. Classifiers suggests a class affiliation based on the input data. This problem domain is denoted classification, and machine learning and statistical techniques have proven very useful. At present, these two cultures have only to a limited extent, be able to take out synergies.
Exploring the synergies are the next step to move forward. The focus of this project is to join their strengths to get rid of bottlenecks within classification in particular related to uncertainty estimation, transferable methodology and highly heterogenous data sets.
As test cases, we focus on specific tasks that are important for both the collaboration companies and for the geoscientific community. In particular, we aim to develop a product that automatize the recognition of specific objects at and within the seabed from several image acquisition types (examples in figure below) .Moreover, we will demonstrate how a time-consuming manual procedure can be replaced by an automatic system for identification and classification of objects carrying information about climatology and environmental conditions.Successful algorithms developed in the present project will pave the way for a fully automatic system, so that the human interaction of the whole process, at some point in the future, largely can be omitted.
For more information please contact: Fred Godtliebsen
Steffen Aagaard Sørensen or Morten Hald
Heggeli's primary work tasks involve managing, maintaining and optimizing the GPU cluster at UiT Machine Learning (ML) Group in Tromsø, while serving as an important advising resource for our UiT-based VI researchers.
The company's interest in Visual Intelligence is to utilize new developments in artificial intelligence (AI) to improve and automate interpretations of data, while providing the centre with data, problem formulations, and different forms of guidance.
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