This project aims to implement and further develop the use of image analysis in laboratory. By imaging more efficient laboratory operations. Additional effects, improved quality and increased traceability. The project is a continuation of SBUF Project 12 275 "Use of imaging in evaluating the adequacy of the roller bottle method". This section extends the project to include more methods, which creates synergies in the research but also synergy in the implementation of technology in the laboratory. The project aims at developping methods that could be in regular asphalt laboratory use. The main idea is to use standard components. Image analysis programs must, however, be developed for each application. The project has both a purely scientific approach but also a large part of the drafting procedure manuals and practical recommendations.
The aim of this project is to study the geometry and algebra of multiple camera systems. During the last decade there has been many attempts at making fully automatic structure and motion systems for ordinary camera systems. Much is known about minimal cases, feature detection, tracking and structure and motion recovery for ordinary cameras. Many automatic systems rely on small image motions in order to solve the correspondence problem. In combination with most cameras' small fields of view, this limits the way the camera can be moved in order to make good 3D reconstruction. The problem is significantly more stable with a large field of view. This has spurred research in so called omnidirectional or non-central cameras. A difficulty with ordinary cameras with or without large field of view is the inherent ambiguities that exists for structure and motion problem for ordinary cameras. There are ambiguous configurations for which structure and motion recovery is impossible.
Funded by the Development Fund of the Swedish
Construction Industry (SBUF).
Principal Investigator: Anders Heyden.
Below is a selection of the publications within the project.
Measuring and evaluating bitumen coverage of stones using two different digital image analysis methods
Källén, Hanna; Heyden, Anders; Åström, Kalle; Lindh, Per, 2016, Published in: Measurement
Towards grading Gleason score using generically trained deep convolutional neural networks
Källén, Hanna; Molin, Jesper; Heyden, Anders; Lundström, Claes; Åström, Kalle, 2016, Published at: International Symposium on Biomedical Imaging: From Nano to Macro
Measurement of Bitumen Coverage of Stones for Road Building, Based on Digital Image Analysis
Källen, Hanna; Heyden, Anders; Åström, Kalle; Lindh, Per, 2012, Published at: IEEE Workshop on Applications of Computer Vision, WACV 2012, Breckenridge, CO, USA, January 9-11, 2012
Last updated: 2012-04-24