Research Projects

Continuous surveillance of animal welfare in housed dairy cows using image analysis technology

The project aims to identify biomarkers of animal health and welfare in order to increase knowledge of animal behavior and basic needs in general, and the importance of locomotion disorders in particular. With larger herds and higher production requirements less space is given for supervision of the individual animal. In milk production a reduction of the estimated management hours per cow and year has been halved from 40 h in a tie stall barn to 20 h in a cubicle system with automatic milking (AMS). This means that it will be more difficult to detect disease before it endangers animal welfare. We also know that euthanized and dead cows increase in cubicle systems, which is an indication that the disease was not detected in time and that the animal suffered unnecessarily. Using image analysis the animals are supervised with cameras and the information is analyzed mathematically. With so called algorithms the animals are identified as well as their positions, movements and interactions with other animals and the equipment. The system can identify animals that have disease problems or disturbed behavior in order to be able to treat these in time or correct the cause of the disorders before it's gone too far. In addition, the system can also be used to evaluate and correct risk factors for disturbed animal behavior that affects the function of the system and threaten to cause disease problems or disturbed behavior. Healthy, sustainable cows also mean less environment load.

Main applicant: 

Christer Bergsten
SLU Biosystem och teknologi


Kalle Åström
LU, Matematikcentrum 

Anders Herlin
SLU, Biosystem och teknologi 

Funded by FORMAS. 
Period: 2014-2016.

DOGS (SWElife)



Global Indoor Positioning in 3D

Vinnova funded project together with Combain

InDeV (In Depth Analysis of Vulnerable Road Users)

Robust Methods for 3D-Reconstruction of Static and Non-Static Objects, Scenes and Environments

Semantic Mapping and Visual Navigation for Smart Robots

Sony - research collaborations on various computer vision topics

Quality Enhancement in Laboratory Tests using Image Analysis

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.
Period: 2010-2015.

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