Dissertations

1. Date: 100520 
   Contributions to 3D Image Analysis using Discrete Methods and Fuzzy Techniques: With Focus on Images from Cryo-Electron Tomography 
   Student: Magnus Gedda 
   Supervisor: Stina Svensson 
   Assistant Supervisors: Ewert Bengtsson; Carolina Wählby 
   Opponent: Punam Saha, Dept. of Electrical and Computer Engineering, The University of Iowa, Iowa City, USA 
   Committee: Martin Rydmark, Göteborgs University, Michael Felsberg, Linköpings University, Ingela Nystöm, CBA, Lars Norlen, Karolinska Institutet, Nataša Sladoje, University of Novi Sad, Serbia.
   Publisher: Acta Universitatis Upsaliensis, ISBN: 978-91-554-7768-4
   Abstract: With the emergence of new imaging techniques, researchers are always eager to push the boundaries by examining objects either smaller or further away than what was previously possible. The development of image analysis techniques has greatly helped to introduce objectivity and coherence in measurements and decision making. It has become an essential tool for facilitating both large-scale quantitative studies and qualitative research. In this Thesis, methods were developed for analysis of low-resolution (in respect to the size of the imaged objects) three-dimensional (3D) images with low signal-to-noise ratios (SNR) applied to images from cryo-electron tomography (cryo-ET) and fluorescence microscopy (FM). The main focus is on methods of low complexity, that take into account both grey-level and shape information, to facilitate large-scale studies. Methods were developed to localise and represent complex macromolecules in images from cryo-ET. The methods were applied to Immunoglobulin G (IgG) antibodies and MET proteins. The low resolution and low SNR required that grey-level information was utilised to create fuzzy representations of the macromolecules. To extract structural properties, a method was developed to use grey-level-based distance measures to facilitate decomposition of the fuzzy representations into sub-domains. The structural properties of the MET protein were analysed by developing a analytical curve representation of its stalk. To facilitate large-scale analysis of structural properties of nerve cells, a method for tracing neurites in FM images using local path-finding was developed. Both theoretical and implementational details of computationally heavy approaches were examined to keep the time complexity low in the developed methods. Grey-weighted distance definitions and various aspects of their implementations were examined in detail to form guidelines on which definition to use in which setting and which implementation is the fastest. Heuristics were developed to speed up computations when calculating grey-weighted distances between two points. The methods were evaluated on both real and synthetic data and the results show that the methods provide a step towards facilitating large-scale studies of images from both cryo-ET and FM.
2. Date: 100604 
   Automatic Analysis of Log end Face Images in the Sawmill Industry 
   Student: Kristin Norell 
   Supervisor: Gunilla Borgefors 
   Assistant Supervisors: Mats Nylinder, Dept. of Forest Products, SLU; Lars Björklund, SDC IT-company for the Swedish forestry sector 
   Opponent: Karl Entacher, Information Technology and Systems Management, Salzburg University, Austria 
   Committee: Gunnar Sparr, Dept. of Mathematics, Lund University; Björn Kruse, Dept. of Science and Technology, Linköping university; Anders Grönlund, Dept. of Wood Technology, Luleâ University 
   Publisher: Acta Universitatis agriculturae Sueciae, ISBN: 978-91-576-7502-6 
   Abstract: At present grading of sawlogs in a sawmill relies on visual inspection wherein a human expert grades a log every few seconds as it passes on a conveyor belt. This tedious and difficult work is prone to substantial inter- and intra-grader variability.

   This dissertation presents methods for automatic analysis of end faces from Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst). Two features are extracted: the pith (centre core) position and the number of annual rings. The pith detection uses local orientations to estimate the centre of the annual rings in a manner that is robust to disturbances as knots and cracks as well as partial coverage of dirt or snow. The number of annual rings is counted using the polar distance transform, a tool developed here. This transform combines the image intensity and the circular shape of the rings so that the annual ring pattern can be outlined in rough and noisy images. First the marks on end faces from uneven sawing are removed using an automatic method developed in this work.

   The data are images of untreated end faces mostly acquired at sawmills. A large amount of the data was imaged using a camera mounted above a conveyor belt at a sawmill, collecting images every month during one year. In total, the data consists of over 4000 images of pine and spruce. In this dissertation an algorithm for generating synthetic log end face images is also presented. The synthetic data can be used as a tool for developing image analysis methods.

   The annual ring measurements were thoroughly evaluated on pine end face images acquired using the mounted end face camera. This evaluation shows that the method performs equally well as an experienced manual grader for grading the logs into quality classes. The method can thus be used as a component of an automatic grading system, overseen by a manual grader.

3. Date: 101203 
   Measuring and Modelling Parameters From Hyperspectral Sensors for Site-specific Crop Protection 
   Student: Anders Larsolle 
   Supervisor: Girma Gebresenbet, Prof. Dept of Energy and Technology, SLU 
   Assistant Supervisor: Gunilla Borgefors 
   Opponent: Oliver Hensel, Kassel University, Witzenhausen, Germany 
   Committee: Rainer Lenz, Dept. of Science and Technology, Linköping University Campus Norrköping 
   Anneli Lundkvist, Dept. of Crop Production Ecology, SLU, Uppsala 
   Jan-Erik Mattsson, Dept. of Agriculture-farming systems, technology and product quality, SLU Alnarp 
   Publisher: Faculty of Natural Resources and Agricultural Sciences, SLU, ISBN:978-91-576-7539-2
   Abstract: This thesis sought to optimise systems for plant protection in precision agriculture through developing a field method for estimating crop status parameters from hyperspectral sensors, and an empirical model for estimating the required herbicide dose in different parts of the field.

   The hyperspectral reflectance measurements in the open field took the form of instantaneous spectra recording using an existing method called feature vector based analysis (FVBA), which was applied on disease severity. A new method called iterative normalisation based analysis (INBA) was developed and evaluated on disease severity and plant biomass. The methods revealed two different spectral signatures in both disease severity and plant density data. By concentrating the analysis on a 12% random subset of the hyperspectral field data, the unknown part of the data could be estimated with 94-97% coefficient of determination.

   The empirical model for site-specific weed control combined a model for weed competition and a dose response model. Comparisons of site-specific and conventional uniform spraying using model simulations showed that site-specific spraying with the uniform recommended dose resulted in 64% herbicide saving. Comparison with a uniform dose with equal weed control effect resulted in 36% herbicide saving.

   The methods developed in this thesis can be used to improve systems for site-specific plant protection in precision agriculture and to evaluate site-specific plant protection systems in relation to uniform spraying. Overall, this could be beneficial both for farm finances and for the environment.