This thesis introduces a new signal transform, called polynomial expansion, and based on this develops novel methods for estimation of orientation and motion. The methods are designed exclusively in the spatial domain and can be used for signals of any dimensionality. Two important concepts in the use of the spatial domain for signal processing is projections into subspaces, e.g. the subspace of second degree polynomials, and representations by frames, e.g. wavelets. It is shown how these concepts can be unified in a least squares framework for representation of nite dimensional vectors by bases, frames, subspace bases, and subspace frames. This framework is used to give a new derivation of normalized convolution, a method for signal analysis that takes uncertainty in signal values into account and also allows for spatial localization of the analysis functions. Polynomial expansion is a transformation which at each point transforms the signal into a set of expansion coefficients with respect to a polynomial local signal model. The expansion coefficients are computed using normalized convolution. As a consequence polynomial expansion inherits the mechanism for handling uncertain signals and the spatial localization feature allows good control of the properties of the transform. It is shown how polynomial expansion can be computed very efficiently. As an application of polynomial expansion, a novel method for estimation of orientation tensors is developed. A new concept for orientation representation, orientation functionals, is introduced and it is shown that orientation tensors can be considered a special case of this representation. By evaluation on a test sequence it is demonstrated that the method performs excellently. Considering an image sequence as a spatiotemporal volume, velocity can be estimated from the orientations present in the volume. Two novel methods for velocity estimation are presented, with the common idea to combine the orientation tensors over some region for estimation of the velocity field according to a parametric motion model, e.g. affine motion. The first method involves a simultaneous segmentation and velocity estimation algorithm to obtain appropriate regions. The second method is designed for computational efficiency and uses local neighborhoods instead of trying to obtain regions with coherent motion. By evaluation on the Yosemite sequence, it is shown that both methods give substantially more accurate results than previously published methods. Another application of polynomial expansion is a novel displacement estimation algorithm, i.e. an algorithm which estimates motion from only two consecutive frames rather than from a whole spatiotemporal volume. This approach is necessary when the motion is not temporally coherent, e.g. because the camera is affected by vibrations. It is shown how moving objects can robustly be detected in such image sequences by using the plane+parallax approach to separate out the background motion. To demonstrate the power of being able to handle uncertain signals it is shown  how normalized convolution and polynomial expansion can be computed for interlacedvideo signals. Together with the displacement estimation algorithm this gives a method to estimate motion from a single interlaced frame.

The art of medical computed tomography is constantly evolving and the last years have seen new ground breaking systems with multi-row detectors. These tomographs are able to increase both scanning speed and image quality compared to the single-row systems more commonly found in hospitals today. This thesis deals with three-dimensional image reconstruction algorithms to be used in future generations of tomographs with even more detector rows than found in currentmultirow systems. The first practical algorithm for three-dimensional reconstruction from conebeamprojections acquired from a circular source trajectory is the FDKmethod. We present a novel version of this algorithm that produces images of higher quality. We also formulate a version of the FDK method that performs the backprojection in O(N3 logN) steps instead of the O(N4) steps traditionally required. An efficient way to acquire volumetric patient data is to use a helical source trajectory together with a multi-row detector. We present an overview of existing reconstruction algorithms for this geometry. We also present a new family of algorithms, the PI methods, which seem to surpass other proposals in simplicity while delivering images of high quality. The detector used in the PI methods is limited to a window that exactly fits the cylindrical section between two consecutive turns of the helical source path. A rebinning to oblique parallel beams yields a geometry with many attractive properties. The key property behind the simplicity of the PI methods is that each object point to be reconstructed is illuminated by the source during a rotation of exactly half a turn. This allows for fast and simple reconstruction.

The thesis describes novel methods for image motion computation and template matching. A multiscale algorithm for energy-based estimation and representation of local spatiotemporal structure by second order symmetric tensors is presented. An efficient spatiotemporal implementation of a signalmodellingmethod called normalized convolution is described. This provides a means to handle signals with varying degree of reliability. As an application of the above results, a smooth pursuit motion tracking algorithm that uses observations of both targetmotion and position for camera head control and motion prediction is described. The target is detected using a novel motion field segmentation algorithm which assumes that the motion fields of the target and its immediate vicinity, at least occasionally, each can be modelled by a single parameterized motion model. A method to eliminate camera-induced background motion in the case of a pan/tilt rotating camera is suggested. In a second application, a high-precision image motion estimation algorithm performing clustering in motion parameter space is developed. The algorithm, which can handle multiple motions by simultaneous motion parameter estimation and image segmentation, iteratively maximizes the posterior probability of the motion parameter set given the observed local spatiotemporal structure tensor field. The probabilistic formulation provides a natural way to incorporate additional prior information about the segmentation of the scene into the objective function. A simple homotopy continuation method (embedding algorithm) is used to increase the likelihood of convergence to a nearoptimal solution. The final part of the thesis is concerned with tracking of (partially) occluded targets. An algorithm for target tracking in head-up display sequences is presented. The method generalizes cross-correlation coefficient matching by introducing a signal confidencebased distance metric. To handle target shape changes, a method for template mask shape-adaptation based on geometric transformation parameter optimisation is introduced. The presence of occluding objects makes local structure descriptors (e.g., the gradient) unreliable, which means that only pixelwise comparisons of target and template can be made, unless the local structure operators are modified to take into account the varying signal certainty. Normalized convolution provides the means for such a modification. This is demonstrated in a section on phase-based target tracking, which also contains a presentation of a generic method for tracking of occluded targets by combining normalized convolution with iterative reweighting.

Three-dimensional (3D) volume data has become increasingly common with the emergence and wide availability of modern 3D image acquisition techniques. The demand for computerized analysis and visualization techniques is constantly growing to utilize the abundant information embedded in these data. This thesis consists of three parts. The first part presents methods of analyzing  3D volume data by using second derivatives. Harmonic functions are used to combine the non-orthogonal second derivative operators into an orthogonal basis. Three basic features, magnitude, shape, and orientation, are extracted from the second derivative responses after diagonalizing the Hessian matrix. Two applications on magnetic resonance angiography (MRA) data are presented. One of them utilizes a scale-space and the second order variation to enhance the vascular  system by discriminating for string structures. The other one employs the local shape information to detect cases of stenosis. The second part of the thesis discusses some modifications of the fast marching method in 2D and 3D space. By shifting the input and output grids relative to each other, we show that the sampled cost functions are used in a more consistent way. We present new algorithms for anisotropic fast marching which incorporate orientation information during the marching process. Three applications illustrate the usage of the fast marching methods. The first one extracts a guide wire as a minimum-cost path on a salience distance map of a line detection result of a flouroscopy image. The second application extracts the vascular tree from a whole bodyMRA volume. In the third application, a 3D guide wire is reconstructed from a pair of biplane images using the minimum-cost path formulation. The third part of the thesis proposes a new frame-coherent volume rendering algorithm. It is an extension of the algorithm by Gudmundsson and Rand´en (1990). The new algorithm is capable of efficiently generating rotation sequences around an arbitrary axis. Essentially, it enables the ray-casting procedure to quickly approach the hull of the object using the so called shadow-lines recorded from the previous frame.

This thesis presents some concepts and methods for low level computer vision and learning, with object recognition as the primary application. An efficient method for detection of local rotational symmetries in images is presented. Rotational symmetries include circle patterns, star patterns, and certain high curvature patterns. The method for detection of these patterns is based on local moments computed on a local orientation description in double angle representation, which makes the detection invariant to the sign of the local direction vectors. Some methods are also suggested to increase the selectivity of the detection method. The symmetries can serve as feature descriptors and interest points for use in hierarchical matching structures for object recognition and related problems. A view-based method for 3D object recognition and estimation of object pose from a single image is also presented. The method is based on simple feature vector matching and clustering. Local orientation regions computed at interest points are used as features for matching. The regions are computed such that they are invariant to translation, rotation, and locally invariant to scale. Each match casts a vote on a certain object pose, rotation, scale, and position, and a joint estimate is found by a clustering procedure. The method is demonstrated on a number of real images and the region features are compared with the SIFT descriptor, which is another standard region feature for the same application. Finally, a new associative network is presented which applies the channel representation for both input and output data. This representation is sparse and monopolar, and is a simple yet powerful representation of scalars and vectors. It is especially suited for representation of several values simultaneously, a property that is inherited by the network and something which is useful in many computer vision problems. The chosen representation enables us to use a simple linear model for non-linear mappings. The linear model parameters are found by solving a least squares problem with a non-negative constraint, which gives a sparse regularized solution.

The overall aim of this thesis is to describe the synthesisof branched aliphatic polycarbonates and show the potentialapplication of these polymers in the field of powder coatings.The characterization of the polycarbonates was facilitated bythe study of a series of bis-MPAdendrimers, which served asreference of perfectly branched polymers. In addition anε-caprolactone monomer with a bis-MPA pendant unit wassynthesized and polymerized in order to find an alternativesynthetic route hyperbranched polyesters. Cationic ring opening polymerization (CROP) of neopentylenecarbonate was utilized to synthesize a number of branchedpolymers. This monomer was chosen because the thermalproperties of poly(neopentylene carbonate) are promising forpowder coating applications. CROP enabled the synthesis ofbranched polymers, which are of great interest because of theirreduced melt viscosity and high functionality compared tolinear polymers. CROP of neopentylene carbonate, with a seriesof polyols including a hyper-branched polyester (Boltorn H30),in the presence of fumaric acid resulted in polymers withvaried degrees of branching and molecular weights ranging from2 000-100 000 g mol-1. Neopentylene carbonate was also used in the synthesispolycarbonate macromonomers possessing a polymerizablemethacrylate functional group at one of the chain ends. Inthjis case hydroxyethylmethacrylate was used as initiatopr inthe reaction catalyzed by methyl sulfonic acid. The MW of thismacromonomer was 2500 g mol-1and it was used to produce polymer brushes byfree radical and atom transfer radical polymerization(ATRP). An ε-caprolactone bearing a pendant bis-MPA wassynthesized and polymerized by Sn(Oct)2. Copolymerization with ε-caprolactone wasperformed to introduce linear segm,ents between the branchingpoints. The molecular weights of the homopolymer and thecopolymer were 3000 and 8000 g mol-1respectively as determined by Size exclusionchromatography (SEC) calibrated with polystyrene. SEC was used to analyze a series of bis-MPA dendrimers, andthe results were used to characterize the branchedpolycarbonates. The Mark-Houwink plots of the dendrimers wereproduced and used as reference in the characterization of thepolycarbonates. The thermal and rheological characterization of thepolycarbonates showed that the polymers were semi-crystallinewith Tgbetween 20-30 °C and Tmbetween 90-120 °C. Rheology measurementsshowed that the architecture had a considerable impact on themelt viscosity. Coating films were produced by UV curing of a series oflinear polycarbonates were functionalized with methacrylicgroups. The storage stability was tested for one week at 45°C, no coagulation of the particles was observed at theend of the testing period. The cured films showed good chemicalresistance and flexibility.

This thesis addresses computer gameplay activities in terms of the physical handling of a game, players’ meaning-making activities, and how these two processes are closely interrelated. It is examined in greater detail which role the body plays in gameplay, but also how gameplay is shaped by sociocultural factors outside the game, including different kind of tools and players’ participation in community practices. An important step towards an understanding of these key factors and their interaction is the consideration of gameplay as situated activity where players who actively engage with games are situated in both the physical world and the virtual in-game world. To analyse exactly how players interact with both worlds, two case studies on two different games have been carried out, and three different levels of situatedness are identified and discussed in detail in this thesis, on the basis of existing theories within situated cognition research.

The focus of this licentiate thesis is air transportation and especially the logistics at an airport. The concept of airport logistics is investigated based on the following definition: Airport logistics is the planning and control of all resources and information that create a value for the customers utilizing the airport. As a part of the investigation, indicators for airport performance are considered. One of the most complex airport processes is the turn-around process. The turn-around is the collective name for all those activities that affect an aircraft while it is on the ground. In the turn-around process almost all of the actors operating at the airport are involved and the process is connected to other activities which take place on airside, in the terminal as well as in the control tower. This makes the turn-around process an excellent focal point for studying airport logistics. A detailed conceptual model of the turn-around process is developed and a simplified version of this is implemented in a computerized simulation program. The aim of the simulation is to enable the assessment of various logistical operations involved in turn-around, and their impact on airport performance. The flow of support vehicles serving the aircraft with fuel, food, water etc during the turn-around is received particular attention. The output from the model can be used as indicators for the airport performance. One of the most interesting support flows to study is the flow of de-icing trucks. De-icing is performed to remove ice and snow from the aircraft body and to prevent the build up of new ice. There is a limited time span prior the take off, within which de-icing has to be performed. This makes the time of service critical. An optimization approach is developed to plan a schedule for the de-icing trucks. Scheduling the flow of de-icing trucks can be seen as a heterogeneous vehicle routing problem with time windows. The objective of the optimization is total airport performance and a heuristic method is used to solve the problem. The optimized schedule for the de-icing trucks is used as input in the simulation model. The schedule optimized for the entire airport is compared to a schedule based on a simpler scheduling rule as well as a schedule optimized for the de-icing company. By running the model with the different routings, it is found that the schedule optimized for the entire airport gives the best results according to the indicators specified for measuring airport performance.

The road topography in highways affects the powertrain control of a heavy truck substantially since the engine power is low in relation to the vehicle weight. In large road gradients constant speed is not possible to keep, which would have been beneficial otherwise, and in some uphills shifting gears becomes inevitable. If information about the road ahead, i.e. look ahead information, is available, then the powertrain can be controlled in a more fuel efficient way. Trial runs are performed, where the velocity trajectory that minimizes energy consumption, is calculated and communicated in real time as set points to the conventional cruise control. This look ahead control gives significant fuel consumption reductions compared to a standard cruise control, while keeping to the same mean speed. The results are the inspiration to further studies in how powertrain control can benefit from look ahead information. An engine with a non-linear fuel map is studied to understand its impact on fuel optimal speed. It is shown that for a significant fuel map non-linearity, quantified by a threshold value, constant speed in small road gradients is no longer optimal. Further, an automated manual transmission (AMT) optimal gear control is studied. It is shown that the reduced propulsion of a typical AMT gear-shifting process must be considered when choosing when to shift gears. Thus, additional reductions of fuel consumption are obtained with a look ahead control based on knowledge of engine and transmission characteristics.

This thesis considers unmanned airborne surveillance systems equipped with electroopticalvision sensors. The aim is to increase the level of autonomy and improve thesystem performance by the use of planning methods for aerial exploration and targettracking. The general problem is very complex due to the “curse-of-dimensionality” andsuboptimal approaches are necessary in order to handle advanced surveillance missions.A general planning framework is proposed and the planner contains a high-level schedulerand a number of planning modes. Each mode consists of planning modules thatsolve smaller sub-tasks and in this thesis a number of these modules are developed. Inparticular, two major approaches are treated; information based planning, and Bayesiantarget search. In addition, the on-road target tracking problem is treated in detail and analgorithm based on the Particle filter is presented. In information based planning, different information measures are used to solve theoptimal trajectory planning problem for bearings-only estimation. Thus, the problem ishow to maneuver an unmanned aerial vehicle (UAV) to achieve the best possible estimateof a target location while observing it with a vision sensor. Approaches based on the Informationfilter and the differential entropy are proposed. The Information filter approachis also used to develop an exploration framework where the UAV flight trajectory and thesensor pointing direction are considered concurrently. In Bayesian target search, the aim is to find a target as quickly as possible givensome prior knowledge of where it might be. Methods based on both gradient search andcombinatorial optimization routines are proposed for the search problem where a UAV isequipped with a controllable vision sensor with limited field-of-view.