This Masters thesis studies the concept of local binary patterns, which describe the neighbourhood of a pixel in a digital image by binary derivatives. The operator is often used in texture analysis and has been successfully used in facial recognition. This thesis suggests two methods based on some basic ideas of Björn Kruse and studies of literature on the subject. The first suggested method presented is an algorithm which reproduces images from their local binary patterns by a kind of integration of the binary derivatives. This method is a way to prove the preservation of information. The second suggested method is a technique of interpolating missing pixels in a single CCD camera based on local binary patterns and machine learning. The algorithm has shown some very promising results even though in its current form it does not keep up with the best algorithms of today.

The objective of this thesis is to investigate if it is possible to use stereo vision to find and track the players and the ball during a football game. The thesis shows that it is possible to detect all players that isn’t too occluded by another player. Situations when a player is occluded by another player is solved by tracking the players from frame to frame. The ball is also detected in most frames by looking for ball-like features. As with the players the ball is tracked from frame to frame so that when the ball is occluded, the positions is estimated by the tracker.

A common problem when using background models to segment moving objects from video sequences is that objects cast shadow usually significantly differ from the background and therefore get detected as foreground. This causes several problems when extracting and labeling objects, such as object shape distortion and several objects merging together. The purpose of this thesis is to explore various possibilities to handle this problem. Three methods for statistical background modeling are reviewed. All methods work on a per pixel basis, the first is based on approximating the median, the next on using Gaussian mixture models, and the last one is based on channel representation. It is concluded that all methods detect cast shadows as foreground. A study of existing methods to handle cast shadows has been carried out in order to gain knowledge on the subject and get ideas. A common approach is to transform the RGB-color representation into a representation that separates color into intensity and chromatic components in order to determine whether or not newly sampled pixel-values are related to the background. The color spaces HSV, IHSL, CIELAB, YCbCr, and a color model proposed in the literature (Horprasert et al.) are discussed and compared for the purpose of shadow detection. It is concluded that Horprasert's color model is the most suitable for this purpose. The thesis ends with a proposal of a method to combine background modeling using Gaussian mixture models with shadow detection using Horprasert's color model. It is concluded that, while not perfect, such a combination can be very helpful in segmenting objects and detecting their cast shadow.

A face tracker is exactly what it sounds like. It tracks a face in a video sequence. Depending on the complexity of the tracker, it could track the face as a rigid object or as a complete deformable face model with face expressions. This report is based on the work of a real time feature based face tracker. Feature based means that you track certain features in the face, like points with special characteristics. It might be a mouth or eye corner, but theoretically it could be any point. For this tracker, the latter is of interest. Its task is to extract global parameters, i.e. rotation and translation, as well as dynamic facial parameters (expressions) for each frame. It tracks feature points using motion between frames and a textured face model (Candide). It then uses an extended Kalman filter to estimate the parameters from the tracked feature points.

As real time visualizations become more realistic it also becomes more important to simulate the perceptual effects of the human visual system. Such effects include the response to varying illumination, glare and differences between photopic and scotopic vision. This thesis evaluates several different tone mapping methods to allow a greater dynamic range to be used in real time visualisations. Several tone mapping methods have been implemented in the Avalanche Game Engine and evaluated using a small test group. To increase immersion in the visualization several filters aimed to simulate perceptual effects has also been implemented. The primary goal of these filters is to simulate scotopic vision. The tests showed that two tone mapping methods would be suitable for the environment used in the tests. The S-curve tone mapping method gave the best result while the Mean Value method gave good results while being the simplest to implement and the cheapest. The test subjects agreed that the simulation of scotopic vision enhanced the immersion in a visualization. The primary difficulties in this work has been lack of dynamic range in the input images and the challenges in coding real time graphics using a graphics processing unit.

This thesis aims at building a system capable of automatically extracting and parameterizing the position of a face and its features in images acquired from a low-end monocular camera. Such a challenging task is justified by the importance and variety of its possible applications, ranging from face and expression recognition to animation of virtual characters using video depicting real actors. The implementation includes the construction of Active Appearance Models of the human face from training images. The existing face model Candide-3 is used as a starting point, making the translation of the tracking parameters to standard MPEG-4 Facial Animation Parameters easy. The Inverse Compositional Algorithm is employed to adapt the models to new images, working on a subspace where the appearance is "projected out" and thus focusing only on shape. The algorithm is tested on a generic model, aiming at tracking different people’s faces, and on a specific model, considering one person only. In the former case, the need for improvements in the robustness of the system is highlighted. By contrast, the latter case gives good results regarding both quality and speed, with real time performance being a feasible goal for future developments.

This Master’s Thesis was part of the project called Multimodalanalysis at the Depart-ment of Biomedical Engineering and Informatics at the Ume˚ University Hospital inUme˚ Sweden. The aim of the project is to develop multivariate measurement anda,analysis methods of the skeletal muscle physiology. One of the methods used to scanthe muscle is functional ultrasound. In a study performed by the project group datawas aquired, where test subjects were instructed to follow a certain exercise scheme,which was measured. Since there currently is no superior method to analyze the result-ing data (in form of ultrasound video sequences) several methods are being looked at.One considered method is called Independent Vector Analysis (IVA). IVA is a statisticalmethod to find independent components in a mix of components. This Master’s Thesisis about segmenting and analyzing the ultrasound images with help of IVA, to validateif it is a suitable method for this kind of tasks.First the algorithm was tested on generated mixed data to find out how well itperformed. The results were very accurate, considering that the method only usesapproximations. Some expected variation from the true value occured though.When the algorithm was considered performing to satisfactory, it was tested on thedata gathered by the study and the result can very well reflect an approximation of truesolution, since the resulting segmented signals seem to move in a possible way. But themethod has weak sides (which have been tried to be minimized) and all error analysishas been done by human eye, which definitly is a week point. But for the time being itis more important to analyze trends in the signals, rather than analyze exact numbers.So as long as the signals behave in a realistic way the result can not be said to becompletley wrong. So the overall results of the method were deemed adequate for the application at hand. / Multimodalanalys

This master thesis investigates the difficulties of constructing a depth map using one low resolution grayscale camera mounted in the front of a car. The goal is to produce a depth map in real-time to assist other algorithms in the safety system of a car. This has been shown to be difficult using the evaluated combination of camera position and choice of algorithms. The main problem is to estimate an accurate optical flow. Another problem is to handle moving objects. The conclusion is that the implementations, mainly triangulation of corresponding points tracked using a Lucas Kanade tracker, provide information of too poor quality to be useful for the safety system of a car. / I detta examensarbete undersöks svårigheterna kring att skapa en djupbild från att endast använda en lågupplöst gråskalekamera monterad framtill i en bil. Målet är att producera en djupbild i realtid som kan nyttjas i andra delar av bilens säkerhetssystem. Detta har visat sig vara svårt att lösa med den undersökta kombinationen av kameraplacering och val av algoritmer. Det huvudsakliga problemet är att räkna ut ett noggrant optiskt flöde. Andra problem härrör från objekt som rör på sig. Slutsatsen är att implementationerna, mestadels triangulering av korresponderande punktpar som följts med hjälp av en Lucas Kanade-följare, ger resultat av för dålig kvalitet för att vara till nytta för bilens säkerhetssystem.

Medical imaging is an important tool for diagnosis and treatment planning today. However as the demand for efficiency increases at the same time as the data volumes grow immensely, the need for computer assisted analysis, such as image segmentation, to help and guide the practitioner increases. Medical image segmentation could be used for various different tasks, the localization and delineation of pathologies such as cancer tumors is just one example. Numerous problems with noise and image artifacts in the generated images make the segmentation a difficult task, and the developer is forced to choose between speed and performance. In clinical practise, however, this is impossible as both speed and performance are crucial. One solution to this problem might be to involve the user more in the segmentation, using interactivite algorithms where the user might influence the segmentation for an improved result. This thesis has concentrated on finding a fast and interactive segmentation method for liver tumor segmentation. Various different methods were explored, and a few were chosen for implementation and further development. Two methods appeared to be the most promising, Bayesian Region Growing (BRG) and Level Set. An interactive Level Set algorithm emerged as the best alternative for the interactivity of the algorithm, and could be used in combination with both BRG and Level Set. A new data term based on a probability model instead of image edges was also explored for the Level Set-method, and proved to be more promising than the original one. The probability based Level Set and the BRG method both provided good quality results, but the fastest of the two was the BRG-method, which could segment a tumor present in 25 CT image slices in less than 10 seconds when implemented in Matlab and mex-C++ code on an ACPI x64-based PC with two 2.4 GHz Intel(R) Core(TM) 2CPU and 8 GB RAM memory. The interactive Level Set could be succesfully used as an interactive addition to the automatic method, but its usefulness was somewhat reduced by its slow processing time ( 1.5 s/slice) and the relative complexity of the needed user interactions.

In this thesis, an investigation was performed to find ways of differencing between firesand vehicles at waste stations in hope of removing vehicles as a source of error duringearly fire detection. The existing system makes use of a heat camera, which rotates in 48different angles (also known as zones) in a fixed position. If the heat is above a certainvalue within a zone the system sounds the fire alarm.The rotation of the camera results in an unwanted displacement between two successiveframes within the same zone. By use of image registration, this displacement wasremoved. After the registration of an image, segmentation was performed where coldobjects are eliminated as an error source. Lastly, an analysis was performed upon thewarm objects.At the end, it was proven that the image registration had been a successful improvementof the existing system. It was also shown that vehicles can, to some extent, beeliminated as an error source. / I denna uppsats görs en undersökning av sätt att urskilja mellan bränder och fordon vid avfallsbunkrar, i hopp om att ta bortfordon som felkälla under tidig branddetektion. Dagens system använder sig av en värmekamera som roterar i 48 vinklar(även kallade zoner) från en fix position och larmar då det blir för varmt i någon zon.Roteringen av kameran medför en icke önskvärd förskjutning mellan två efterföljande bilder inom samma zon. Processenbildregistrering används för att eliminera denna förskjutning. Efter registreringen utförs en segmentering där kalla objekt tasbort som felkälla. När detta är utfört görs en analys av de varma objekten med en mängd mätningar.I slutet bevisas att registreringen har fungerat mycket väl, likaså att det går till viss del att eliminera fordon som felkällaunder tidig brandetektion.