A laser-triangulating range camera uses a laser plane to light an object. If the position of the laser relative to the camera as well as certrain properties of the camera is known, it is possible to calculate the coordinates for all points along the profile of the object. If either the object or the camera and laser has a known motion, it is possible to combine several measurements to get a three-dimensional view of the object. Camera calibration is the process of finding the properties of the camera and enough information about the setup so that the desired coordinates can be calculated. Several methods for camera calibration exist, but this thesis proposes a new method that has the advantages that the objects needed are relatively inexpensive and that only objects in the laser plane need to be observed. Each part of the method is given a thorough description. Several mathematical derivations have also been added as appendices for completeness. The proposed method is tested using both synthetic and real data. The results show that the method is suitable even when high accuracy is needed. A few suggestions are also made about how the method can be improved further.

Time of flight is an imaging technique with uses depth information to capture 3D information in a scene. Recent developments in the technology have made ToF cameras more widely available and practical to work with. The cameras now enable real time 3D imaging and positioning in a compact unit, making the technology suitable for variety of object recognition tasks An object recognition system for locating teats is at the center of the DeLaval VMS, which is a fully automated system for milking cows. By implementing ToF technology as part of the visual detection procedure, it would be possible to locate and track all four teat’s positions in real time and potentially provide an improvement compared with the current system. The developed algorithm for teat detection is able to locate teat shaped objects in scenes and extract information of their position, width and orientation. These parameters are determined with an accuracy of millimeters. The algorithm also shows promising results when tested on real cows. Although detecting many false positives the algorithm was able to correctly detected 171 out of 232 visible teats in a test set of real cow images. This result is a satisfying proof of concept and shows the potential of ToF technology in the field of automated milking.

Detta examensarbete undersöker forensisk längdmätning i bild, t ex längduppskattning av människor i bilder rörande brottsmål. Problemen identifieras och några av dagens befintliga längdmätningsmetoder diskuteras. Den metod som bäst uppfyller de i arbetet ställda kraven, d v s snabb handläggning, minimal systeminformation, minimalt arbete på plats och exakthet, har valts ut, anpassats och utvärderats. Metoden bygger på att hitta s k gränspunkter och grundplanets gränslinje i bilden och utifrån en i världen känd referenslängd beräkna den sökta längden. Den bakomliggande teorin presenteras och metoden beskrivs i detalj. Funktioner, algoritmer och ett användargränssnitt har implementerats i beräkningsprogrammet MatLab. Tester har utförts för att validera metodens noggrannhet och parameterberoende. Metoden visar sig ge mycket bra resultat då rätt förutsättningar ges, men har konstaterats vara känslig för variation på gränslinjen. En rad förbättringsförslag presenteras för att utveckla metoden och stabilisera resultatet. Examensarbetet omfattar 20 högskolepoäng och utgör ett obligatoriskt moment i utbildningsprogrammet civilingenjör i datateknik som ges av Linköpings universitet. Arbetet är utfört vid och på uppdrag av Statens kriminaltekniska laboratorium (SKL) i Linköping.

Detta examensarbete utreder avståndsbedömning med hjälp av bildbehandling och stereoseende för känd kamerauppställning. Idag existerar ett stort antal beräkningsmetoder för att få ut avstånd till objekt, men metodernas prestanda har knappt mätts. Detta arbete tittar huvudsakligen på olika blockbaserade metoder för avståndsbedömning och tittar på möjligheter samt begränsningar då man använder sig av känd kunskap inom bildbehandling och stereoseende för avståndsbedömning. Arbetet är gjort på Bofors Defence AB i Karlskoga, Sverige, i syfte att slutligen användas i ett optiskt sensorsystem. Arbetet utreder beprövade Resultaten pekar mot att det är svårt att bestämma en närmask, avstånd till samtliga synliga objekt, men de testade metoderna bör ändå kunna användas punktvis för att beräkna avstånd. Den bästa metoden bygger på att man beräknar minsta absolutfelet och enbart behåller de säkraste värdena.

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.