In this paper we show how the theory of dynamical systems can be employed to solve problems in motion vision. In particular we develop algorithms for the recovery of dense depth maps and motion parameters using state space observers or filters. Four different dynamical models of the imaging situation are investigated and corresponding filters/ observers derived. The most powerful of these algorithms recovers depth and motion of general nature using a brightness change constraint assumption. No feature-matching preprocessor is required.

In the absence of cues for absolute depth measurements as binocular disparity, motion, or defocus, the absolute distance between the observer and a scene cannot be measured. The interpretation of shading, edges and junctions may provide a 3D model of the scene but it will not inform about the actual "size" of the space. One possible source of information for absolute depth estimation is the image size of known objects. However, this is computationally complex due to the difficulty of the object recognition process. Here we propose a source of information for absolute depth estimation that does not rely on specific objects: we introduce a procedure for absolute depth estimation based on the recognition of the whole scene. The shape of the space of the scene and the structures present in the scene are strongly related to the scale of observation. We demonstrate that, by recognizing the properties of the structures present in the image, we can infer the scale of the scene, and therefore its absolute mean depth. We illustrate the interest in computing the mean depth of the scene with application to scene recognition and object detection.

The central line-depths of absorption lines depend upon stellar temperature. By dividing the central line-depth of such a line with a central line-depth independent of temperature, a thermometer of relative star temperatures is obtained in the form of a line-depth ratio (LDR), once it is related to an effective temperature scale. Such thermometers are known to give precise results which is why the method is pursued. The purpose of this work is to rank LDRs according to a set of criteria to find the most suitable ratio to measure temperature. This is done based on a set of LDRs measured for a large sample of dwarf stars with known effective temperature, atmospheric pressure and chemical composition. Numerous LDRs are eliminated because their temperature dependence are limited to a short temperature interval. Further LDRs are eliminated due to dependence on the atmospheric pressure and chemical composition of the LDR. The remaining LDRs are ranked based on the strength of temperature dependence, the fit of the representative polynomial to the data points and the number of data points available. The best ranked LDR provides a temperature resolution smaller than 10 K over a temperature interval of 4500-6250 K, assuming an uncertainty in LDR of 0.01.

Digital watermarking of multimedia content has been proposed as a method for different applications such as copyright protection, content authentication, transaction tracking and data hiding. In this thesis, we propose a lossless watermarking approach based on Discrete Cosine Transform (DCT) for a new application of watermarking. A depth map obtained from a stereoscopic image pair is embedded into one of the two images using a reversible watermarking algorithm. Different from existing approaches which hide depth map in spatial domain, the depth information is hidden in the quantized DCT domain of the stereo image in our method. This modification makes the watermarking algorithm compatible with JPEG and MPEG-2 compression. After the investigation of the quantized DCT coefficients distribution of the compressed image and video, The bit-shift operation is utilized to embed the depth map into its associated 2D image reversibly for the purpose of achieving high compression efficiency of the watermarked image and/or video and high visual quality of stereo image and/or video after the depth map is extracted. We implement the proposed method to analyze its performance. The experimental results show that a very high payload of watermark (e.g. depth map) can be embedded into the JPEG compressed image and MPEG-2 video. The compression efficiency is only slightly reduced after the watermark embedding and the quality of the original image or video can be restored completely at the decoder side.

Due to the fast development in 3D technology during recent decades, many approaches in 3D representation technologies have been proposed worldwide. In order to get an accurate information to render a 3D representation, more data need to be recorded compared to normal video sequence. In this case, how to find an efficient way to transmit the 3D representation data becomes an important part in the whole 3D representation technology. Recent years, many coding schemes based on the principle of encoding the depth have been proposed. Compared to the traditional multiview coding schemes, those new proposed schemes can achieve higher compression efficiency. Due to the development of depth capturing technology, the accuracy and quality of the reconstructed depth image also get improved. In this thesis we propose an efficient depth data compression scheme for 3D images. Our proposed depth data compression scheme is platelet based coding using Lagrangian optimization, quadtree decomposition and quadratic curve fitting. We study and improve the original platelet based coding scheme and achieve a compression improvement of 1-2 dB compared to the original platelet based scheme. The experimental results illustrate the improvement provided by our scheme. The quality of the reconstructed results of our proposed curve fitting based platelet coding scheme are better than that of the original scheme.

This project aims to determine if and how the relatively new journalistic tool of Twitter is impacting journalistic decision-making and news production as a legitimate tool amongst sports writers. Using the methods of qualitative textual analysis and in-depth interviewing, this project analyzes the words and tweets of nine journalists at prominent U.S. newspapers in an attempt to fill a void in research among the topics of journalistic decision-making, sports journalism, and Twitter and to answer questions that arise from the marriage of a certain type of journalism and a particular new media platform.

Previous studies (e.g. Benston and Hagerman, 1974, Bagehot, 1971 and Stoll, 1978) suggest that the bid-ask spread consists of three components: asymmetric information cost, inventory holding cost, and order processing cost. Other literature (e.g. Brock and Kleidon, 1992, Hef-lin et al, 2007, and McInish and Van Ness, 2002) reports that the bid-ask spread varies during a trading day following a U-shaped pattern. One explanation for this observation is that it is the result of changes in information asymmetry costs over the trading hours, assuming the other costs are fixed. However, no empirical study directly measures how information asym-metry changes over the trading day. We explore how this measure relates to the spread as well as the quote depth.<p> Our research divides a trading day into 13 half-hour trading intervals and measures in-formation asymmetry during each interval following the model developed by Madhavan and Smidt (1991) and Noronha et al (1996). Their model can directly estimate the level of infor-mation asymmetry in each interval. This enables us to observe the intraday pattern of infor-mation asymmetry directly and compare it to the patterns of the spread and the quote depth. Furthermore, we test the relationship between the spread and the information asymmetry and the relationship between the depth and the information asymmetry in a dynamic context to see how market makers manage information risk over trading hours.<p> We find that the risk of information asymmetry varies significantly during the trading day. There is a large drop over the first interval, and another large drop over the last interval, with smaller fluctuations over the remaining intervals. Moreover, we show that the spread is consistent with an L-shaped pattern as opposed to the U-shaped pattern proposed by previous studies while the depth is increasing throughout the 13 trading intervals. Furthermore, we ob-serve that the variations of the spread and the depth are respectively positively and negatively related to the intraday variations in the degree of information asymmetry across the trading intervals. In particular, a large decline in information asymmetry at the beginning of the day is associated with a large reduction in the spread, whereas a large decline in information asymmetry at the end of the day is associated with a large increase in the quote depth. This emphasises the importance of studying both measures of liquidity simultaneously.

Digital watermarking of multimedia content has been proposed as a method for different applications such as copyright protection, content authentication, transaction tracking and data hiding. In this thesis, we propose a lossless watermarking approach based on Discrete Cosine Transform (DCT) for a new application of watermarking. A depth map obtained from a stereoscopic image pair is embedded into one of the two images using a reversible watermarking algorithm. Different from existing approaches which hide depth map in spatial domain, the depth information is hidden in the quantized DCT domain of the stereo image in our method. This modification makes the watermarking algorithm compatible with JPEG and MPEG-2 compression. After the investigation of the quantized DCT coefficients distribution of the compressed image and video, The bit-shift operation is utilized to embed the depth map into its associated 2D image reversibly for the purpose of achieving high compression efficiency of the watermarked image and/or video and high visual quality of stereo image and/or video after the depth map is extracted. We implement the proposed method to analyze its performance. The experimental results show that a very high payload of watermark (e.g. depth map) can be embedded into the JPEG compressed image and MPEG-2 video. The compression efficiency is only slightly reduced after the watermark embedding and the quality of the original image or video can be restored completely at the decoder side.

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When an autonomous vehicle has a mission to perform, it is of high importance that the robot has good knowledge about its position. Without good knowledge of the position, it will not be able to navigate properly and the data that it gathers, which could be of importance for the mission, might not be usable. A helicopter could for example be used to collect laser data from the terrain beneath it, which could be used to produce a 3D map of the terrain. If the knowledge about the position and orientation of the helicopter is poor then the collected laser data will be useless since it is unknown what the laser actually measures. A successful solution to position and orientation (pose) estimation of an autonomous helicopter, using an inertial measurement unit (IMU), a camera and a GPS, is proposed in this thesis. The problem is to estimate the unknown pose using sensors that measure different physical attributes and give readings containing noise. An extended Kalman filter solution to the simultaneous localisation and mapping (SLAM) is used to fuse data from the different sensors and estimate the pose of the robot. The scale invariant feature transform (SIFT) is used for feature extraction and the unified inverse depth parametrisation (UIDP) model is used to parametrise the landmarks. The orientation of the robot is described by quaternions. To be able to evaluate the performance of the filter an ABB industrial robot has been used as reference. The pose of the end tool of the robot is known with high accuracy and gives a good ground truth so that the estimations can be evaluated. The results shows that the algorithm performs well and that the pose is estimated with good accuracy. / När en autonom farkost skall utföra ett uppdrag är det av högsta vikt att den har god kännedom av sin position. Utan detta kommer den inte att kunna navigera och den data som den samlar in, relevant för uppdraget, kan vara oanvändbar. Till exempel skulle en helikopter kunna användas för att samla in laser data av terrängen under den, för att skapa en 3D karta av terrängen. Om kännedomen av helikopterns position och orientering är dålig kommer de insamlade lasermätningarna att vara oanvändbara eftersom det inte är känt vad lasern faktiskt mäter. I detta examensarbete presenteras en väl fungerande lösning för position och orienterings estimering av autonom helikopter med hjälp av en inertial measurement unit (IMU), en kamera och GPS. Problemet är att skatta positionen och orienteringen med hjälp av sensorer som mäter olika fysiska storheter och vars mätningar innehåller brus. En extended Kalman filter (EKF) lösning för simultaneous localisation and mapping (SLAM) problemet används för att fusionera data från de olika sensorerna och estimera positionen och orienteringen. För feature extrahering används scale invariant feature transform (SIFT) och för att parametrisera landmärken används unified inverse depth parametrisation (UIDP). Orienteringen av roboten beskrivs med hjälp av qvartinjoner. För att evaluera skattningarna har en ABB robot används som referens vid datainsamling. Då roboten har god kännedom om position och orientering av sitt främre verktyg gör detta att prestandan i filtret kan undersökas. Resultaten visar att algorithmen fungerar bra och att skattningar har hög noggrannhet.