The estimation of a patch position in an image is a long established but still relevant topic with many applications, e.g. pose estimation and tracking in image sequences. In most systems the position estimate needs to be fused with other estimates, and hence, covariance information is required to weight the different estimates in the right way. In this paper we address the issue with covariance estimation in the case of sum of absolute difference (SAD) block matching. First, we derive the theory for covariance estimation in the case of SAD matching. Second, we evaluate the suggested method in a virtual 3D patch tracking scenario in order to verify the performance in real-world scenarios.

The GPU is the main processing unit on a graphics card. A modern GPU typically provides more than ten times the computational power of an ordinary PC processor. This is a result of the high demands for speed and image quality in computer games. This paper investigates the possibility of exploiting this computational power for tracking points in image sequences. Tracking points is used in many computer vision tasks, such as tracking moving objects, structure from motion, face tracking etc. The algorithm was successfully implemented on the GPU and a large speed up was achieved.

In order to insert a virtual object into a TV image, the graphics system needs to know precisely how the camera is moving, so that the virtual object can be rendered in the correct place in every frame. Nowadays this can be achieved relatively easily in post-production, or in a studio equipped with a special tracking system. However, for live shooting on location, or in a studio that is not specially equipped, installing such a system can be difficult or uneconomic. To overcome these limitations, the MATRIS project is developing a real-time system for measuring the movement of a camera. The system uses image analysis to track naturally occurring features in the scene, and data from an inertial sensor. No additional sensors, special markers, or camera mounts are required. This paper gives an overview of the system and presents some results.

The possibility to use real-time computer vision in video sequences gives many opportunities for a system to interact with the environment. Possible ways for interaction are e.g. augmented reality like in the MATRIS project where the purpose is to add new objects into the video sequence, or surveillance where the purpose is to find abnormal events.

The increase of the speed of computers the last years has simplified this process and it is now possible to use at least some of the more advanced computer vision algorithms that are available. The computational speed of computers is however still a problem, for an efficient real-time system efficient code and methods are necessary. This thesis deals with both problems, one part is about efficient implementations using single instruction multiple data (SIMD) instructions and one part is about robust tracking.

An efficient real-time system requires efficient implementations of the used computer vision methods. Efficient implementations requires knowledge about the CPU and the possibilities given. In this thesis, one method called SIMD is explained. SIMD is useful when the same operation is applied to multiple data which usually is the case in computer vision, the same operation is executed on each pixel.

Following the position of a feature or object in a video sequence is called tracking. Tracking can be used for a number of applications. The application in this thesis is to use tracking for pose estimation. One way to do tracking is to cut out a small region around the feature, creating a patch and find the position on this patch in the other frames. To find the position, a measure of the difference between the patch and the image in a given position is used. This thesis thoroughly investigates the sum of absolute difference (SAD) error measure. The investigation involves different ways to improve the robustness and to decrease the average error. One method to estimate the average error, the covariance of the position error is proposed. An estimate of the average error is needed when different measurements are combined.

Finally, a system for camera pose estimation is presented. The computer vision part of this system is based on the result in this thesis. This presentation contains also a discussion about the result of this system.

 In this paper we discuss the benefits of writing code for a specific processor and exploiting all its capabilities. We shows that in some situations it is possible to significantly reduce the time consumption by using SSE2, a Single Instruction Multiple Data (SIMD) extension available in new Pentium processors. Speed of the Harris operator is used for evaluation. All experiments are run on a Pentium 4 and the results are compared between ordinary C-code and code using SSE2. The purpose is not only to achieve a significant speed-up of the code, but also to benefit from SSE2 code with the least possible programming effort.