This thesis aims at introducing a methodology for clinical evaluation of orthodontic treatments using three-dimensional dento-maxillofacial images. Since complementary information is achieved by integrating multiple modalities, cone-beam computed tomography (CBCT) and stereophotogrammetry technologies are used to develop a methodology for tracking bone and facial skin variations over time. Our proposed methodology consists of a two-phase registration procedure. In the first phase, the multimodal images are registered using an extrinsic landmark-based registration followed by a robust Iterative Closest Points (ICP) method. In the second phase, by utilizing specific anatomical landmarks, single modal images of the skull and the mandible are registered over time using an intrinsic landmark-based registration method followed by the robust ICP algorithm. The results of registrations show that the signed error distribution of both mandible and skull registrations follow a normal distribution while all the errors fall within the CBCT precision range.

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This thesis addresses the issue of ground collision in poor weather conditions. As bad weather is an adverse factor when airplanes are taxiing, an obstacle detection system based on thermal vision is proposed to enhance the awareness of pilots during taxiing in poor weather conditions. Two infrared cameras are employed to detect the objects and estimate the distance of the obstacle. The distance is computed by stereo vision technology. A warning will be given if the distance is less than the safe distance predefined. To make the system independent, the proposed system is an on-board system which does not rely on airports or other airplanes. The type of obstacle is classified by the temperature of the object. Fuzzy logic is employed in the classification. Obstacles are classified into three main categories: aircraft, vehicle and people. Membership functions are built based on the temperature distribution of obstacles measured at the airport. In order to improve the accuracy of classification, a concept of using position information is proposed. Different types of obstacle are predefined according to different area at the airport. In the classification, obstacles are classified according to the types limited in that area. Due to the limitation of the thermal infrared camera borrowed, images were captured first and then processed offline. Experiments were carried out to evaluate the detecting distance error and the performance of system in poor weather conditions. The classification of obstacle is simulated with real thermal images and pseudo position information at the airport. The results suggest that the stereo vision system developed in this research was able to detect the obstacle and estimate the distance. The classification method classified the obstacles to a certain extent. Therefore, the proposed system can improve safety of aircraft and enhance situational awareness of pilots. The programming language of the system is Python 2.7. Computer graphic library OpenCV 2.3 is used in processing images. MATLAB is used in the simulation of obstacle classification.

This report contains a description on how it is possible to guide a robot along an edge, by using a camera mounted on the robot. If stereo matching is used to calculate 3Dcoordinates of an object or an edge, it requires two images from different known positions and orientations to calculate where it is. In the image analysis in this project, the Canny edge filter has been used. The result from the filter is not useful directly, because it finds too many edges and it misses some pixels. The Canny edge result must be sorted and finally filled up before the final calculations can be started. This additional work with the image decreases unfortunately the accuracy in the calculations. The accuracy is estimated through comparison between measured coordinates of the edge using a coordinate measuring machine and the calculated coordinates. There is a deviation of up to three mm in the calculated edge. The camera calibration has been described in earlier thesis so it is not mentioned in this report, although it is a prerequisite of this project.

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3D animation has developed rapidly in the multimedia nowadays, in computer games, virtual reality and films. Therefore, how to make a 3D model which is really true to life, especially in the facial expressions, and can have vivid actions, is a significant issue. At the present time, the methods to construct 3D facial model are divided into two categories: one is based on computer graphic technology, like geometric function, polygon, or simple geometric shapes, the other one is using hardware to measure a real face by laser scanning system, and three-dimensional digitizer. Moreover, the method to acquire the 3D facial expression primarily are applied as following: keyframing, motion capture, and simulation. The research covers two areas: 1. Use two CCDs to digitalize the facial expressions of a real person simultaneously from both right and left side, and save the obtained standard image. Then, get the feature match points from the two standard images in the space domain, and by using the Stereo to attain the ¡§depth information¡¨ which helps to build 3D facial model. 2. Use one CCD to continuously digitalize two facial expressions and get the feature match points¡¦ coordinates in the time domain to calculate the motion vector. By combining the ¡§depth information¡¨ from space domain and the motion vector from the time domain, the 3D facial model¡¦s motion sequence can be therefore obtained. If sufficient digitalized facial expressions are processed by the 3D facial model¡¦s motion sequence, a database could be built. By matching the feature points between the 2D test image and 2D standard image in the database, the standard image¡¦s ¡§depth information¡¨ and motion vector can be used and turn the test image into 3D model which can also imitate the facial expressions of the standard images sequences. The method to match the feature points between the test image and standard images in the database can be entirely processed by computers, and as a result eliminate unnecessary human resources.

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<p>In this thesis, two real-time stereo methods have been implemented and evaluated. The first one is based on blockmatching and the second one is based on local phase. The goal was to be able to run the algorithms at real-time and examine which one is best. The blockmatching method performed better than the phase based method, both in speed and accuracy. SIMD operations (Single Instruction Multiple Data) have been used in the processor giving a speed boost by a factor of two.</p> / <p>I det här exjobbet har två stereometoder för realtidstillämpningar implementerats och utvärderats. Den ena bygger på blockmatchning och den andra på lokal fas. Målet var att kunna köra metoderna i realtid och undersöka vilken av dem som fungerar bäst. Blockmatchningsmetoden gav gott resultat medan den fasbaserade fungerade sämre, både vad gäller hastighet och precision. SIMD-operationer (Single Instruction Multiple Data) användes hos processorn vilket resulterade en i fördubbling av prestandan.</p>