<p>Stereoscopy is a technique that can create an illusion of depth in a flat image. There are many different methods to do this, and here is explained some of the most common and popular ways, with a bigger focus on the anaglyphic method. Since stereoscopy is an old technique, the discovery of it by Charles Wheatstone is explained briefly. In Autodesk Maya 2009, a new stereoscopic plug-in was included which makes the creation of stereoscopic imagery easier. An animated project is made during the course of this research which takes advantage of and tests the functions of the new plug-in. The main purpose of the project is to create a stereoscopic movie which utilized the anaglyph stereoscopic technique. The result is rendered stereoscopic material that is edited with Adobe Premiere Pro to create anaglyphic imagery and a full color alternative using the Infitec technique.</p>

Surface characteristics such as topography and critical dimensions serve as important indicators of product quality and manufacturing process performance especially at the micrometer and the nanometer scales. This paper first reviews different technologies used for obtaining high precision 3-D images of surfaces, along with some selected applications. Atomic force microscopy (AFM) is one of such methods. These images are commonly distorted by convolution effects, which become more prominent when the sample surface contains high aspect ratio features. In addition, data artifacts can result from poor dynamic response of the instrument used. In order to achieve reliable data at high throughput, dynamic interactions between the instrument's components need to be well understood and controlled, and novel image deconvolution schemes need to be developed. Our work aims at mitigating these distortions and achieving reliable data to recover metrology soundness. A summary of our findings will be presented. / Singapore-MIT Alliance (SMA)

This paper describes a hand-eye system we developed to perform the binpicking task. Two basic tools are employed: the photometric stereo method and the extended Gaussian image. The photometric stereo method generates the surface normal distribution of a scene. The extended Gaussian image allows us to determine the attitude of the object based on the normal distribution. Visual analysis of an image consists of two stages. The first stage segments the image into regions and determines the target region. The photometric stereo system provides the surface normal distribution of the scene. The system segments the scene into isolated regions using the surface normal distribution rather than the brightness distribution. The second stage determines object attitude and position by comparing the surface normal distribution with the extended-Gaussian-image. Fingers, with LED sensor, mounted on the PUMA arm can successfully pick an object from a pile based on the information from the vision part.

Computational modules early in the human vision system typically generate sparse information about the shapes of visible surfaces in the scene. Moreover, visual processes such as stereopsis can provide such information at a number of levels spanning a range of resolutions. In this paper, we extend this multi-level structure to encompass the subsequent task of reconstructing full surface descriptions from the sparse information. The mathematical development proceeds in three steps. First, the surface most consistent with the sparse constraints is characterized as the solution to an equilibrium state of a thin flexible plate. Second, local, finite element representations of surfaces are introduced and, by applying the finite element method, the continuous variational principle is transformed into a discrete problem in the form of a large system of linear algebraic equations whose solution is computable by local-support, cooperative mechanisms. Third, to exploit the information available at each level of resolution, a hierarchy of discrete problems is formulated and a highly efficient multi-level algorithm, involving both intra-level relaxation processes and bi-directional inter-level algorithm, involving both intra-level relaxation processes and bidirectional inter-level local interpolation processes is applied to their simultaneous solution.. Examples of the generation of hierarchies of surface representations from stereo constraints are given. Finally, the basic surface approximation problem is revisited in a broader mathematical context whose implications are of relevance to vision.

Three-dimensional models which contain both geometry and texture have numerous applications such as urban planning, physical simulation, and virtual environments. A major focus of computer vision (and recently graphics) research is the automatic recovery of three-dimensional models from two-dimensional images. After many years of research this goal is yet to be achieved. Most practical modeling systems require substantial human input and unlike automatic systems are not scalable. This thesis presents a novel method for automatically recovering dense surface patches using large sets (1000's) of calibrated images taken from arbitrary positions within the scene. Physical instruments, such as Global Positioning System (GPS), inertial sensors, and inclinometers, are used to estimate the position and orientation of each image. Essentially, the problem is to find corresponding points in each of the images. Once a correspondence has been established, calculating its three-dimensional position is simply a matter of geometry. Long baseline images improve the accuracy. Short baseline images and the large number of images greatly simplifies the correspondence problem. The initial stage of the algorithm is completely local and scales linearly with the number of images. Subsequent stages are global in nature, exploit geometric constraints, and scale quadratically with the complexity of the underlying scene. We describe techniques for: 1) detecting and localizing surface patches; 2) refining camera calibration estimates and rejecting false positive surfels; and 3) grouping surface patches into surfaces and growing the surface along a two-dimensional manifold. We also discuss a method for producing high quality, textured three-dimensional models from these surfaces. Some of the most important characteristics of this approach are that it: 1) uses and refines noisy calibration estimates; 2) compensates for large variations in illumination; 3) tolerates significant soft occlusion (e.g. tree branches); and 4) associates, at a fundamental level, an estimated normal (i.e. no frontal-planar assumption) and texture with each surface patch.

In this text, we present two stereo-based head tracking techniques along with a fast 3D model acquisition system. The first tracking technique is a robust implementation of stereo-based head tracking designed for interactive environments with uncontrolled lighting. We integrate fast face detection and drift reduction algorithms with a gradient-based stereo rigid motion tracking technique. Our system can automatically segment and track a user's head under large rotation and illumination variations. Precision and usability of this approach are compared with previous tracking methods for cursor control and target selection in both desktop and interactive room environments. The second tracking technique is designed to improve the robustness of head pose tracking for fast movements. Our iterative hybrid tracker combines constraints from the ICP (Iterative Closest Point) algorithm and normal flow constraint. This new technique is more precise for small movements and noisy depth than ICP alone, and more robust for large movements than the normal flow constraint alone. We present experiments which test the accuracy of our approach on sequences of real and synthetic stereo images. The 3D model acquisition system we present quickly aligns intensity and depth images, and reconstructs a textured 3D mesh. 3D views are registered with shape alignment based on our iterative hybrid tracker. We reconstruct the 3D model using a new Cubic Ray Projection merging algorithm which takes advantage of a novel data structure: the linked voxel space. We present experiments to test the accuracy of our approach on 3D face modelling using real-time stereo images.

This thesis discusses how a historic woman is represented on stage today. The woman in mind is Sabina Spielrein, and she is portrayed in a play called: Name: Spielrein Sabina. She was born in the late 19th century and made a significant impact on the development in psycho-analysis. Since her letters, diaries and fragments was found 1977, and later on in 1982, nu-merous of people have been fascinated regarding her life, especially with her relation to Jung and Freud. One of my arguments is that there could be another story to tell, as she is living in a context when women were organized and struggled for their rights on many levels. It is a period when a new type of women is formulated, with new visions on society, morals and sexuality. This New Woman discourse was intense all over Europe. With representation as a theoretical instrument it was possible to see how this play challenges several typical women images. By using the character Sabina I have looked at (stereo) types such as: the good daughter, the mad woman, the genius, the mistress, the wife and the mother. Putting Sabina Spielrein in focus has also made it possible to discuss around cultural power, how it works and the importance of who is in charge.

3D representation nowadays has attracted much more public attention than ever before. One of the most important techniques in this field is depth extraction. In this thesis, we first introduce a well-known stereo matching method using color segmentation and belief propagation, and make an implementation of this framework. The color-segmentation based stereo matching method performs well recently, since this method can keep the object boundaries accurate, which is very important to depth map. Based on the implemented framework of segmentation based stereo matching, we proposed a color segmentation based 2D-to-3D video conversion method using high quality motion information. In our proposed scheme, the original depth map is generated from motion parallax by optical flow calculation. After that we employ color segmentation and plane estimation to optimize the original depth map to get an improved depth map with sharp object boundaries. We also make some adjustments for optical flow calculation to improve its efficiency and accuracy. By using the motion vectors extracted from compressed video as initial values for optical flow calculation, the calculated motion vectors are more accurate within a shorter time compared with the same process without initial values. The experimental results shows that our proposed method indeed gives much more accurate depth maps with high quality edge information. Optical flow with initial values provides good original depth map, and color segmentation with plane estimation further improves the depth map by sharpening its boundaries.

Acquisition of 3D model of real objects and scenes is indispensable and useful in many practical applications, such as digital archives, game and entertainment industries, engineering, advertisement. There are 2 main methods for 3D acquisition : laser-based reconstruction (active method) and image-based reconstruction from multiple images of the scene in different points of view (passive method). While laser-based reconstruction achieves high accuracy, it is complex, expensive and difficult to set up for large-scale outdoor reconstruction. Image-based, or multi-view stereo methods are more versatile, easier, faster and cheaper. By the time we begin this thesis, most multi-view methods could handle only low resolution images under controlled environment. This thesis targets multi-view stereo both both in large scale and high accuracy issues. We significantly improve some previous methods and combine them into a remarkably effective multi-view pipeline with GPU acceleration. From high-resolution images, we produce highly complete and accurate meshes that achieve best scores in many international recognized benchmarks. Aiming even larger scale, on one hand, we develop Divide and Conquer approaches in order to reconstruct many small parts of a big scene. On the other hand, to combine separate partial results, we create a new merging method, which can merge automatically and quickly hundreds of meshes. With all these components, we are successful to reconstruct highly accurate water-tight meshes for cities and historical monuments from large collections of high-resolution images (around 1600 images of 5 M Pixel images)

In this master thesis a visual odometry system is implemented and explained. Visual odometry is a technique, which could be used on autonomous vehicles to determine its current position and is preferably used indoors when GPS is notworking. The only input to the system are the images from a stereo camera and the output is the current location given in relative position. In the C++ implementation, image features are found and matched between the stereo images and the previous stereo pair, which gives a range of 150-250 verified feature matchings. The image coordinates are triangulated into a 3D-point cloud. The distance between two subsequent point clouds is minimized with respect to rigid transformations, which gives the motion described with six parameters, three for the translation and three for the rotation. Noise in the image coordinates gives reconstruction errors which makes the motion estimation very sensitive. The results from six experiments show that the weakness of the system is the ability to distinguish rotations from translations. However, if the system has additional knowledge of how it is moving, the minimization can be done with only three parameters and the system can estimate its position with less than 5 % error.