3D face modeling is currently a popular area in Computer Graphics and Computer Vision. Many techniques have been introduced for this purpose, such as using one or more cameras, 3D scanners, and many other systems of sophisticated hardware with related software. But the main goal is to find a good balance between visual reality and the cost of the system. In this thesis, reconstruction of a 3D human face from a pair of stereo cameras is studied. Unlike many other systems, facial feature points are obtained automatically from two photographs with the help of a dot pattern projected on the object&amp / #8217 / s face. It is seen that using projection pattern also provided enough feature points to derive 3D face roughly. These points are then used to fit a generic face mesh for a more realistic model. To cover this 3D model, a single texture image is generated from the initial stereo photographs.

With the advances in computer technology and the changing needs of people&rsquo / s daily lives, robots start to offer alternative solutions. As one of these solutions, the branch of humanoid robots emerged as advanced robots that can interact with people. Robot faces are one of the most effective means of interacting with people / since they can express their emotions and reactions through facial mimics. However, the development of realistic robot faces necessitates the knowledge of the trajectories and displacements of actual face mimics. In this study, a setup (both hardware and software), that can be used for tracking critical points on human face while exhibiting mimics, is developed. From the outputs of this setup, the mimic trajectories are going to be extracted. The setup is designed and manufactured to be durable to external effects so that with a single camera calibration procedure the 3D reconstruction can be carried out several times. The setup consists of two webcams that are specially oriented for mimic tracking. The images taken from the cameras are corrected / their features are extracted using image processing algorithms / the centroids of the features are found / correspondence is carried out and the reconstruction is made. This system can also be used for any special point tracking or volumetric measurement purposes.

Nowadays, 3D modelling of objects from multiple images is a topic that has gained great recognition and is widely used in various fields. Recently, lots of progress has been made in identification of people using 3D face models, which are usually reconstructed from multiple face images. In this thesis, a system including stereo cameras and structured light is built for the purpose of 3D modelling. The system outputs are 3D shapes of the face and also the texture information registered to this shape. Although the system in this thesis is developed for face reconstruction, it is not specific to faces. Using the same methodology proposed in this study 3D reconstruction of any object can be achieved.

Fusion of multiple stereo algorithms is performed in order to obtain accurate disparity segmentation. Reliable disparity map of real-time stereo images is estimated and disparity segmentation is performed for object detection purpose. First, stereo algorithms which have high performance in real-time applications are chosen among the algorithms in the literature and three of them are implemented. Then, the results of these algorithms are fused to gain better performance in disparity estimation. In fusion process, if a pixel has the same disparity value in all algorithms, that disparity value is assigned to the pixel. Other pixels are labelled as unknown disparity. Then, unknown disparity values are estimated by a refinement procedure where neighbourhood disparity information is used. Finally, the resultant disparity map is segmented by using mean shift segmentation. The proposed method is tested in three different stereo data sets and several real stereo pairs. The experimental results indicate an improvement for the stereo analysis performance by the usage of fusion process and refinement procedure. Furthermore, disparity segmentation is realized successfully by using mean shift segmentation for detecting objects at different depth levels.

The present graph cut methods cannot be used directly for real time stereo matching applications because of their recursive structure. Graph cut method is modified to change its recursive structure so that making it suitable for real time FPGA (Field Programmable Gate Array) implementation. The modified method is firstly tested by MATLAB on several data sets, and the results are compared with those of previous studies. Although the disparity results of the modified method are not better than other methods&rsquo / , computation time performance is better. Secondly, the FPGA simulation is performed using real data sets. Finally, the modified method is implemented in FPGA with two PAL cameras at 25 Hz. The computation time of the implementation is 40 ms which is suitable for real time applications.

Three dimensional (3D) shape reconstruction that aims to reconstruct 3D surface of objects using acquired images, is one of the main problems in computer vision. There are many applications of 3D shape reconstruction, from satellite imaging to material sciences, considering a continent on earth or microscopic surface properties of a material. One of these applications is the automated firearm identification that is an old, yet an unsolved problem in forensic science. Firearm evidence matching algorithms rely on the fact that a firearm creates characteristic marks on surfaces of the bullets and the cartridge cases. These marks should be digitized unaffected from different surface material properties of evidences. Accuracy of 3D shape is one of the most important parameters affecting the overall identification performance. A very high resolution, accurate 3D data have to be reconstructed in the order of minutes. Photometric stereo (PS) method is capable of reconstructing high resolution surfaces in a fast manner. But, the metallic material and the surface topology of the firearm evidences generate highlights and shadows on their images that does not comply with the assumptions of conventional PS. In the scope of this work, it is intended to design an accurate, fast and robust 3D shape reconstruction scheme using PS considering highlights and shadows. These new PS procedures to be developed here should not be limited only to the ballistic evidences but they also could be used for a wider range of objects reflection properties and texture. For this purpose, masked PS methods which are quite fast when compared to other approaches, were classified and implemented. Simple additional masking methods are also proposed. A novel weighted PS method, using weighted least square estimation, is presented to eliminate false edges created by the masks. Concurrently, the calibration processes and the illumination configuration were improved. The disturbances due to close light sources were removed by image calibrations. From experimental tests to simulate the light positioning problem, it is concluded that the double zenith illumination configuration have better performance than the optimal single zenith illumination configuration, when the highlights and the shadows are considered. Double zenith illumination configuration results were further improved by the weighted normal PS with a small additional computation cost. All the implemented methods were tested firstly on the controlled environment using synthetic images. Later the same tests were conducted on real objects with varying characteristics as well as the firearm evidences.

<p>The company where this thesis was formulated constructs VR applications for the medical environment. The hardware used is ordinary dektops with consumer level graphics cards and haptic devices. In medicin some operations require microscopes or cameras. In order to simulate these in a virtual reality environment for educational purposes, the effect of depth of field or focus have to be considered. </p><p>A working algorithm that generates this optical occurence in realtime, stereo rendered computer graphics is presented in this thesis. The algorithm is implemented in OpenGL and C++ to later be combined with a VR application simulating eye-surgery which is built with OpenGL Optimizer. </p><p>Several different approaches are described in this report. The call for realtime stereo rendering (~60 fps) means taking advantage of the graphics hardware to a great extent. In OpenGL this means using the extensions to a specific graphic chip for better performance, in this case the algorithm is implemented for a GeForce3 card. </p><p>To increase the speed of the algorithm much of the workload is moved from the CPU to the GPU (Graphics Processing Unit). By re-defining parts of the ordinary OpenGL pipeline via vertex programs, a distance-from-focus map can be stored in the alpha channel of the final image with little time loss. </p><p>This can effectively be used to blend a previously blurred version of the scene with a normal render. Different techniques to quickly blur a renderedimage is discussed, to keep the speed up solutions that require moving data from the graphics card is not an option.</p>

Early detection of breast cancer on screening mammograms is crucial to reduce mortality rates. Computer-aided detection (CADe) systems for mammography are of great importance since they have been shown to positively assist radiologists in detecting early cancer. However, one area where CADe systems for mammography need improvement is in the early detection and annotation of spiculated lesions, which may represent invasive malignancies, and hence, early detection is crucial. Spicule annotation is important since it can yield useful discriminative information about the suspect lesion location on the mammogram and can also provide rich visual evidence to the interpreting radiologist to make the right follow-up decision. However, spicule annotation is a non-trivial task since spicules are fine scale curvilinear structures that are often not clearly visible amidst the surrounding breast parenchyma. The first contribution of this dissertation is an active contour algorithm called snakules for the annotation of spicules on mammography. Observer studies with experienced radiologists to evaluate the performance of snakules demonstrate the potential of the algorithm as an annotation tool that could be used to augment existing spiculated mass CADe systems. Mammography suffers from a major limitation: the 3-D to 2-D projection process results in anatomical noise due to overlapping of out of plane tissue structures, which hinders both radiologists and CADe systems in finding early cancers. This has motivated the development of 3-D breast imaging in the form of breast tomosynthesis, stereoscopic (stereo) mammography, and breast computed tomography (CT) to augment mammography for early cancer detection. Our second contribution is a novel computational stereo model for estimating a dense disparity map from a pair of stereo mammograms. This problem is very important since this is the first step towards elucidating 3-D information that is essential for conducting 3-D digital analysis on the stereo mammogram images. Nearly all of the 3-D structural information of interest on a stereo mammogram exists as a complex network of multi-layered, heavily occluded curvilinear structures, which is unlike what is seen on optical images of the real world. Our proposed stereo model employs a new singularity index as a constraint in a global optimization framework to obtain better estimates of disparity along critical curvilinear structures. The new singularity index is an important contribution of this work. In-depth theoretical analyses and experiments on several real world images demonstrate the efficacy of the index for detecting multi-scale curvilinear structures. Experiments on synthetic images with known ground truth and on real stereo mammograms highlight the advantages of the proposed stereo model over the canonical stereo model. The final contribution of this dissertation is an observer study, which demonstrates the feasibility of viewing breast tomosynthesis projection images stereoscopically. Unlike stereo mammogram images, each tomosynthesis projection image is acquired at a much lower dose. Stereo viewing of tomosynthesis projection images has the potential to reveal the 3-D structure of the breast, unlike the current cine or slice-by slice viewing modes. The results from our study suggest that stereo viewing could be a viable reading mode for breast tomosynthesis data in the future. / text

The primary purpose of this study was to determine the effectiveness of implementing desktop 3-dimensional (3D) stereo images of human anatomy into an undergraduate human anatomy distance laboratory. User perceptions of 2D and 3D images were gathered via questionnaire in order to determine ease of use and level of satisfaction associated with the 3D software in the online learning environment. Mayer's (2001, p. 184) principles of design were used to develop the study materials that consisted of PowerPoint presentations and AVI files accessed via Blackboard. The research design employed a mixed-methods approach. Volunteers each were administered a demographic survey and were then stratified into groups based upon pre-test scores. A total sample size of 62 pairs was available for combined data analysis. Quantitative research questions regarding the effectiveness of 2D versus the 3D treatment were analyzed using a doubly-multivariate repeated measures (Doubly- MANOVA) design. Paired test scores achieved by undergraduates on a laboratory practical of identification and spatial relationships of the bones and features of a human skull were used in the analysis. The questionnaire designed to gather user perceptions consisted of quantitative and qualitative questions. Response frequencies were analyzed for the two groups and common themes were noted. Results revealed a statistically significant difference in group means for the main effect of the treatment groups 2D and 3D and for the variables of identification and relationship with the 3D group outperforming the 2D group on both dependent variables. Effect sizes were determined to be small, 0.215 for the identification variable and 0.359 for the relationship variable. Overall, all students liked the convenience of using PowerPoint and AVI files online. The 3D group felt their PowerPoint was more realistic than did the 2D group and both groups appreciated the detailed labeling of the online images. One third of the volunteers in the 3D group indicated that "eye strain" was what they liked least about working with the 3D images. Results indicate that desktop, stereo imaging may be incorporated effectively into online anatomy and physiology courses, but that more work needs to be done to ensure less eye strain.

The visual appearance of an image is a complex function of factors such as scene geometry, material reflectances and textures, illumination, and the properties of the camera used to capture the image. Understanding how these factors interact to produce an image is a fundamental problem in computer vision and graphics. This dissertation examines two aspects of this problem: models of visual appearance that allow us to recover scene properties from images and videos, and tools that allow users to manipulate visual appearance in images and videos in intuitive ways. In particular, we look at these problems in three different applications. First, we propose techniques for compositing images that differ significantly in their appearance. Our framework transfers appearance between images by manipulating the different levels of a multi-scale decomposition of the image. This allows users to create realistic composites with minimal interaction in a number of different scenarios. We also discuss techniques for compositing and replacing facial performances in videos. Second, we look at the problem of creating high-quality still images from low-quality video clips. Traditional multi-image enhancement techniques accomplish this by inverting the camera’s imaging process. Our system incorporates feature weights into these image models to create results that have better resolution, noise, and blur characteristics, and summarize the activity in the video. Finally, we analyze variations in scene appearance caused by changes in lighting. We develop a model for outdoor scene appearance that allows us to recover radiometric and geometric infor- mation about the scene from images. We apply this model to a variety of visual tasks, including color-constancy, background subtraction, shadow detection, scene reconstruction, and camera geo-location. We also show that the appearance of a Lambertian scene can be modeled as a combi- nation of distinct three-dimensional illumination subspaces — a result that leads to novel bounds on scene appearance, and a robust uncalibrated photometric stereo method. / Engineering and Applied Sciences