Low-level processing for real-time image analysis

A system that detects object outlines in television images in real time is described. A high-speed pipeline processor transforms the raw image into an edge map and a microprocessor, which is integrated into the system, clusters the edges, and represents them as chain codes. Image statistics, useful for higher level tasks such as pattern recognition, are computed by the microprocessor. Peak intensity and peak gradient values are extracted within a programmable window and are used for iris and focus control. The algorithms implemented in hardware and the pipeline processor architecture are described. The strategy for partitioning functions in the pipeline was chosen to make the implementation modular. The microprocessor interface allows flexible and adaptive control of the feature extraction process. The software algorithms for clustering edge segments, creating chain codes, and computing image statistics are also discussed. A strategy for real time image analysis that uses this system is given

Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

Learning to Transform Time Series with a Few Examples

We describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. This algorithm is applied to tracking, where a time series of observations from sensors is transformed to a time series describing the pose of a target. Instead of defining and implementing such transformations for each tracking task separately, our algorithm learns a memoryless transformation of time series from a few example input-output mappings. The algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. The learning procedure is fast and lends itself to a closed-form solution. It is closely related to nonlinear system identification and manifold learning techniques. We demonstrate our algorithm on the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences. For these tasks, this algorithm requires significantly fewer examples compared to fully-supervised regression algorithms or semi-supervised learning algorithms that do not take the dynamics of the output time series into account

Time-varying volume visualization

Volume rendering is a very active research field in Computer Graphics because of its wide range of applications in various sciences, from medicine to flow mechanics. In this report, we survey a state-of-the-art on time-varying volume rendering. We state several basic concepts and then we establish several criteria to classify the studied works: IVR versus DVR, 4D versus 3D+time, compression techniques, involved architectures, use of parallelism and image-space versus object-space coherence. We also address other related problems as transfer functions and 2D cross-sections computation of time-varying volume data. All the papers reviewed are classified into several tables based on the mentioned classification and, finally, several conclusions are presented.Preprin

Automatic 3D human modeling: an initial stage towards 2-way inside interaction in mixed reality

3D human models play an important role in computer graphics applications from a wide range of domains, including education, entertainment, medical care simulation and military training. In many situations, we want the 3D model to have a visual appearance that matches that of a specific living person and to be able to be controlled by that person in a natural manner. Among other uses, this approach supports the notion of human surrogacy, where the virtual counterpart provides a remote presence for the human who controls the virtual character\u27s behavior. In this dissertation, a human modeling pipeline is proposed for the problem of creating a 3D digital model of a real person. Our solution involves reshaping a 3D human template with a 2D contour of the participant and then mapping the captured texture of that person to the generated mesh. Our method produces an initial contour of a participant by extracting the user image from a natural background. One particularly novel contribution in our approach is the manner in which we improve the initial vertex estimate. We do so through a variant of the ShortStraw corner-finding algorithm commonly used in sketch-based systems. Here, we develop improvements to ShortStraw, presenting an algorithm called IStraw, and then introduce adaptations of this improved version to create a corner-based contour segmentatiuon algorithm. This algorithm provides significant improvements on contour matching over previously developed systems, and does so with low computational complexity. The system presented here advances the state of the art in the following aspects. First, the human modeling process is triggered automatically by matching the participant\u27s pose with an initial pose through a tracking device and software. In our case, the pose capture and skeletal model are provided by the Microsoft Kinect and its associated SDK. Second, color image, depth data, and human tracking information from the Kinect and its SDK are used to automatically extract the contour of the participant and then generate a 3D human model with skeleton. Third, using the pose and the skeletal model, we segment the contour into eight parts and then match the contour points on each segment to a corresponding anchor set associated with a 3D human template. Finally, we map the color image of the person to the 3D model as its corresponding texture map. The whole modeling process only take several seconds and the resulting human model looks like the real person. The geometry of the 3D model matches the contour of the real person, and the model has a photorealistic texture. Furthermore, the mesh of the human model is attached to the skeleton provided in the template, so the model can support programmed animations or be controlled by real people. This human control is commonly done through a literal mapping (motion capture) or a gesture-based puppetry system. Our ultimate goal is to create a mixed reality (MR) system, in which the participants can manipulate virtual objects, and in which these virtual objects can affect the participant, e.g., by restricting their mobility. This MR system prototype design motivated the work of this dissertation, since a realistic 3D human model of the participant is an essential part of implementing this vision

Dynamic Illumination for Augmented Reality with Real-Time Interaction

Current augmented and mixed reality systems suffer a lack of correct illumination modeling where the virtual objects render the same lighting condition as the real environment. While we are experiencing astonishing results from the entertainment industry in multiple media forms, the procedure is mostly accomplished offline. The illumination information extracted from the physical scene is used to interactively render the virtual objects which results in a more realistic output in real-time. In this paper, we present a method that detects the physical illumination with dynamic scene, then uses the extracted illumination to render the virtual objects added to the scene. The method has three steps that are assumed to be working concurrently in real-time. The first is the estimation of the direct illumination (incident light) from the physical scene using computer vision techniques through a 360° live-feed camera connected to AR device. The second is the simulation of indirect illumination (reflected light) from the real-world surfaces to virtual objects rendering using region capture of 2D texture from the AR camera view. The third is defining the virtual objects with proper lighting and shadowing characteristics using shader language through multiple passes. Finally, we tested our work with multiple lighting conditions to evaluate the accuracy of results based on the shadow falling from the virtual objects which should be consistent with the shadow falling from the real objects with a reduced performance cost