A Few Photons Among Many: Unmixing Signal and Noise for Photon-Efficient Active Imaging

Conventional LIDAR systems require hundreds or thousands of photon detections to form accurate depth and reflectivity images. Recent photon-efficient computational imaging methods are remarkably effective with only 1.0 to 3.0 detected photons per pixel, but they are not demonstrated at signal-to-background ratio (SBR) below 1.0 because their imaging accuracies degrade significantly in the presence of high background noise. We introduce a new approach to depth and reflectivity estimation that focuses on unmixing contributions from signal and noise sources. At each pixel in an image, short-duration range gates are adaptively determined and applied to remove detections likely to be due to noise. For pixels with too few detections to perform this censoring accurately, we borrow data from neighboring pixels to improve depth estimates, where the neighborhood formation is also adaptive to scene content. Algorithm performance is demonstrated on experimental data at varying levels of noise. Results show improved performance of both reflectivity and depth estimates over state-of-the-art methods, especially at low signal-to-background ratios. In particular, accurate imaging is demonstrated with SBR as low as 0.04. This validation of a photon-efficient, noise-tolerant method demonstrates the viability of rapid, long-range, and low-power LIDAR imaging

Survey of currently available high-resolution raster graphics systems

Presented are data obtained on high-resolution raster graphics engines currently available on the market. The data were obtained through survey responses received from various vendors and also from product literature. The questionnaire developed for this survey was basically a list of characteristics desired in a high performance color raster graphics system which could perform real-time aircraft simulations. Several vendors responded to the survey, with most reporting on their most advanced high-performance, high-resolution raster graphics engine

Visual Odometry Estimation Using Selective Features

The rapid growth in computational power and technology has enabled the automotive industry to do extensive research into autonomous vehicles. So called self- driven cars are seen everywhere, being developed from many companies like, Google, Mercedes Benz, Delphi, Tesla, Uber and many others. One of the challenging tasks for these vehicles is to track incremental motion in runtime and to analyze surroundings for accurate localization. This crucial information is used by many internal systems like active suspension control, autonomous steering, lane change assist and many such applications. All these systems rely on incremental motion to infer logical conclusions. Measurement of incremental change in pose or perspective, in other words, changes in motion, measured using visual only information is called Visual Odometry. This thesis proposes an approach to solve the Visual Odometry problem by using stereo-camera vision to incrementally estimate the pose of a vehicle by examining changes that motion induces on the background in the frame captured from stereo cameras. The approach in this thesis research uses a selective feature based motion tracking method to track the motion of the vehicle by analyzing the motion of its static surroundings and discarding the motion induced by dynamic background (outliers). The proposed approach considers that the surrounding may have moving objects like a truck, a car or a pedestrian body which has its own motion which may be different with respect to the vehicle. Use of stereo camera adds depth information which provides more crucial information necessary for detecting and rejecting outliers. Refining the interest point location using sinusoidal interpolation further increases the accuracy of the motion estimation results. The results show that by using a process that chooses features only on the static background and by tracking these features accurately, robust semantic information can be obtained

Integrating Multiple 3D Views through Frame-of-reference Interaction

Frame-of-reference interaction consists of a unified set of 3D interaction techniques for exploratory navigation of large virtual spaces in nonimmersive environments. It is based on a conceptual framework that considers navigation from a cognitive perspective, as a way of facilitating changes in user attention from one reference frame to another, rather than from the mechanical perspective of moving a camera between different points of interest. All of our techniques link multiple frames of reference in some meaningful way. Some techniques link multiple windows within a zooming environment while others allow seamless changes of user focus between static objects, moving objects, and groups of moving objects. We present our techniques as they are implemented in GeoZui3D, a geographic visualization system for ocean data

Toward General Purpose 3D User Interfaces: Extending Windowing Systems to Three Dimensions

Recent growth in the commercial availability of consumer grade 3D user interface devices like the Microsoft Kinect and the Oculus Rift, coupled with the broad availability of high performance 3D graphics hardware, has put high quality 3D user interfaces firmly within the reach of consumer markets for the first time ever. However, these devices require custom integration with every application which wishes to use them, seriously limiting application support, and there is no established mechanism for multiple applications to use the same 3D interface hardware simultaneously. This thesis proposes that these problems can be solved in the same way that the same problems were solved for 2D interfaces: by abstracting the input hardware behind input primitives provided by the windowing system and compositing the output of applications within the windowing system before displaying it. To demonstrate the feasibility of this approach this thesis also presents a novel Wayland compositor which allows clients to create 3D interface contexts within a 3D interface space in the same way that traditional windowing systems allow applications to create 2D interface contexts (windows) within a 2D interface space (the desktop), as well as allowing unmodified 2D Wayland clients to window into the same 3D interface space and receive standard 2D input events. This implementation demonstrates the ability of consumer 3D interface hardware to support a 3D windowing system, the ability of this 3D windowing system to support applications with compelling 3D interfaces, the ability of this style of windowing system to be built on top of existing hardware accelerated graphics and windowing infrastructure, and the ability of such a windowing system to support unmodified 2D interface applications windowing into the same 3D windowing space as the 3D interface applications. This means that application developers could create compelling 3D interfaces with no knowledge of the hardware that supports them, that new hardware could be introduced without needing to integrate it with individual applications, and that users could mix whatever 2D and 3D applications they wish in an immersive 3D interface space regardless of the details of the underlying hardware

Region of Interest Generation for Pedestrian Detection using Stereo Vision

Pedestrian detection is an active research area in the field of computer vision. The sliding window paradigm is usually followed to extract all possible detector windows, however, it is very time consuming. Subsequently, stereo vision using a pair of camera is preferred to reduce the search space that includes the depth information. Disparity map generation using feature correspondence is an integral part and a prior task to depth estimation. In our work, we apply the ORB features to fasten the feature correspondence process. Once the ROI generation phase is over, the extracted detector window is represented by low level histogram of oriented gradient (HOG) features. Subsequently, Linear Support Vector Machine (SVM) is applied to classify them as either pedestrian or non-pedestrian. The experimental results reveal that ORB driven depth estimation is at least seven times faster than the SURF descriptor and ten times faster than the SIFT descriptor