Near real-time stereo vision system

The apparatus for a near real-time stereo vision system for use with a robotic vehicle is described. The system is comprised of two cameras mounted on three-axis rotation platforms, image-processing boards, a CPU, and specialized stereo vision algorithms. Bandpass-filtered image pyramids are computed, stereo matching is performed by least-squares correlation, and confidence ranges are estimated by means of Bayes' theorem. In particular, Laplacian image pyramids are built and disparity maps are produced from the 60 x 64 level of the pyramids at rates of up to 2 seconds per image pair. The first autonomous cross-country robotic traverses (of up to 100 meters) have been achieved using the stereo vision system of the present invention with all computing done onboard the vehicle. The overall approach disclosed herein provides a unifying paradigm for practical domain-independent stereo ranging

Study of optical techniques for the Ames unitary wind tunnels. Part 4: Model deformation

A survey of systems capable of model deformation measurements was conducted. The survey included stereo-cameras, scanners, and digitizers. Moire, holographic, and heterodyne interferometry techniques were also looked at. Stereo-cameras with passive or active targets are currently being deployed for model deformation measurements at NASA Ames and LaRC, Boeing, and ONERA. Scanners and digitizers are widely used in robotics, motion analysis, medicine, etc., and some of the scanner and digitizers can meet the model deformation requirements. Commercial stereo-cameras, scanners, and digitizers are being improved in accuracy, reliability, and ease of operation. A number of new systems are coming onto the market

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

Remote sensing applications in forestry - The development of an earth resources information system using aerial photographs and digital computers Annual progress report

Earth resources information system using aerial photographs and digital computer

Scanning from heating: 3D shape estimation of transparent objects from local surface heating

Today, with quality becoming increasingly important, each product requires three-dimensional in-line quality control. On the other hand, the 3D reconstruction of transparent objects is a very difficult problem in computer vision due to transparency and specularity of the surface. This paper proposes a new method, called Scanning From Heating (SFH), to determine the surface shape of transparent objects using laser surface heating and thermal imaging. Furthermore, the application to transparent glass is discussed and results on different surface shapes are presented

Formulation and error analysis for a generalized image point correspondence algorithm

A Generalized Image Point Correspondence (GIPC) algorithm, which enables the determination of 3-D motion parameters of an object in a configuration where both the object and the camera are moving, is discussed. A detailed error analysis of this algorithm has been carried out. Furthermore, the algorithm was tested on both simulated and video-acquired data, and its accuracy was determined

Toward-1mm depth precision with a solid state full-field range imaging system

Previously, we demonstrated a novel heterodyne based solid-state full-field range-finding imaging system. This system is comprised of modulated LED illumination, a modulated image intensifier, and a digital video camera. A 10 MHz drive is provided with 1 Hz difference between the LEDs and image intensifier. A sequence of images of the resulting beating intensifier output are captured and processed to determine phase and hence distance to the object for each pixel. In a previous publication, we detailed results showing a one-sigma precision of 15 mm to 30 mm (depending on signal strength). Furthermore, we identified the limitations of the system and potential improvements that were expected to result in a range precision in the order of 1 mm. These primarily include increasing the operating frequency and improving optical coupling and sensitivity. In this paper, we report on the implementation of these improvements and the new system characteristics. We also comment on the factors that are important for high precision image ranging and present configuration strategies for best performance. Ranging with sub-millimeter precision is demonstrated by imaging a planar surface and calculating the deviations from a planar fit. The results are also illustrated graphically by imaging a garden gnome

Motion analysis report

Human motion analysis is the task of converting actual human movements into computer readable data. Such movement information may be obtained though active or passive sensing methods. Active methods include physical measuring devices such as goniometers on joints of the body, force plates, and manually operated sensors such as a Cybex dynamometer. Passive sensing de-couples the position measuring device from actual human contact. Passive sensors include Selspot scanning systems (since there is no mechanical connection between the subject's attached LEDs and the infrared sensing cameras), sonic (spark-based) three-dimensional digitizers, Polhemus six-dimensional tracking systems, and image processing systems based on multiple views and photogrammetric calculations

Generalized Image Translation and Realignment: The GITR Process

Image processing is continually hampered by the effects of noise. This paper introduces the GITR noise reduction and analysis system. It is performing removal and analysis of noise in images of a scale model of the University of Pennsylvania campus. GITR uses several algorithms including voting and median filtering to remove noise and difference of images and histogram functions for noise analysis. Also, this work includes reports on inherent noise contained in the cameras available in the Penn GRASP Laboratory. A stuck-pixel map and analysis of any inherent noisy screen pattern is presented for four camera systems. Statistical data for all noise patterns is also given. Future research directions concerning other noise removal algorithms and comparisons with this study, and reports of noise profiles of other cameras or camera lenses are motivated by the results of this experiment, namely study of the effect of combining using rotated or scaled data