Image processing algorithm design and implementation for real-time autonomous inspection of mixed waste

The ARIES {number_sign}1 (Autonomous Robotic Inspection Experimental System) vision system is used to acquire drum surface images under controlled conditions and subsequently perform autonomous visual inspection leading to a classification as `acceptable` or `suspect`. Specific topics described include vision system design methodology, algorithmic structure,hardware processing structure, and image acquisition hardware. Most of these capabilities were demonstrated at the ARIES Phase II Demo held on Nov. 30, 1995. Finally, Phase III efforts are briefly addressed

Eliciting Domain Knowledge in Handwritten Digit Recognition

Abstract. Pattern recognition methods for complex structured objects such as handwritten characters often have to deal with vast search spaces. Developed techniques, despite significant advancement in the last decade, still face some performance barriers. We believe that additional knowl-edge about the structure of patterns, elicited from humans perceptions, will help improve the recognition’s performance, especially when it comes to classify irregular, outlier cases. We propose a framework for the trans-fer of such knowledge from human experts and show how to incorporate it into the learning process of a recognition system using methods based on rough mereology. We also demonstrate how this knowledge acquisi-tion can be conducted in an interactive manner, with a large dataset of handwritten digits as an example.

Advanced Sensing and Control Techniques to Facilitate Semi-Autonomous Decommissioning of Hazardous Sites - Final Report

This report summarizes work after 4 years of a 3-year project (no-cost extension of the above-referenced project for a period of 12 months granted). The fourth generation of a vision sensing head for geometric and photometric scene sensing has been built and tested. Estimation algorithms for automatic sensor calibration updating under robot motion have been developed and tested. We have modified the geometry extraction component of the rendering pipeline. Laser scanning now produces highly accurate points on segmented curves. These point-curves are input to a NURBS (non-uniform rational B-spline) skinning procedure to produce interpolating surface segments. The NURBS formulation includes quadrics as a sub-class, thus this formulation allows much greater flexibility without the attendant instability of generating an entire quadric surface. We have also implemented correction for diffuse lighting and specular effects. The QRobot joint level control was extended to a complete semi-autonomous robot control system for D and D operations. The imaging and VR subsystems have been integrated and tested

Advanced sensing and control techniques to facilitate semi-autonomous decommissioning of hazardous sites. 1997 annual progress report, September 15, 1996--September 14, 1997

'The first year of this effort emphasized independent development and refinement of each of the three major subsystems (imaging/AI, robotics and virtual reality). Two of the three efforts emphasized the critical task of site. virtualization, prior to telepresence-guided D and D. Substantial algorithm refinement and software and hardware development occurred in each area. Relevant publications resulting from this work are cited below.

Advanced sensing and control techniques to facilitate semi-autonomous decommissioning. 1998 annual progress report

'This research is intended to advance the technology of semi-autonomous teleoperated robotics as applied to Decontamination and Decommissioning (D and D) tasks. Specifically, research leading to a prototype dual-manipulator mobile work cell is underway. This cell is supported and enhanced by computer vision, virtual reality and advanced robotics technology. This report summarizes work after approximately 1.5 years of a 3-year project. The autonomous, non-contact creation of a virtual environment from an existing, real environment (virtualization) is an integral part of the workcell functionality. This requires that the virtual world be geometrically correct. To this end, the authors have encountered severe sensitivity in quadric estimation. As a result, alternative procedures for geometric rendering, iterative correction approaches, new calibration methods and associated hardware, and calibration quality examination software have been developed. Following geometric rendering, the authors have focused on improving the color and texture recognition components of the system. In particular, the authors have moved beyond first-order illumination modeling to include higher order diffuse effects. This allows us to combine the surface geometric information, obtained from the laser projection and surface recognition components of the system, with a stereo camera image. Low-level controllers for Puma 560 robotic arms were designed and implemented using QNX. The resulting QNX/PC based low-level robot control system is called QRobot. A high-level trajectory generator and application programming interface (API) as well as a new, flexible robot control API was required. Force/torque sensors and interface hardware have been identified and ordered. A simple 3-D OpenGL-based graphical Puma 560 robot simulator was developed and interfaced with ARCL and RCCL to assist in the development of robot motion programs.

Edge detection based on the Shannon Entropy by piecewise thresholding on remote sensing images

Edge detection is one of the most important concepts used in processing of remote sensing images. The aim of edge detection is to mark the points of an image at which the rate of brightness changes sharply. Sharp changes in image features often represent important events and changes in environmental properties. In other words, edges can be defined as the boundary between two regions separated by two relatively distinct grey level properties. Most classic mathematical methods for edge detection are based on deriving original image pixels such as Laplacian gradient operator. In remote sensing images, because of the high variation rate, the edge detection operators may have some weaknesses in correct detection of the scope of complications. This study provides a novel approach for detecting the edges based on the features of remote sensing images. In this method, at first, thresholds of different regions of the image were determined in a piecewise manner; then, by using the proposed methods, appropriate thresholds were extracted, and finally, the boundary between these regions was extracted using Shannon entropy. The obtained results were compared with some standard algorithms and it was observed that the method was efficiently able to detect edges