Towards the automation of product geometric verification: An overview

The paper aims at providing an overview on the current automation level of geometric verification process with reference to some aspects that can be considered crucial to achieve a greater efficiency, accuracy and repeatability of the inspection process. Although we are still far from making this process completely automatic, several researches were made in recent years to support and speed up the geometric error evaluation and to make it less human-intensive. The paper, in particular, surveys: (1) models of specification developed for an integrated approach to tolerancing; (2) state of the art of Computer-Aided Inspection Planning (CAIP); (3) research efforts recently made for limiting or eliminating the human contribution during the data processing aimed at geometric error evaluation. Possible future perspectives of the research on the automation of geometric verification process are finally described

Radiation imaging apparatus

A radiation imaging system using a charge multiplier and a position sensitive anode in the form of periodically arranged sets of interconnected anode regions for detecting the position of the centroid of a charge cloud arriving thereat from the charge multiplier. Various forms of improved position sensitive anodes having single plane electrode connections are disclosed. Various analog and digital signal processing systems are disclosed, including systems which use the fast response of microchannel plates, anodes and preamps to perform scintillation pulse height analysis digitally

Semantic Segmentation of Human Model Using Heat Kernel and Geodesic Distance

A novel approach of 3D human model segmentation is proposed, which is based on heat kernel signature and geodesic distance. Through calculating the heat kernel signature of the point clouds of human body model, the local maxima of thermal energy distribution of the model is found, and the set of feature points of the model is obtained. Heat kernel signature has affine invariability which can be used to extract the correct feature points of the human model in different postures. We adopt the method of geodesic distance to realize the hierarchical segmentation of human model after obtaining the semantic feature points of human model. The experimental results show that the method can overcome the defect of geodesic distance feature extraction. The human body models with different postures can be obtained with the model segmentation results of human semantic characteristics

Research in interactive scene analysis

An interactive scene interpretation system (ISIS) was developed as a tool for constructing and experimenting with man-machine and automatic scene analysis methods tailored for particular image domains. A recently developed region analysis subsystem based on the paradigm of Brice and Fennema is described. Using this subsystem a series of experiments was conducted to determine good criteria for initially partitioning a scene into atomic regions and for merging these regions into a final partition of the scene along object boundaries. Semantic (problem-dependent) knowledge is essential for complete, correct partitions of complex real-world scenes. An interactive approach to semantic scene segmentation was developed and demonstrated on both landscape and indoor scenes. This approach provides a reasonable methodology for segmenting scenes that cannot be processed completely automatically, and is a promising basis for a future automatic system. A program is described that can automatically generate strategies for finding specific objects in a scene based on manually designated pictorial examples

Guidance for benthic habitat mapping: an aerial photographic approach

This document, Guidance for Benthic Habitat Mapping: An Aerial Photographic Approach, describes proven technology that can be applied in an operational manner by state-level scientists and resource managers. This information is based on the experience gained by NOAA Coastal Services Center staff and state-level cooperators in the production of a series of benthic habitat data sets in Delaware, Florida, Maine, Massachusetts, New York, Rhode Island, the Virgin Islands, and Washington, as well as during Center-sponsored workshops on coral remote sensing and seagrass and aquatic habitat assessment. (PDF contains 39 pages) The original benthic habitat document, NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation (Dobson et al.), was published by the Department of Commerce in 1995. That document summarized procedures that were to be used by scientists throughout the United States to develop consistent and reliable coastal land cover and benthic habitat information. Advances in technology and new methodologies for generating these data created the need for this updated report, which builds upon the foundation of its predecessor

Video guidance, landing, and imaging systems

The adaptive potential of video guidance technology for earth orbital and interplanetary missions was explored. The application of video acquisition, pointing, tracking, and navigation technology was considered to three primary missions: planetary landing, earth resources satellite, and spacecraft rendezvous and docking. It was found that an imaging system can be mechanized to provide a spacecraft or satellite with a considerable amount of adaptability with respect to its environment. It also provides a level of autonomy essential to many future missions and enhances their data gathering ability. The feasibility of an autonomous video guidance system capable of observing a planetary surface during terminal descent and selecting the most acceptable landing site was successfully demonstrated in the laboratory. The techniques developed for acquisition, pointing, and tracking show promise for recognizing and tracking coastlines, rivers, and other constituents of interest. Routines were written and checked for rendezvous, docking, and station-keeping functions

Modeling of objects using planar facets in noisy range images

Products designed and manufactured before the advent of Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) technology have not been documented electronically. To avoid the laborious procedure of redesigning the parts, a reverse engineering approach can be adopted. This approach involves, taking a picture of the object and constructing a solid model from the image data. Range image is a three dimensional image of an object or a scene. This image can be obtained from special cameras, called range image cameras, or can be constructed from the Coordinate Measuring Machine\u27s (CMM) output data. Adaptive Fuzzy c-Elliptotype (AFC) clustering algorithm is used to identify the planar facets in a range image. A modified version of AFC algorithm can handle noisy range images. Unknown number of planar facets can be identified using the Agglomerative clustering approach. The object is reconstructed using segmented image data. The equations of the edge are obtained from the plane intersections. An edge validity criterion is developed to validate the existence of an edge. Vertices are the two extreme points on the edge. A Boundary representation of the object is developed. The information about this object is then passed to a CAD software using Initial Graphics Exchange Specification (IGES)

ANALYSIS OF FULL-WAVEFORM LIDAR DATA FOR CLASSIFICATION OF URBAN AREAS

International audienceIn contrast to conventional airborne multi-echo laser scanner systems, full-waveform (FW) lidar systems are able to record the entire emitted and backscattered signal of each laser pulse. Instead of clouds of individual 3D points, FW devices provide connected 1D profiles of the 3D scene, which contain more detailed and additional information about the structure of the illuminated surfaces. This paper is focused on the analysis of FW data in urban areas. The problem of modelling FW lidar signals is first tackled. The standard method assumes the waveform to be the superposition of signal contributions of each scattering object in such a laser beam, which are approximated by Gaussian distributions. This model is suitable in many cases, especially in vegetated terrain. However, since it is not tailored to urban waveforms, the generalized Gaussian model is selected instead here. Then, a pattern recognition method for urban area classification is proposed. A supervised method using Support Vector Machines is performed on the FW point cloud based on the parameters extracted from the post-processing step. Results show that it is possible to partition urban areas in building, vegetation, natural ground and artificial ground regions with high accuracy using only lidar waveforms