The Near-Infrared Spectrograph (NIRSPEC) on board the James Webb Space Telescope can be reconfigured in space for
astronomical observation in a range of NIR sub-bands as well as spectral resolutions. Reconfiguration of the NIRSpec
instrument will be achieved using a Filter Wheel Mechanism (FWA) which carries 7 transmission filters and one reflective
mirror and a Grating Wheel Mechanism (GWA) which carries six gratings and one prism. The dispersive components
on the grating wheel (GWA) cooperate with the edge transmission filters mounted on the filter wheel (FWA) which block
the higher dispersion orders of the gratings. The paper gives an overview on the design of all optical elements, their key
requirements and the employed manufacturing approach. Test results from breadboard and component level qualification
phase are also given.
The Grating and Filter Wheel Mechanisms of the JWST NIRSpec instrument allow for reconfiguration of the
spectrograph in space in a number of NIR sub-bands and spectral resolutions. Challenging requirements need to be met
simultaneously including high launch loads, the large temperature shift to cryo-space, high position repeatability and
minimum deformation of the mounted optics. The design concept of the NIRSpec wheel mechanisms is based on the
ISOPHOT Filter Wheels but with significant enhancements to support much larger optics. A well-balanced set of design
parameters was to be found and a considerable effort was spent to adjust the hardware within narrow tolerances.
The Near-Infrared Spectrograph (NIRSpec) onboard the James Webb Space Telescope can be reconfigured in space for astronomical observation in a range of filter bands as well as spectral resolutions. This will be achieved using a Filter wheel (FWA) which carries 7 transmission filters and a Grating wheel (GWA) which carries six gratings and one prism. The large temperature shift between warm launch and cryogenic operation (30K) and high launch vibration loads on the one hand side and accurate positioning capability and minimum deformation of optical components on the other hand side must be consolidated into a single mechanical design which will be achieved using space-proven concepts derived from the successful ISO filter wheel mechanisms which were manufactured and tested by Carl Zeiss. Carl Zeiss Optronics has been selected by Astrium GmbH for the implementation of both NIRSpec wheel mechanisms. Austrian Aerospace and Max-Planck-Institut fur Astronomie Heidelberg (MPIA) will contribute major work shares to the project. The project was started in October 2005 and the preliminary designs have been finalized recently. Critical performance parameters are properly allocated to respective hardware components, procurements of long-lead items have been initiated and breadboard tests have started. This paper presents an overview of the mechanism designs, discusses its properties and the approach for component level tests.
In this paper, we will derive a phenomenological model of the bidirectional
reflectance distribution function of non-Lambertian metallic
materials typically used in industrial inspection. We will show, how
the model can be fitted to measured reflectance values and how the
fitted model can be used to determine a suitable illumination position.
Together with a given sensor pose, this illumination position can
be used to calculate the necessary shutter time, aperture, focus setting
and expected gray value to successfully perform a given visual inspection
task. The paper concludes with several example inspection tasks.
One of the key issues for a successful inspection process is the determination of the necessary number of cameras and their respective positions given a specific inspection task and a geometric model of the inspected work-piece and its surroundings.
In the last decades, a number of approaches concerning camera positioning strategies have been proposed. Generally, these approaches define an inspection task in terms of good visibility of certain features on the surface of the inspected objects. However, these approaches neither provide general means to include arbitrary inspection requirements, nor do they minimize the number of required cameras. Others use only hard constraints to determine the area of feasibility for certain task requirements. To overcome these shortcomings, we propose a model-based approach to optimize one or more camera positions by optimizing cost-functions derived from the inspection task. The goal is to use a minimum number of cameras / camera positions to fulfill the inspection task. Feature-visibility is represented using a novel concept: the visibility map. It can be calculated quickly by using a projective approach, consumes little storage memory and allows for quick feature-visibility checks. The system is evaluated on several real-world examples using real inspection tasks from current production processes.
In this paper, we propose a method to estimate the pose of tube-shaped flexible objects from their shadow cast on non-planar backgrounds. To compensate for the distortion of the shadow by the non-planar backgrounds, we introduce a model-based method to calculate a look-up-table that enables scene-specific undistortion of shadow images if the geometric relations of camera, background and light source are known. In accordance with the established term image rectification that is used to correct images for camera lens errors we propose the term geometric rectification for this process. It is shown how to estimate the 3d position of tube-shaped flexible objects from geometrically rectified shadow images. For unknown lamp positions, we present a method to estimate the position of the point light from an image showing a special calibration rig. The method is verified on synthetic and real world example images taken from Industrial Machine Vision applications.
In this paper, we will present a novel method for shape reconstruction
of flexible objects, such as rubber-tubes, from monocular images.
We understand shape as the three-dimensional position of a tube model
in world space. Model knowledge that is available through CAD-data
is used to infer parameters for an active contour algorithm "snake".
Unlike traditional image-based snakes, our active contour algorithm
optimizes fully three-dimensional tube-models in world space by projecting a 3d representation of itself onto the image plane. Using a novel method to estimate a 3d tangent of a curve by means of differential texture distortion, we exploit information from the monocular image that is not used in traditional edge-based active contour methods. Integrating both model and image information as energy terms into the active contour algorithm the 3d position of the tube is iteratively refined until an optimum shape of the tube is found.
In this paper, we present a novel approach for multiple-feature, multiple-sensor classification and localization of three-dimensional objects in two-dimensional images. We use a hypothesize-and-test-approach where we fit three-dimensional geometric models to image data. A hypothesis consists of an object's class and its six degrees of freedom. Our models consist of the objects' geometric data which is attributed with several local features, e.g. hotspots, edges and textures, and their respective rule of applicability (e.g. visibility). The model-fitting process is divided into three parts: using the hypothesis we first project the object onto the image plane while evaluating the rules of applicability for its local features. Hence, we get a two-dimensional representation of the objects which - in a second step - is aligned to the image data. In the last step, we perform a pose estimation to calculate the object's six degrees of freedom and to update the hypothesis out of the alignment results. The paper describes the major components of our system. This includes the management and generation of the hypotheses, the matching process, the pose estimation, and model-based prediction of the object's pose in six degrees of freedom. At the end, we show the performance, robustness and accuracy of the system in two applications (optical inspection for quality control and airport ground-traffic surveillance).
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