Camera based probes and machinevision have found increased use in coordinate measuring machines over the past years and the calibration of artifacts for these probes has become an important task for NIST. Until recen...
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Camera based probes and machinevision have found increased use in coordinate measuring machines over the past years and the calibration of artifacts for these probes has become an important task for NIST. Until recently these artifacts have been calibrated using one or two dimensional measuring machines with electro-optic microscopes or scanning devices as probes. These sensors evaluate only a small section of the edge of a grid mark, and irregularities in this particular spot from local deformations or contamination influence the measurement result. Since these measurements result in a single number based on the entire field of view, the influence of small irregularities are not easily detected. Since different probes scan different parts of the grid mark edge they may give systematically different positions of the mark. The conversion to video based sensors has allowed more flexibility it edge detection, although most instruments still use least squares fits as the substitute geometry of straight edges. This method is very susceptible to noise and edge irregularities. We present some experiments for finding the sub-pixel edge point locations and fitting the set of edge points to a line using a fairly simple least sum of absolute deviations fit. Data from a high accuracy 2D measuring machine is used to show the strengths of the algorithms.
A novel concept of object-oriented vision-based recognizing objects of various shapes is introduced. It can be used for a vision-guided manipulator gasping objects without quantitative modeling of the robot and the op...
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A novel concept of object-oriented vision-based recognizing objects of various shapes is introduced. It can be used for a vision-guided manipulator gasping objects without quantitative modeling of the robot and the optical system. The object detection from the background and other irrelevant image information is achieved by observing direct the object appearance in real-time images. By this approach, coordinate transformations and reconstructions of objects are avoided; instead, image data are used directly to control the behavior of the robot, or the interactions of the robot with physical objects. The approach was evaluated and demonstrated in real-word experiments on a vision-guided calibration-free manipulator with five degrees of freedom (DOF) for recognizing and grasping a variety of differently shaped objects in nearly arbitrary orientations and positions anywhere in the robot's 3-D work space.
A high-speed machinevision system far the quality inspection and grading of potatoes has been developed. The vision system grades potatoes on size, shape and external defects such as greening, mechanical damages, rhi...
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ISBN:
(纸本)0819435848
A high-speed machinevision system far the quality inspection and grading of potatoes has been developed. The vision system grades potatoes on size, shape and external defects such as greening, mechanical damages, rhizoctonia, silver scab, common scab, cracks and growth cracks. A 3-CCD line-scan camera inspects the potatoes in flight as they pass under the camera. The use of mirrors to obtain a 360-degree view of the potato and the lack of product holders guarantee a full view of the potato. To achieve the required capacity of 12 tons/hour, 11 SHARC Digital Signal Processors perform the imageprocessing and classification tasks. The total capacity of the system is about 50 potatoes/sec. The color segmentation procedure uses Linear Discriminant Analysis (LDA) in combination with a Mahalanobis distance classifier to classify the pixels. The procedure for the detection of misshapen potatoes uses a Fourier based shape classification technique. Features such as area, eccentricity and central moments are used to discriminate between similar colored defects. Experiments with red and yellow skin-colored potatoes have shown that the system is robust and consistent in its classification.
Dimensional control by artificial vision is becoming a standard tool for industrialists interested in such remote and without contact measurement methods. The expected precision of those systems is largely dependant o...
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Dimensional control by artificial vision is becoming a standard tool for industrialists interested in such remote and without contact measurement methods. The expected precision of those systems is largely dependant on camera resolution and high precision requires very costly CCD sensor and frame grabber. A method is proposed which tends to increase significantly the precision of dimensional measurements without increasing the hardware complexity. This algorithm is also quite robust against noisy images such that it could be encountered in real world imaging, a precision of 1/16 pixel can easily be obtained with SNR = 2dB. Dimensional control by artificial vision generally involves an edge detection stage in its process, it is this step that we propose to improve. A lot of edge detection techniques with pixel resolution are well known and some of them are designed in order to be robust against image corruption. On the other hand B-spline interpolation methods have been considerably improved and popularized by the signal processing techniques proposed by M. Unser and Al.. An algorithm resulting from the merging of these two ideas is proposed in this paper. In this algorithm, the interpolation is prepared by an optimized filtering and by a detection of local maxima of gradient.
The reconstruction of highly detailed, 3D object models is a major goal of current research. Such models can be used in machinevisionapplications as well as for visualization purposes. The method presented here assu...
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The reconstruction of highly detailed, 3D object models is a major goal of current research. Such models can be used in machinevisionapplications as well as for visualization purposes. The method presented here assumes that there are multiple range and intensity image pairs of an object, all registered to a global coordinate system. The individual range images are then used to create a surface mesh and the associated intensity images are applied to the surface mesh as a texture map. These multiple, textured, range meshes are then used to update a volume grid - based upon whether a location in the volume grid is known, unknown, or empty - using information that has the highest confidence for any given voxel. The updated volume grid can then be passed through a marching cubes algorithm with adaptive subdivisions to get a fully textured 3D model. The adaptive marching cubes algorithm takes into account additional information concerning edge weights and texture coordinates to give a smoother surface than that produced with standard marching cubes. Once complete, additional, registered intensity images can be applied to the surface of the object.
A PC based machinevision system is described, designed for precise positioning and reliable recognition of gas oil filters. The system has been integrated into the production line, which is capable of assembling seve...
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A PC based machinevision system is described, designed for precise positioning and reliable recognition of gas oil filters. The system has been integrated into the production line, which is capable of assembling several types of filters, each one having its own visual appearance. Our primary goal was to design a flexible system, which could be easily adapted for assembling different filter types. To achieve this an appearance based method, employing the Karhunen-Loeve expansion, was used. Based on this method, the most significant visual information is automatically extracted from a set of rotated filter images, i.e. templates, and described by a small number of eigenimages. The eigenimages constitute the eigenspace. These templates and the captured image of the filter in an unknown position are projected to the eigenspace. The distances between the projected templates and the projected filter image are computed. Based on these distances, the filter position and its type are determined and the filter rotated. The system operates in a closed loop, therefore the new position can be evaluated and corrected, as required. The results obtained so far show that the system works reliably, and meets the required accuracy and speed.
Traditional computer vision is based on a camera-computer system in which the image understanding algo-rithms are embedded in the computer. To circumvent the computational load of vision algorithms, low-level processi...
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ISBN:
(纸本)0819435848
Traditional computer vision is based on a camera-computer system in which the image understanding algo-rithms are embedded in the computer. To circumvent the computational load of vision algorithms, low-level processing and imaging hardware can be integrated in a single compact module where a dedicated architecture is implemented. This paper presents a Computer vision Camera based on an open architecture implemented in an FPGA. The system is targeted to real-time computer vision tasks where low level processing and feature extraction tasks can be implemented in the FPGA device. The camera integrates a CMOS image sensor, an FPGA device, two memory banks, and an embedded PC for communication and control tasks. The FPGA device is a medium size one equivalent to 25,000 logic gates. The device is connected to two high speed memory banks, an ISA interface, and an imager interface. The camera can be accessed for architecture programming, data transfer, and control through an Ethernet link from a remote computer. A hardware architecture can be defined in a Hardware Description Language (like VHDL), simulated and synthesized into digital structures that can be programmed into the FPGA and tested on the camera. The architecture of a classical multi-scale edge detection algorithm based on a Laplacian of Gaussian convolution has been developed to show the capabilities of the system.
In the semiconductor manufacturing environment, defect imagery is used to diagnose problems in the manufacturing line, train automatic defect classification systems, and examine historical data for trends. image manag...
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In the semiconductor manufacturing environment, defect imagery is used to diagnose problems in the manufacturing line, train automatic defect classification systems, and examine historical data for trends. image management in semiconductor yield management systems is a growing cause of concern since many facilities collect 3000 to 5000 images each month, with future estimates of 12,000 to 20,000. Engineers at Oak Ridge National Laboratory (ORNL) have developed a semiconductor-specific content-based image retrieval architecture, also known as Automated image Retrieval (AIR). We review the AIR system approach including the application environment as well as details on image interpretation for content-based image retrieval. We discuss the software architecture that has been designed for flexibility and applicability to a variety of implementation schemes in the fabrication environment. We next describe details of the system implementation including imageprocessing and preparation, database indexing, and image retrieval. The imageprocessing and preparation discussion includes a description of an imageprocessing algorithm which enables a more accurate description of the semiconductor substrate (non-defect area). We also describe the features used that identify the key areas of the defect imagery. The feature indexing mechanisms are described next, including their implementation in a commercial database. Next, the retrieval process is described, including query imageprocessing. Feedback mechanisms, which direct the retrieval mechanism to favor specified retrieval results, are also discussed. Finally, experimental results are shown with a database of over 10,000 images obtained from various semiconductor manufacturing facilities. These results include subjective measures of system performance and timing details for our implementation.
In 3D inspection applications, a round-view datacloud rather than range data is needed to access the dimensions of an industrial part. This paper discusses how to acquire the round-view datacloud of a part with a stru...
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In 3D inspection applications, a round-view datacloud rather than range data is needed to access the dimensions of an industrial part. This paper discusses how to acquire the round-view datacloud of a part with a structured light machinevision (SLMV) scanner. The SLMV system consists of a line-structured laser, scanning means, image grabber and computer. In this scanning system, the part to be inspected is held on a turntable to sequentially expose different sides of the part to the scanner. For each side off the part, range data is found by triangulation means. Combining range data captured from different sides into a single composite produces a more complete datacloud description of the part's surfaces. Many more dimensions of a typical part can be inspected by analyzing the composite datacloud. The scanning process is divided into two phases: part rotation and surface scanning. Once the turntable is rotated to a specified position, the laser scans the part surfaces available in that position. Data points captured from different positions are merged into one composite datacloud according to a previously found rotational center.
The morphologies of the reinforcement strongly influence the material properties of fiber-reinforced composites. The ability to quantify the morphological features of the composites is important for the prediction of ...
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The morphologies of the reinforcement strongly influence the material properties of fiber-reinforced composites. The ability to quantify the morphological features of the composites is important for the prediction of material properties and for quality control of the manufacturing processes. This paper presents a method for quantitatively measuring fiber braid angles at the surface of a part by the use of computer-aided image-processing analysis. The materials under investigation are triaxially braided glass/urethane composites. The determination of braid angle at a sample surface is performed non-destructively and is based on the use of a standard fast Fourier transform (FFT) method coupled with a novel directional scanning method developed as part of this work. Calibration tests were performed for the purpose of comparison between computer-based pattern recognition and manual determinations. Good agreement between machinevision results and manual measurements for braid angle was found. The machinevision system is fully automated and features rapid image acquisition and processing with good measurement accuracy. It can be applied to on-line quantitative measurements of geometrical features and other applications that are important for quality control and engineering design. (C) 2000 Elsevier Science Ltd. All rights reserved.
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