We develop multicomponent AM-FM models for multidimensional signals, The analysis is cast in a general n-dimensional framework where the component modulating functions are assumed to lie in certain Sobolev spaces. For...
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We develop multicomponent AM-FM models for multidimensional signals, The analysis is cast in a general n-dimensional framework where the component modulating functions are assumed to lie in certain Sobolev spaces. For both continuous and discrete LSI systems with AM-FM inputs, powerful new approximations are introduced that provide closed form expressions for the responses in terms of the input modulations. The approximation errors are bounded by generalized energy variances quantifying the localization of the filter impulse response and by Sobolev norms quantifying the smoothness of the modulations. The approximations are then used to develop novel spatially localized demodulation algorithms that estimate the AM and FM functions for multiple signal components simultaneously from the channel responses of a multiband linear filterbank used to isolate components. Two discrete computational paradigms are presented. Dominant component analysis estimates the locally dominant modulations in a signal, which are useful in a variety of machinevisionapplications, while channelized components analysis delivers a true multidimensional multicomponent signal representation, We demonstrate the techniques on several images of general interest in practical applications, and obtain reconstructions that establish the validity of characterizing images of this type as sums of locally narrowband modulated components.
Texture analysis is an important generic research area of machinevision for detection of patterns in two-dimensional data representations. Despite the wide array of potential areas of application for texture analysis...
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Texture analysis is an important generic research area of machinevision for detection of patterns in two-dimensional data representations. Despite the wide array of potential areas of application for texture analysis, only a limited number of successful exploitations of texture exist so far since most reported techniques lack the computational tractability required in industry. Neural network based classifiers have also been proposed for texture recognition. Recent studies of the visual cortex of the cat highlight the role of temporal processing using synchronous oscillations for object identification. In this paper, the original Eckhorn's neural model is modified according to Johnson for texture classification and analysis. A two-dimensional texture image can be mapped into a one-dimensional output function time signature. Each time signature in the form of 8-bit grey level images are further presented to a second PCNN to produce binary barcodes. There is a one-to-one correspondence between these barcoded PCNN outputs and the corresponding input images. The effectiveness of this novel method is demonstrated using 50 textures taken from Brodatz texture album. An n-tuple (RAM-based) neural network is finally used for recognition. Our test results demonstrate that the approach is fast and robust making it suitable for real-time applications.
We present two families of reflective surfaces that are capable of providing a wide field of view, and yet still approximate a perspective projection to a high degree. These surfaces are derived by considering a plane...
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ISBN:
(纸本)0769507042;0769507050
We present two families of reflective surfaces that are capable of providing a wide field of view, and yet still approximate a perspective projection to a high degree. These surfaces are derived by considering a plane perpendicular to the axis of a surface of revolution and finding the equations governing the distortion of the image of the plane in this surface. We then view this relation as a differential equation and prescribe the distortion term to be linear. By choosing appropriate initial conditions for the differential equation and solving it numerically, we derive the surface shape and obtain a precise estimate as to what degree the resulting sensor can approximate a perspective projection. Thus these surfaces act as computational sensors, allowing for a wide-angle perspective view of a scene without processing the image in software. The applications of such a sensor should be numerous, including surveillance, robotics and traditional photography.
Motion estimation in image sequences is a fundamental issue in many applications as for instance in artificial vision and three-dimensional scene reconstruction. Among all the existing techniques, we are particularly ...
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Motion estimation in image sequences is a fundamental issue in many applications as for instance in artificial vision and three-dimensional scene reconstruction. Among all the existing techniques, we are particularly interested in methods using Gabor filters, which are known to furnish quality results but usually require intensive calculation. A new, fast energy-based method is presented, which combines in a direct manner the energetic responses of Gabor spatio-temporal filters organized in triads. An implementation of this technique on a general purpose Digital Signal Processor (DSP) board is described and the advantages compared with Very Large Scale Integration (VLSI) and parallel machine approaches. Our hardware implementation attains a reasonably fast output rate (several images/second) for a better resolution than in the most recent VLSI implementations. These results open interesting perspectives for real-time implementations (such as in mobile robotics) and for the obtention of higher-level results by combining different Gabor filter techniques (for motion, edge detection, texture analysis, etc.).
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.
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 low power smart vision system based on a large format (currently 1K×1K) active pixel sensor (APS) integrated with a programmable neural processor for fast visionapplications is presented. The concept of buildi...
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A low power smart vision system based on a large format (currently 1K×1K) active pixel sensor (APS) integrated with a programmable neural processor for fast visionapplications is presented. The concept of building a low power smart vision system is demonstrated by a system design, which is composed with an APS sensor, a smart image window handler, and a neural processor. The paper also shows that it is feasible to put the whole smart vision system into a single MCM chip in a standard CMOS technology. This smart vision system on-a-chip can take the combined advantages of the optics and electronics to achieve ultra-high-speed smart sensory information processing and analysis at the focal plane. The proposed system will enable many applications including robotics and machinevision, guidance and navigation, automotive applications, and consumer electronics. Future applications will also include scientific sensors such as those suitable for highly integrated imaging systems used in NASA deep space and planetary spacecraft.
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.
This paper describes research on the application of machinevision techniques to a real time automatic inspection task of air filter components in a manufacturing line. A novel calibration algorithm is proposed based ...
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This paper describes research on the application of machinevision techniques to a real time automatic inspection task of air filter components in a manufacturing line. A novel calibration algorithm is proposed based on a special camera setup where defective items would show a large calibration error. The algorithm makes full use of rigid constraints derived from the analysis of geometrical properties of reflected correspondence vectors which have been synthesized into a single coordinate frame and provides a closed form solution to the estimation of all parameters. For a comparative study of performance, we also developed another algorithm based on this special camera setup using epipolar geometry. A number of experiments using synthetic data have shown that the proposed algorithm is generally more accurate and robust than the epipolar geometry based algorithm and that the geometric properties of reflected correspondence vectors provide effective constraints to the calibration of rigid body transformations.
An in-line, non-destructive process is being developed for characterizing polycrystalline thin-film and other large area electronic devices using computer vision based imaging of the manufacturing and inspection steps...
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An in-line, non-destructive process is being developed for characterizing polycrystalline thin-film and other large area electronic devices using computer vision based imaging of the manufacturing and inspection steps during the device fabrication process. This process is being applied specifically to Cadmium Telluride/Cadmium Sulfide (CdTe/CdS) thin film, polycrystalline solar cells. Our process involves the acquisition of reflective, transmission and electroluminescence (EL) intensity images for each device. The EL intensity images have been processed by use of a modified median cut segmentation. The processed images reveal different gray level regions corresponding to different intensities of EL originating from radiative recombination events occurring within a biased solar cell. Higher efficiency devices show a more uniform intensity distribution in contrast with lower efficiency devices. The uniform intensity regions are made up of gray level intensity values found near the mean of the histogram distribution these are identified as regions of good device performance and are attributed to better material quality and processing. Low intensity regions indicate either material defects or errors in processing. This novel characterization process and analysis are providing new insights into the causes of poor performance in CdTe-based solar cells.
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