Showing papers on "Subpixel rendering published in 1993"

Accurate measurement of three-dimensional deformations in deformable and rigid bodies using computer vision

Abstract: Recently, digital-image-correlation techniques have been used to accurately determine two-dimensional in-plane displacements and strains. An extension of the two-dimensional method to the acquisition of accurate, three-dimensional surfacedisplacement data from a stereo pair of CCD cameras is presented in this paper. A pin-hole camera model is used to express the transformation relating three-dimensional world coordinates to two-dimensional computer-image coordinates by the use of camera extrinsic and intrinsic parameters. Accurate camera model parameters are obtained for each camera independently by (a) using several points which have three-dimensional world coordinates that are accurate within 0.001 mm and (b) using two-dimensional image-correlation methods that are accurate to within 0.05 pixels to obtain the computer-image coordinates of various object positions. A nonlinear, least-squares method is used to select the optimal camera parameters such that the deviations between the measured and estimated image positions are minimized. Using multiple orientations of the cameras, the accuracy of the methodology is tested by performing translation tests. Using theoretical error estimates, error analyses are presented. To verify the methodology for actual tests both the displacement field for a cantilever beam and also the surface, three-dimensional displacement and strain fields for a 304L stainless-steel compact-tension specimen were experimentally obtained using stereo vision. Results indicate that the three-dimensional measurement methodology, when combined with two-dimensional digital correlation for subpixel accuracy, is a viable tool for the accurate measurement of surface displacements and strains.

Electronic speckle photography: analysis of an algorithm giving the displacement with subpixel accuracy.

Abstract: Replacing photographic recording by electronic processing has some obvious advantages. An algorithm used for electronic speckle pattern photography is presented, and the reliability and accuracy is analyzed by using computer-generated speckle patterns. The algorithm is based on a two-dimensional discrete cross correlation between subimages from different images. Subpixel accuracy is obtained by a Fourier series expansion of the discrete correlation surface. The accuracy of the algorithm was found to vary in proportion to sigma/n(1 - delta)(2), where sigma is the speckle size, n is the subimage size, and delta is the amount of decorrelation, with negligible systematic errors. For typical values the uncertainty in the displacement is approximately 0.05 pixels. The uncertainty is found to increase with increased displacement gradients.

Performance analysis and application of grid interpolation techniques for fluid flows

Abstract: Although it is common for automated image processing techniques to claim subpixel accuracy in the identification of particles, or centroids of displacements of groups of particles, additional errors are inevitably introduced when and if these data are reinterpolated back onto a grid mesh whose nodes lie at different locations from the original data. Moreover, these errors can be large compared to the errors introduced in the original image processing step.

Subpixel accuracy image registration by spectrum cancellation

Abstract: An image registration algorithm which achieves subpixel accuracy using a frequency-domain technique is proposed. This approach is efficient compared with the conventional approaches based on interpolation or correlations in the spatial/frequency domain. This approach can achieve subpixel accuracy registration even when images contain aliasing errors due to undersampling. The FFTs (fast Fourier transforms) of the images have computational complexities smaller than the interpolation or convolution computations by orders of magnitude. The accuracy of the proposed approach is demonstrated through computer simulations for different types of images. >

Precision tracking with segmentation for imaging sensors

Abstract: Precision target tracking based on data obtained from imaging sensors when the target is not fully visible during tracking is addressed. The image is divided into several layers of gray level intensities and thresholded. A binary image is obtained and grouped into clusters using image segmentation. The association of the various clusters to the track to be estimated relies on both the motion and pattern recognition characteristics of the target. The centroid measurements of the clusters and the probabilistic data association filter (PDAF) are employed for state estimation. Expressions for the single-frame-based centroid measurement noise variance of the target cluster and the optimal parameters for cluster segmentation are given. Simulation results validate the expressions for the measurement noise variance as well as the performance predictions of the tracking method. For a dim synthetic target with strong background noise, subpixel accuracy in the range of 0.3-0.4 pixel RMS error with moderate

A method for accurate geometric correction of NOAA AVHRR HRPT data

Abstract: A method for the geometric correction of NOAA Advanced Very High Resolution Radiometer (AVHRR) high-resolution picture transmission (HRPT) data is presented. After precise determination of nominal attitude angles for each time instant, geometric correction is done for ground control points (GCPs), and residual errors are interpreted as attitude angle variation effects and in this way corrected. All attitude angle deviations (in pitch, roll, and yaw) are simultaneously corrected by applying to two reference vectors (the vector normal to the scanning plane and the vector that defines the instantaneous viewing direction of the first pixel of each line) a three-axis rotation. A separate program performs the geometric correction, applying the orbital model to each point of the desired output geographical area. An application of this method is presented, in which AVHRR data are registered over a 1:25000 topographic map with subpixel accuracy, allowing the use of AVHRR data in a nearly local scale in combination with other high-resolution data, such as Landsat TM or SPOT. >

Multiresolution image registration procedure using spline pyramids

Abstract: We present an iterative multiresolution algorithm for the translational and rotational alignment of digital images. An image is represented by an interpolating spline. Coarser versions of this continuous image model are obtained by using spline approximations at various scales (polynomial spline pyramid). We use a coarse-to-fine updating strategy to compute the alignment parameters iteratively, using a variation of the Levenberg-Marquardt non-linear least-squares optimization method. This approach yields very precise image registration with subpixel accuracy. It is also much faster and more robust than a comparable single-scale implementation, because the resolution of the underlying image mode is adapted to the step size of the algorithm.

System and method for producing anti-aliased lines

Abstract: A system and method for providing a data processing system operable for displaying anti-aliased lines on a display device comprised of a matrix array of pixels This system receives data pertaining to the line to be displayed, the line being defined by start and end points which position the line relative to the pixels within the matrix array The relative position of the line with respect to pairs of pixels bounding the line are ascertained The illumination intensities for each pixel within each pair is then dependent upon this relative position The relative positions are ascertained by subdividing the matrix array of pixels into subpixel regions so that the subpixel nearest the intersection of the line and a theoretical boundary intersecting the pairs of pixels may be established Illumination intensities for bounding pixels near the end points of the line are also established in the same manner and are then reduced in intensity relative to the distance from the end point to a theoretical boundary intersecting these bounding pixels

Moment-preserving edge detection and its application to image data compression

Abstract: In contrast to the numerous edge-detection techniques that detect edges either point by point or using overlapping circular windows, an edge detector using nonoverlapping rectangular windows is proposed. The detector examines the pixels within each rectangular window of an image, and decides whether an edge element is present or not in the window. Based on the gray and mass moment-preserving principles, the step edge is estimated locally to subpixel accuracy using analytical formulas. To apply the edge detection results to image compression, the detected edge elements are then tracked and grouped based on proximity and orientation. Using the line parameters of the grouped edge elements, region boundaries are approximated in a piecewise linear manner. This reduces the amount of data required to describe region shapes and is useful for compressing some types of images. Good experimental results of compressing character and trademark images are also included to show the feasibility of the proposed approach.

Accurate Projective Reconstruction

Abstract: It is possible to recover the three-dimensional structure of a scene using images taken with uncalibrated cameras and pixel correspondences. But such a reconstruction can only be computed up to a projective transformation of the 3D space. Therefore, constraints have to be added to the reconstructed data in order to get the reconstruction in the euclidean space. Such constraints arise from knowledge of the scene: location of points, geometrical constraints on lines, etc. We first discuss here the type of constraints that have to be added then we show how they can be fed into a general framework. Experiments prove that the accuracy needed for industrial applications is reachable when measurements in the image have subpixel accuracy. Therefore, we show how a real camera can be mapped into an accurate projective camera and how accurate point detection improve the reconstruction results.

EXACT: algorithm and hardware architecture for an improved A-buffer

Abstract: Computer Graphics, vol 27, no 4, August 1993 (SIGGRAPH ’93 Proceedings), pp 85–92 The EXACT (EXact Area Coverage calculaTion) algorithm presented in this paper solves the Hidden Surface Elimination (HSE) problem on the subpixel level The use of subpixel masks for anti-aliasing causes some problems with the HSE on the pixel level that are difficult to overcome The approximations of the well known A-buffer algorithm are replaced by an exact solution that avoids erratic pixels along intersecting or touching surfaces With EXACT the HSE problem on the subpixel level is solved with the help of p-masks P-masks (priority masks) are subpixel masks that indicate for each subpixel which one of two given planes is closer to the viewer An algorithm to produce the p-masks in an efficient way and its hardware implementation are presented The p-mask generator is used in a hardware implementation of an A-buffer algorithm in the form of a rendering pipeline Of course the algorithm can also be used in software to enhance an existing A-buffer implementation The paper ends with the description of the list processing architecture for which the EXACT A-buffer has been built1 ∗Wilhelm–Schickard–Institut fur Informatik, Graphisch–Interaktive Systeme, Auf der Morgenstelle 10/C9, 7400 Tubingen, E-mail: andreas@grisinformatikuni-tuebingende, strasser@grisinformatikuni-tuebingende 1The experiences described here were gained in a research project partly supported by the Commission of the European Communities through the ESPRIT II-Project SPIRIT-workstation, Project No 2484 CR

Effects of window size and shape on accuracy of subpixel centroid estimation of target images

Abstract: A new algorithm is presented for increasing the accuracy of subpixel centroid estimation of (nearly) point target images in cases where the signal-to-noise ratio is low and the signal amplitude and shape vary from frame to frame. In the algorithm, the centroid is calculated over a data window that is matched in width to the image distribution. Fourier analysis is used to explain the dependency of the centroid estimate on the size of the data window, and simulation and experimental results are presented which demonstrate the effects of window size for two different noise models. The effects of window shape were also investigated for uniform and Gaussian-shaped windows. The new algorithm was developed to improve the dynamic range of a close-range photogrammetric tracking system that provides feedback for control of a large gap magnetic suspension system (LGMSS).

Image registration for luminescent paint applications

Abstract: The use of pressure sensitive luminescent paints is a viable technique for the measurement of surface pressure on wind tunnel models. This technique requires data reduction of images obtained under known as well as test conditions and spatial transformation of the images. A general transform which registers images to subpixel accuracy is presented and the general characteristics of transforms for image registration and their derivation are discussed. Image resection and its applications are described. The mapping of pressure data to the three dimensional model surface for small wind tunnel models to a spatial accuracy of 0.5 percent of the model length is demonstrated.

Class of algorithms for real-time subpixel registration

Abstract: In 1972, Barnea and Silverman presented a new approach to the wide field of template matching, the SSD-algorithm. Further work has been done to adapt the method to gain subpixel accuracy. Intense investigation of the proposed algorithms led to our new approach: by interpolating the template instead of the reference image, and by applying sort of an error- correction to the resulting subpixel-value, both computation time and accuracy can be improved. Exhaustive experiments with a CCD-camera and various kinds of reference images showed that a maximum error of 10% of the pixel period can be expected. Depending on the kind of image, mean square errors range from 0.4% to 4%.

Comments on "Design of fiducials for accurate registration using machine vision"

Abstract: The commenters point out that the subpixel registration accuracy improves by one order of magnitude if images are not binarized as indicated in the above said paper by Bose and Amir (ibid. vol.12, p.1196-1200 (1990), but gray scale information is fully exploited in calculating the centroid position. >

Motion detection in image sequences acquired from a moving platform

Abstract: A two-step motion segmentation algorithm for the detection of moving objects in image sequences acquired from a moving platform is presented. First, the image plane transformation induced by the moving platform is estimated using a subpixel accuracy image registration algorithm. The registration algorithm is fully automatic and performs well under significant camera rotation and translation. The input images are then transformed into a common coordinate system (for example, the coordinate system of the first image). One then segments the changed regions from the camera motion-compensated frame difference. Finally, the moving object is detected from the closure of changing segments, and the object motion parameters are estimated. Experimental results for real image sequences are presented. >

Subpixel distance maps and weighted distance transforms

Abstract: An algorithm for computing the Euclidean distance from the boundary of a given digitized shape is presented. The distance is calculated with sub-pixel accuracy. The algorithm is based on an equal distance contour evolution process. The moving contour is embedded as a level set in a time varying function of higher dimension. This representation of the evolving contour makes possible the use of an accurate and stable numerical scheme, due to Osher and Sethian.

Gray pixel halftone encoder

Abstract: A gray pixel encoding scheme is used in combination with a halftoner and an imager to produce halftoned images. A halftoner and an imager can be totally separate because the gray pixel encoding allows the imager to decode them at any time. The encoded gray pixels require less bandwidth than explicit gray pixels and can be decoded by the imager in an optimal fashion for the characteristics of the specific printer being used. An imager produces a small partial pixel within a spot boundary. The subpixel produced is called a gray pixel. The gray pixel is allowed to grow from a zero width to a full spot width and is allowed to start anywhere within the spot. The pixel codes describe the starting positions of the gray pixels, the locations of the gray pixels within the spot, and the gray pixel width required. The imager uses the encoded gray pixel from the halftoner to produce a final halftoned image. The list of encoded pixels produced by the halftoner can be stored and used at a later time or sent through a network to a different imager to be printed out.

Machine vision techniques for subpixel estimation of critical dimensions

Abstract: Novel edge detection and line-fitting machine vision algorithms are applied for linewidth measurement on optical images of integrated circuits. The techniques are used to achieve subpixel resolution. The strategy employs a two-step procedure. In the first step, a neural network is used for edge detection ofthe image. Three neural network approaches are investigated: self-organizing, bootstrap linear threshold, and constrained maximization strategies. The weights of the neural networks are estimated using unsupervised learning procedures, the advantage of which is the ability to adapt to the imaging environment. Consequently, these proposed neural network approaches for edge detection do not require an a priori data base of images with known linewidths for calibration. In the second step, line-fitting methods are applied to the edge maps defined by the neural network to compute linewidth. Two methods are investigated: the Hough transform method and an eigenvector strategy. By employing this two-step strategy, the entire image is used to estimate linewidth as opposed to the use of just a single or a few line scans. Thus, edge roughness effects can be spatially averaged to obtain an optimal estimate of linewidth, and subpixel resolution can be achieved. However, the accuracy (or variance) of this estimate will, of course, be dependent on issues such as pixel size and the capability of the imaging system. The techniques are general and can be used on images from a variety of microscopes, including optical and electron-beam microscopes.

Image registration with use of the epipolar constraint for parallel projections

Abstract: In this paper the registration mapping function is derived for images that are produced by parallel projections. This function has the form F(x, y) = A(x, y) + h(x, y)e, where A(x, y) is an affine transformation, h(x, y) is a scalar-valued function, and e is a vector that defines the epipolar lines. The main result of the paper is the formulation of a normalization constraint that guarantees the uniqueness of the parameters of this function and makes possible their least-squares estimation from a collection of matching points. This approach reduces the search for match points from a two-dimensional to a one-dimensional search along the epipolar lines, thereby increasing the accuracy and robustness of image registration. Simulation results are presented that demonstrate the validity of this approach for nonparallel as well as for parallel imaging geometries. Subpixel registration accuracy is possible for perspective projections as long as either the field of view or the separation angle between the two images is small.

Subpixel registration for a high resolution imaging scheme using multiple imagers

Abstract: In order to obtain high-resolution pictures at sufficiently high SNR, the authors have been investigating a signal-processing-based method that utilizes multiple low-resolution imagers and reconstructs a high-resolution signal by processing the low-resolution images. The method consists of registration of multiple images and reconstruction of high-resolution images. In the present work, the authors discuss an image registration scheme that exploits camera projection parameters so that the precision is substantially improved and the computational complexity is reduced. >

Image registration for automated inspection of printed circuit patterns using CAD reference data

Abstract: An image registration approach for inspection of printed circuit patterns which has been validated on a prototype system is described. Theoffline procedure forms, selects, prioritizes, and sorts registration features from CAD-generated reference data. A feature is selected if it satisfies clearance rules that account for the maximum expecteddiscongruence between captured and reference images. The sorting scheme considers the detection complexity of a feature and its distance away from the center of the expected image, since outer features represent potential global distortions better. Theruntime registration procedure detects features and finds the parameters that transform pixels into reference data coordinates and vice versa. We represent robust feature-measurement techniques that offer accurate subpixel localization and verify feature authenticity. We describe an edge-detection technique based on a novel way of authenticating zero-crossings and a method that disqualifies edges detected on defects of the part under inspection.

Subpixel image registration using circular fiducials

Abstract: The design of fiducials for precise image registration is of major practical importance in computer vision, especially in automatic inspection applications. The authors analyze the subpixel registration accuracy that can, and cannot, be achieved by some rotation-invariant fiducials, and present and analyze efficient algorithms for the registration procedure. They rely on some old and new results from lattice geometry and number theory and efficient computational-geometric algorithms. >

Frequency-domain subpixel position estimation algorithm for overlay measurement

Abstract: In lithographic metrology tasks such as overlay measurement, the pixel size of the imaging device is often much larger than the desired accuracy of the measurement tool. Interpolating the imaged data prior to the application of a measurement algorithm gives an accurate measurement but increases the amount of data to be processed, placing demands upon computational resources. In this paper, we propose a computationally-simple and highly- accurate algorithm for determining the subpixel shift between two waveforms. The algorithm utilizes the fast Fourier transform of each waveform to estimate the relative subpixel shift between the two waveforms using the complex phases of the frequency-domain-transformed data. The algorithm uses a simple iterative search for the position parameter that typically converges in less than ten iterations with optical data. We show that this algorithm estimates subpixel position more accurately than cross-correlation followed by interpolated-peak-fitting using both synthetic and real image data. Moreover, in high signal-to-noise ratio situations, the algorithm's precision can be shown to approach the fundamental precision limit determined by the statistics of the image data. Results of the algorithm's application to overlay data from a commercial scanning-confocal optical microscope are presented.

Subpixel resolution for target tracking

Abstract: Optical sensing mechanisms are designed to provide adequate resolution for the images of the intended objects. Often, the image of an object is so small that the resolution of the image falls beyond the resolution of the sensing device, and some method must be used to attain finer resolution. In these cases, a model-based approach, in which a parametric object model is assumed, can attain the desired sub-pixel resolution capabilities. In the model-based approach, the object model is convolved with the optical system and then matched against the limited number of available sensor samples. The unknown parameters of the object model are then determined by an appropriate estimation technique. This study will focus on estimating the two-dimensional location parameters (i.e. (x,y) location ) of a single point source from a limited number of sensor readings. We present a comparative study of three estimation techniques: maximum likelihood, centroiding, and conditional mean. The sub-pixel resolution capability of these techniques will be studied as a function of signal-to-noise ratio (SNR). The Cramer-Rao theoretical lower bound for unbiased estimators is derived for this problem, and it is shown that the maximum likelihood solution attains the Cramer-Rao bound for SNR’s considered. The merits and deficiencies of the three estimation techniques and their applicability to solving the problem for multiple point sources will also be addressed.

Subpixel edge location shift

Abstract: Subpixel accuracy gauging with solid state cameras has been of great interest in past years. Efforts to reduce errors in subpixel edge locations were directed at the subpixel interpolation technique or in the physical structure of the sensor itself. In this paper we present data which supports the opinion that the major error is caused by the sampling technique. We examine the nonlinearity of the subpixel edge location when moving the edge in equidistant steps in horizontal and vertical directions according to the solid state sensor. The relation between the virtual and physical pixels and the influence of the edge shift on the camera calibration and robot guidance are briefly discussed in this paper as well.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Hierarchical clustering for automated line detection

Abstract: An approach to line detection based on hierarchical stepwise segmentation is developed. Pixels are grouped into line support regions based on the criteria of spatial contiguity and similarity of average gradient orientation. Subpixel equations of the lines are computed from these line support region data through plane fitting and principal component analysis. Four methods of computing subpixel line equations from the detected line support regions are presented, and their performances are compared using both synthetic and real test images. The line support regions produced by the hierarchical segmentation are of good quality. The evaluation of the line estimation schemes shows that the summary statistics method provides excellent estimates of line equations in addition to the simply computed measure of line goodness. >