Showing papers on "Kernel (image processing) published in 1983"

Measuring Volterra kernels

Abstract: Volterra series have been in the engineering literature for some time now, and yet there have been few attempts to measure Volterra kernels. This paper discusses techniques for measuring the Volterra kernels of weakly nonlinear systems. We introduce a new quick method for measuring the second Volterra kernel which is analogous to pseudonoise testing of a linear device. To illustrate the discussion we present an experimental example, an electro-acoustic transducer. Throughout the paper we emphasize the practical aspects of kernel measurement.

A Kernel Language for Algebraic Specification and Implementation - Extended Abstract

Abstract: A kernel specification language called ASL is presented. ASL comprises five fundamental but powerful specification-building operations and has a simple semantics. Behavioural abstraction with respect to a set of observable sorts can be expressed, and (recursive) parameterised specifications can be defined using a more powerful and more expressive parameterisation mechanism than usual. A simple notion of implementation permitting vertical and horizontal composition (i.e. it is transitive and monotonic) is adopted and compared with previous more elaborate notions. A collection of identities is given which can provide a foundation for the development of programs by transformation.

Implementation of cellular-logic operators using 3∗3 convolution and table lookup hardware

Abstract: Cellular-logic operations such as erosion, dilation, contour extraction, skeletonization, local majority voting, and pepper-and-salt noise removal are essential in processing binary images. It is shown that cellular-logic operations, like some homomorphic filters, can be constructed from a 3∗3 convolution and a nonlinear table lookup, features of many commercially available image-processing systems. The proposed method extends the field of application of such systems from enhancement and other preprocessing of gray-valued images to the processing and measurement of objects in the segmented image.

Background pattern removal by power spectral filtering.

Abstract: A new method for removing periodic background patterns from pictures is presented. The basic spatial frequency composition of the pattern is determined from an estimate of the power spectrum of the picture. A convolutional restoration kernel is then created from a modified version of the power spectrum. The method is extremely effective and can be automated.

Image processing system.

Abstract: An image processing system comprises: a word organised random access memory for storing a digital input image having X rows by Y columns of image points, the memory comprising N memory blocks each having XY/N image point storage locations; storage means for storing the digital input image in the memory in such manner that for consecutive rows of the input image the first image point in each row i (0

Rule based strategies for image interpretation

Abstract: We present an interpretation system which utilizes world knowledge in the form of simple object hypothesis rules, and more complex interpretation strategies attached to object and scene schemata, to reduce the ambiguities in image measurements. These rules involve sets of partially redundant features each of which defines an area of feature space which represents a "vote" for an object. Convergent evidence from multiple interpretation strategies is organized by top-down control mechanisms in the context of a partial interpretation. One such strategy extends a kernel interpretation derived through the selection of object exemplars, which represent the most reliable image specific hypotheses of a general object class, resulting in the extension of partial interpretations from islands of reliability.

Ξ-filters

Abstract: A new family of nonlinear digital convolution filters is described having unusual and valuable properties. The response is constant phase over the entire input frequency range and, in some forms, no signal whatsoever is passed beyond the cutoff frequency and side-lobes are identically zero. By simply iterating the transform upon which a Ξ-filter is based without changing the size of the convolution kernel, the cutoff frequency may be varied. Use of Ξ-filters is analyzed and demonstrated as applied to ultrahigh-speed image processing using table-lookup computation.

An indicator for Wiener-Hopf integral equations with invertible analytic symbol

Abstract: For Wiener-Hopf integral equations with an operator or matrix valued kernel and with an invertible symbol which is analytic on the real line and at infinity an indicator is introduced. In general this indicator is a bounded linear operator, but when the kernel is matrix valued and the symbol is rational it is a (possibly non-square) matrix. From the indicator the invertibility properties and Fredholm characteristics of the integral equation can be read off. The class of Wiener-Hopf equations studied here is also described in terms of growth conditions on the derivatives of the kernel.

An operating system interface for transportable image processing software

Abstract: The key to portability is the use of a kernel of routines that interface to the operating system of an individual machine. The kernel provides the sophisticated but standard operating system services required by image processing software. It makes the operating system of each computer appear identical and, when carefully designed, does not pose a difficult implementation problem. Above this interface, all applications programs can be machine independent, written is a structured language, such as RATFOR, without sacrificing power or ease of use on any machine. The details of such an interface design are given. This interface, called the kernel, has been implemented on the IBM 370/VM and the VAX 11/780.

A model reduction algorithm by spline approximation in the deconvolution of seismic signals

Abstract: This paper presents a new method for obtaining ap - proximate low-order recursive (ARMA) realizations of convolution models. It is based upon optimum piecewise linear or cubic spline approximation of the convolution kernel. The method may be efficiently used in the deconvolution of seismic signal. The basic seismic wavelet is approximated by splines and a fixed-lag smoothing state-space representation, equivalent to the resulting ARMA model, is derived. Use of Kalman filtering gives fixed-lag smoothed estimates of the so-called reflection coefficient sequence. Adaptive estimation is used in the case when the seismic wavelet is not known apriori. Simulation results using a Ricker wavelet are presented, which illustrate the performance af the proposed method.

Spatial Frequency Filtering In Digital Subtraction Angiography (DSA) By Real-Time Digital Video Convolution

Abstract: A circuit has been constructed to perform spatial frequency filtration on DSA images at real-time video rates. The 10-bit device performs low-pass or high-pass filtering, and with external memory can perform bandpass and more sophisticated filtering. Pixels in the convolving kernel are weighted independendently in the x- and y-directions to provide a Gaussian-like convolving function. The kernel width ranges from 3-30 pixels and appropriate weighting yields a FWHM of the Gaussian kernel function as small as 0.85 pixel width for horizontal image widths of ≤256 pixels and as small as 1.25 pixel width for a horizontal image width of 512 pixels. Applications to be investigated include scatter and glare correction for videodensitometry, enhancement of arteries behind large opacified structures such as the ventricle and aorta, noise suppression in low-spatial frequency DSA exams, edge-enhancement of images, and partial-pixel shifting. Peli and Lim have suggested a more sophisticated algorithm which enhances high-pass filtration only in dark regions of an image. This and other techniques may be implemented with the current circuit to enhance small detail in highly opacified regions such as the ventricle, while leaving the rest of the image unaltered.

Kernel for a responsive and graphical user interface

Abstract: A kernel that facilitates building graphical and responsive user interfaces for application programs has been constructed. A display tree representing the structure of the 2-dimen-sional screen image is defined. Each node contains an input procedure, an output procedure and links to their arguments. This display tree is the only interface between the user and the application program. For viewing purposes, the display tree can be ‘painted’ onto any viewport. The output routines attached to the nodes are invoked to produce the image. The arguments found in the ancestor nodes can be thought of as shared graphical attributes. Upon each input event, the display tree is traversed to determine which node is touched by the cursor. The input procedure attached to that node is then invoked. The use of a library with standard attached procedures (e.g. for screen editing and error checking) leads to a system that behaves uniformly across applications. Some demonstration programs, based on this kernel, show the very dynamic screen communication that can be achieved.

Deconvolution of Gaussian and Other Kernels

Abstract: We consider the numerical solution of the convolution equation h * f = d for various nonnegative kernels h. Obviously, the Dirac measure is the easiest kernel to deconvolve. In a certain sense, there also exists a unique most difficult kernel to deconvolve, namely the Gaussian exp(−x2). A method for deconvolution of the Gaussian is suggested.

Cellular Logic On 3X3 Convolution Hardware

Abstract: Cellular-logic operations like erosion, dilation, contour extraction, skeletonization, local majority voting and pepper-and-salt noise removal are essential in processing binary images. We show that these operations, like some homomorphic filters, can be constructed from a 3*3 convolution and non-linear table look-up, features of many image processing systems on the market. The method proposed extends their field of application from grey-value to binary images.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Direct Image Plane Deblurring

Abstract: We describe here a digital image restoration technique for image data degraded by an a-priori known blur function. To be more specific, we are interested in processing digital image data, obtained using coherent illumination (either optical or microwaves), that has been degraded by a known blur factor, The complex valued image i (amplitude) is related to the measured blurred data i by i = i * b, where * denotes convolution and b is a known complex-valued blur function. An inverse filtering technique is traditionally used in the Fourier domain. But, for some image analysis applications, a more direct, deblurring approach in the image domain may be more desirable. An illustrative possible scenario is given by an image interpreter looking at a small portion of some blurred image. In this case a direct deblurring method applied to the selected image area might be more flexible. We present here a direct image plane deconvolution method using reasonably short length convolution kernels. In the more specific case of quadratic phase-type blurs, a direct image plane Fresnel transform approach is also discussed.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In-time non-iterative fast-algorithm for support constrained deconvolution

Abstract: It has been shown, in the kernel splitting method for support constrained deconvolution recently presented, that the restored signal can be obtained from an infinite series of time-functions. In the present work it is proved that the series is finite when the convolution kernel is left-time-limited. Each function of the series is itself a series built from delayed replicas of the convolved signal multiplied by weighted and delayed rectangular windows. The non-overlapping time-regions of the windows are first located and then by zero trivial multiplication are not computed. Then an in-time non-iterative fast algorithm is deduced. Finally the input recovery of a linear system whose transfer function vanishes at several points is investigated both with a noise-free or a noisy output signal. In these conditions where inverse filtering fails, the present algorithm works well.