Showing papers on "Structuring element published in 1990"

The analysis of morphological filters with multiple structuring elements

Abstract: Mathematical morphology has been widely used in the image processing area in recent years. Due to the nature of mathematical morphology, there are limitations in the performance of simple morphological algorithms that use a single structuring element. Combinations of morphological operators with other set operations as well as the utilization of multiple structuring elements can enhance the performance of morphological-based filters. In this paper we present properties of morphological operators that use multiple structuring elements. We show that these properties can be used to design complex morphological-based algorithms.

Minimal search for the optimal mean-square digital gray-scale morphological filter

Abstract: To characterize optimal mean-square morphological filters it is first necessary to interpret morphological operations in a functional manner appropriate to the theory of statistical estimation. The present paper takes such an approach in the case of digital N-observation grayscale filters these being defined via the Matheron representation. Having obtained the optimality criterion we are lead to the characterization of a minimal search space the nodes of the space being potential erosion structuring elements. More precisely there exists a set of structuring elements which will always contain elements forming the basis for an optimal MS filter. Moreover the set called the fundamental set is minimal in the sense that no element can be deleted from it without possibly yielding a set not containing the optimal structuring element for a single-erosion filter© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

High-speed architectures for morphological image processing

Abstract: This paper presents a dual architecture for the high-speed realization of basic morphological operations. Since mor-phological filtering can be described as a combination of erosion and dilation, two basic building blocks are required for therealization of any morphological filter. Architectures for the two basic units, namely the erosion unit and the dilation unit,are proposed and studied in terms of cycle time, hardware complexity, and cost. These basic units are similar in structure tothe systolic array architecture used in the implementation of linear digital filters. Correspondingly, the proposed units arehighly modular and are suitable for efficient VLSI implementation. These basic units allow the processing of either binary or gray-scale images. They are particularly suitable for applications in robotics, where speed, size and cost are of critical importance. 1. INTRODUCTION Image processing and analysis based on mathematical morphology has been an active research area in recent years[1,2]. The strength of mathematical morphology lies in its natural coupling between the shape of the image under investiga-tion and the structuring element. That is, by carefully selecting a suitable structuring element, morphological operations canbe used for image filtering such as noise removal andimage smoothing. Mathematical morphology can also be used as an

VLSI gray-scale morphology processor for real-time NDE image-processing applications

Abstract: This paper describes the VLSI hardware implementation onto an ASIC (Application Specific Integrated Circuit) of a gray-scale morphology processor. This is a prototype first chip of a series for a project to perform real-time image processing for NDE (Non-Destnicve Evaluation) applications. Processing of images requires the performance of relatively simple mathematical operations like additions subtractions and comparisons on a tremendously large amount of pixels (data points). This hardware implementation uses the idea of an array processor where each processor performs the exact same operations at the same time but on different pixels. The morphology operation is done in a highly parallel fashion thus enabling a real time performance of 80 MOPS (Million Operations Per Second). The chip described in this paper has the capability of performing both erosion and dilation operations on variable size images with a variable size structuring element. This custom chip was designed using a 2. t CMOS process on a die size of 6. 8 x 6. 9 mm2 in the Department of Electrical Engineering and Computer Engineering at Iowa State University. Custom layout was chosen over standard cell implementation in order to reduce the area of the chip and to increase the operational speed. The fabrication is being done through the MOSIS fabrication services. For the project the final version of the chip will be implemented in a 1jt CMOS technology thus enhancing the speed and© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Filtering by morphological pseudoconvolutions

Abstract: The present paper introduces a digital gray-scale morphological filtering technique that is based on postfiltering a given filter so the invariant class of the new filter is larger than the invariant class of the original filter. More specifically, the invariant class of the new filter contains the original invariant class together with all signals whose variation is below a chosen threshold. The postfiltering includes a single erosion and a single dilation by a one-parameter structuring element, the choice of parameter determining the resulting invariant class. While the methodology is quite general, the two applications considered pertain to moving averages with nonnegative weights and moving means, both of which are morphological filters. Both suppress noise in a signal and each possesses specific advantages and disadvantages. In brief, means tend to give better noise suppression while blurring edges, whereas medians preserve edges, while at the same time flattening small background variation in the underlying signal. The new one parameter family of filters derived from a moving average, called pseudoconvolutions, preserve steps beneath a predetermined threshold. The filters derived from medians, called pseudomedians, preserve uncorrupted low background variation. Because they preserve small variation while at the same time behaving like their mother filters, both filters are especially effective when the noise occurs in bursts, rather than uniformly across the signal.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An efficient approach to automatic shape recognition

Abstract: Automated shape recognition in two dimensions is analyzed, and an efficient approach is provided to reduce the redundancy of the matching process. Shape recognition is achieved through locating the objects to be recognized within the field of view. Two theorems are presented. The first theorem is applied to the shape-recognition problem and indicates a clear advantage in reducing the redundancy involved in the matching process when morphological operations are used. Experiments show that a large amount of computing time is saved as a consequence of this theorem. This theorem also offers advantages when performing automated shape recognition on images acquired in a noisy environment. The second theorem suggests that edges of shapes can be used as a structuring element, and all possible outcomes due to noisy environments need to be exhausted. >

Environment for the automatic manipulation and analysis of morphological expressions

Abstract: This paper describes a LISP based environment for the automatic manipulation and analysis of morphological expressions. The foundation of this environment is an aggregation of morphological knowledge that includes signal and system property information rule bases for representing morphological relationships and inferencing mechanisms for using this collection of knowledge. The layers surrounding this foundation include representations of abstract signal and structuring element classes as well as actual structuring elements implementations of the morphological operators and the ability to optimally decompose structels. The representational requirements for automatically manipulating expressions and determining the computational cost are described and the capabilities of the environment are illustrated by examples of symbolic manipulations and expression analysis.

Automated shape recognition in noisy environments based on morphological algorithms

Abstract: Automatic shape recognition using morphological operators has proved to be an effective approach to the problem of shape recognition. We present the problem of shape recognition in noisy environments as that of the problem of recognizing imperfect shapes. The method we present in this paper does not require the use of all possible variations of a shape. Instead, this method employs a priori known shape information as a basis for structuring elements, transforms objects into structuring elements, then uses the structuring elements in a hit-or-miss operation to find the location of the shape being recognized. The choice of structuring elements is critical. The resulting image after the hit or-miss operation contains a set of points which indicate the locations of the target shape. Each occurrence of this target shape is represented by one point, or a small cluster of points within a known disk. A number of examples illustrating the process of recognizing imperfect shapes show that, even though the noise environment changes the appearance of the shapes to be recognized in images, our method provides a fast and accurate solution.

Applications of matrix morphology

Abstract: The current concept of mathematical morphology involves erosions and dilations using structuring elements and will be called scalar morphology in this paper. Scalar morphology can be extended to a matrix morphology formalism by the unusual idea that each matrix element is a set in EN space. An image matrix is an array of separate images and a structuring element matrix is defined to be an array where each component is a structuring element. Dilations or erosions of a matrix of images by a matrix of structuring elements consist of a number of dilations or erosions of various image components with structuring element components. The rules of matrix operations will tell which image components are transformed by which structuring element components and how the results are combined into a new array. Set unions and intersections are analogous to matrix addition. All non-increasing transformations can be described as a matrix erosion followed by a dilation inner product. Applications using scalar morphology can consist of a sequence of operations where several intermediate images are generated and recombined in various ways to eventually give a final result. These intermediate images can represent different features. Specific combinations of features in certain configurations can provide an identification or location of an object. It turns out that sequences of this type that consist of a multiplicity of operations on a multiplicity of images fit very well into the

Application of morphological filters in ultrasonic flaw detection

Abstract: Morphological filters were used to detect flaws in ultrasonic signals contained by grain scattering noise (i.e., speckles). In particular, the deterministic and stochastic properties of morphological filters were studied in the context of utilizing different structuring elements. The structuring elements were characterized by their shape, width, and height. The information content of ultrasonic signals was used to design a suitable structuring element to enhance the flaw-to-clutter ratio. The processed experimental results show that morphological filters can detect flaw echoes while the suppressing microstructure noise. >

Banach Morphology: An Alternative To Impenetrability

Abstract: Least-squares template matching and correlation have long been used for detecting, locating, and quantifying features in images. More recently, binary and grey-level morphology have been used to accomplish similar functions. Least squares techniques involve fitting a function to the image in such a way as to minimize the norm of the error in,L2(Hilbert) space, and result in errors being distributed approximately equally on both sides (+ and -) of the fitted function. Morphological techniques involve fitting a function (i.e., a structuring element) to the image in L_space, and result in all the errors being distributed on the same side of the fitted function. Thus, the final result is completely determined by the worst case error, without regard for the other errors. As a result, morphological techniques are very sensitive to noise. There is a need for a technique falling between these two extremes of "full penetration" (12 space) and "no penetration" (L2 space). Banach spaces (Lp-spaces) provide the basis for such a technique. The Banach-space norm for discretized images has the form II f IIp = [(1/N)ΣIfjIP](1/p)where the summation is taken over all of the N pixels in the structuring element. By an appropriate choice of p, the degree of penetration appropriate to a given type of problem and image noise level can be achieved. This paper presents the mathematical basis for Banach morphology, gives some simple examples, describes possible implementations on existing hardware, and suggests an architecture suited to high-speed implementation.

Symbolic manipulation and analysis of morphological expressions

Abstract: A LISP based system is described for performing the automatic manipulation and analysis of morphological expressions. The system is capable of four types of symbolic manipulations: expression simplification, equivalent-form generation, signal-property analysis, and dual-form generation. The different types of knowledge that need to be represented in such a system include morphological system knowledge, signal and structuring element knowledge, and knowledge about the interaction of signals and morphological systems. As integral components of the analysis system, signals and structuring elements are represented by abstract classes which are distinguished by their properties. In addition, actual structuring elements are also represented and may be used with both abstract signals and abstract structuring elements. The interaction of signals, structuring elements, and systems is determined by a collection of morphological rule bases. >

Nonlinear multiscale filtering using mathematical morphology

Abstract: Mathematical Morphology is a new branch of mathematics powerful enough to study some vision problems like multiscale filtering. Due to the fact morphological openings smooth the signal while preserving the edges, and using the three Matheron's axioms, an important result is obtained: morphological openings do not introduce additional zero-crossing as one moves to a coarser scales. With these results a multiscale filtering scheme is developed. The choice of the structuring element is constrained to the sub-space of convex, compact and homothetic ones. In this paper we will report a procedure for choosing the structuring element based on the pre-filtering effects of morphological openings and the subsequent detection of edges.

VLSI chip architecture design for 2-D gray-level morphological operations

Abstract: The basic operations of mathematical morphology are quite useful for a broad area of image processing and analysis tasks. All morphological operations can be built from erosions and dilations. In this paper we develop a single chip VLSI architecture of an erosion/dilation algorithm for real-time image processing. The new architecture allows sequential inputs and performs parallel processing with 100 percent efficiency. The erosions (mm of differences) and dilations (max of additions) operate on 2-D gray-level image signals and use a 5 x 5-pixel gray-level structuring element. Two chips can be cascaded for a 7 x 7 structuring element. The overall chip design also provides the user with sufficient flexibility to optionally offset the output images by adding a constant level and/or scale their dynamic range. 1.