Novel Binary Adaptive Morphological Operators

SKIPSM (separated-kernel image processing using finite state machines) is a technique for implementing large-kernel binary- morphology operators and many other operations. While earlier papers on SKIPSM concentrated mainly on implementations using pipelined hardware, there is considerable scope for achieving major speed improvements in software systems. Using identical control software, one-pass binary erosion and dilation structuring elements (SEs) ranging from the trivial (3 by 3) to the gigantic (51 by 51, or even larger), are readily available. Processing speed is independent of the size of the SE, making the SKIPSM approach practical for work with very large SEs on ordinary desktop computers. PIP (prolog image processing) is an interactive machine vision prototyping environment developed at the University of Wales Cardiff. It consists of a large number of image processing operators embedded within the standard AI language Prolog. This paper describes the SKIPSM implementation of binary morphology operators within PIP. A large set of binary erosion and dilation operations (circles, squares, diamonds, octagons, etc.) is available to the user through a command-line driven dialogue, via pull-down menus, or incorporated into standard (Prolog) programs. Little has been done thus far to optimize speed on this first software implementation of SKIPSM. Nevertheless, the results are impressive. The paper describes sample applications and presents timing figures. Readers have the opportunity to try out these operations on demonstration software written by the University of Wales, or via their WWW home page at http://bruce.cs.cf.ac.uk/bruce/index.html .

Fast parallel processing of gray-scale images and exact hard-clip thresholding are two important functionalities necessary in optoelectronic implementations of structural processors. The parallel nature of processing stems from optical implementation of local operations with arrays of active smart pixels. We have demonstrated a morphological image processor composed of arrays of bistable optoelectronic transceivers which are connected in differential pairs and work as comparators. The use of differential pairs of optical thyristors fabricated in GaAs technology allows to realize a dual rail architecture for this photonic morphological image processor. The processor consists of a thresholding module and a binary morphological processing module. The thresholding module decomposes gray level images into series of binary slices. In the binary morphological processing module operations are performed within a neighborhood defined by a structuring element implemented as a diffractive fan-out element. In the prototype set-up we demonstrate median filtering, dilation and erosion operations performed for an image of 8x8 pixels and threshold decomposition of 6 gray level images. In principle all rank order as well as morphological filters can be optically calculated in the set-up. Additional functionality of the processor is achieved with use of the electronic layer with digital cellular processors. The electronic layer, designed as an array of simple digital processors, realizes a set of operations on binary images using 4 bit programmable weights. Simulation results for 0.8 µm CMOS technology are presented. We discuss the limitations of the photonic morphological image processing with respect to bandwidth, parallelism and architecture of the processor.

In this paper, a novel image denoising method with a morphological opening is proposed. In many image recovery, the regularization has been introduced to prevent overfitting between a recovered image and a noisy observation. In the regularization of an image, the smoothness of the image is assumed. The cost function, which is minimized during the regularization, is defined as a sum of the approximation error term and the smoothness penalty of the image. In morphological image processing, an image is approximated as a union of structuring elements (SEs) by the morphological image opening. In the approximation with the opening, the SE represents the local intensity surface of the image. We hence impose the smoothness penalty on the SE, instead of the recovered image itself, and propose an image opening method with the regularization penalty for the SE. In denoising, the approximation error is minimized under the constraint of the penalty for the SE. In experiments, we show that the proposed filtering method has ability to remove impulsive noises from degraded texture images.

Binary morphological operations are an important means for real time image processing applications. While fixed size structuring elements are sufficient for usual image processing steps, more sophisticated algorithms require structuring elements of variable shape. These operations can be realized by using Boolean logic in connection with dead time elements, which leads to a straightforward implementation if using FPGAs.

Development of industrial image processing applications is strictly governed by economical concerns. High demands with respect to recognition speed and certainty have to be satisfied by means of low-cost hardware implementations. Especially realization of such algorithms for extraction of regional information, e.g. for shape recognition, is thus a challenging task. The latter is addressed by investigating the implementability of binary morphological operations with large operator masks. The design procedure for implementation in low-cost FPGAs as well as a relatively accurate estimation of the required hardware effort is presented. Programmable operator mask sizes of up to 31 /spl times/ 31 pixels and a high data throughput of several giga-pixels per second can be obtained by the proposed methods.

A morphological image enhancement method based on the GAN (General Adaptive Neighborhood) structuring elements is proposed for some problems such as low overall contrast, noise and inter-target edge of some remote sensing images. Firstly, a pixel to be processed is selected as a seed point, and an adaptive neighborhood is defined according to the image pixel characteristic and the neighborhood constraint relation to obtain the self-adaptive structuring elements set. A corresponding self-adaptive morphological operation based on the self-adaptive structuring elements set is constructed with reference to the classical morphological operation definition, and the self-adaptive alternative filter is constructed by using the adaptive morphological operation combination so as to realize the enhancement of the remote sensing image. The experimental results show that the method can preserve the accurate positioning of the objects edge while enhancing the image, avoid the loss of information, has better noise removing performance. Compared with the classical morphological filter operator, the single-scale Retinex algorithm and bi-histogram equalization (BHE) algorithm, the contrast ratio and the signal-to-noise ratio of the enhanced image are improved.

A general form of morphological operators is developed. The structuring elements of the operators adapt their shapes according to the local features of the processed image and can be any shape formed by connecting a given number of predetermined basic elements. The basic element can be a single pixel or any other connected small shape. It is shown that while sharing the basic properties of the conventional morphological operators, the operators also have some distinguished properties. Those properties are related to intuitive descriptions of the geometric performances of the operators and the development of fast algorithms for the implementation of the operators. The efficiency of the operators in image processing is discussed, and two application examples are given.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, basic concepts of digital binary morphological operations, i.e., dilation and erosion are investigated on a triangular grid. Every triangle pixel is addressed by a unique coordinate triplet with sum zero (even pixels) or one (odd pixels). Even and odd pixels have different orientations. The triangular grid is not a lattice, that is, not every translation with a grid vector maps the grid to itself. Therefore, to extend the morphological operations to the triangular grid is not straightforward. We introduce three types of definition for both of dilation and erosion. Various examples and properties of the considered dilation and erosion are analyzed on the triangular grid.

An overview of SKIPSM (eparated-Kemel Jinage rocessing using Finite state Machines), a powerful new way to implement many standard image processing operations, is presented in a two companion ape2 This paper describes the application of SKJPSM to grey-level morphology, which involves . in some cases, the reformulation of the grey-level morphology problem as a set of binary morphology operations, . the separation of 2-D morphological operations into a row operation followed by a column operation, . the formulation of these row and column operations in a form compatible with pipelined operation, S the implementation of the resulting operations as simple finite-state machines, and S theautomated generation of the finite-state machine configuration data. Grey-level morphology presents some difficulties to the SKJPSM paradigm having to do with word length. In spite of this, some very useful results can be obtained. Some key features of SKIPSM, as applied to grey-level morphology, are S There is a tradeoffbetween structuring element (SE) size and number of grey levels. . The SEs can be arbitrary . With currently-available components, SEa up to 5x5 and larger can be obtained. S Jfl certain special cases, SEs up to 9x9 and larger can be obtained. . Multiple SEs can be applied simultaneously in a single pipeline pass. S The user specifies the SE or SEs. All other steps can be automated. This paper includes some simple examples of the results and gives implementation feasibility guidelines based on SE size and number of grey levels. The limitations of SKIPSM in this application all relate to the capabilities of the available RAM microchips. As chip capabilities expand, larger SE sizes and greater numbers of grey levels will become feasible. KEYWORDS: image processing, separability, real time, implementations, finite-state machines, grey-level morphology

A design for a chip to perform binary and binary morphological operations is provided. The chip is intended to be a part of a pipeline machine controlled by a general purpose processor. It could process 25512/spl times/512 pixel frames per second in one pixel per clock cycle rate. In real time application an array of this chip could be used, leading to a less expensive design than using an array of general DSP chips. The functionality of the chip is programmable by changing the flow of data between the internal units using a multiplexer and demultiplexer set. A 15-bit control register is used to set the flow path and consequently the operation.

A vast number of historical and badly degraded document images can be found in libraries, public, and national archives. Due to the complex nature of different artifacts, such poor quality documents are hard to read and to process. In this paper, a novel adaptive binarization algorithm using ternary entropy-based approach is proposed. Given an input image, the contrast of intensity is first estimated by a grayscale morphological closing operator. A double-threshold is generated by our Shannon entropy-based ternarizing method to classify pixels into text, near-text, and non-text regions. The pixels in the second region are relabeled by the local mean and the standard deviation. Our proposed method classifies noise into two categories which are processed by binary morphological operators, shrink and swell filters, and graph searching strategy. The method is tested with three databases that have been used in the Document Image Binarization Contest 2009 (DIBCO 2009), the Handwriting Document Image Binarization Contest 2010 (H-DBCIO 2010), and the International Conference on Frontier in Handwriting Recognition 2010 (ICFHR 2010). The evaluation is based upon nine distinct measures. Experimental results show that our proposed algorithm outperforms other state-of-the-art methods.

The topic of colour image segmentation has been and still is a hot issue in areas such as computer vision and image processing because of its wide range of practical applications. The urge has led to the development of numerous colour image segmentation algorithms to extract salient objects from colour images. However, because of the diverse imaging conditions in varying application domains, accuracy and robustness of several state-of-the-art colour image segmentation algorithms still leave room for further improvement. This dissertation reports on the development of a new image segmentation algorithm based on perceptual colour difference saliency along with binary morphological operations. The algorithm consists of four essential processing stages which are colour image transformation, luminance image enhancement, salient pixel computation and image artefact filtering. The input RGB colour image is first transformed into the CIE L*a*b colour image to achieve perceptual saliency and obtain the best possible calibration of the transformation model. The luminance channel of the transformed colour image is then enhanced using an adaptive gamma correction function to alleviate the adverse effects of illumination variation, low contrast and improve the image quality significantly. The salient objects in the input colour image are then determined by calculating saliency at each pixel in order to preserve spatial information. The computed saliency map is then filtered using the morphological operations to eliminate undesired factors that are likely present in the colour image. A series of experiments was performed to evaluate the effectiveness of the new perceptual colour difference saliency algorithm for colour image segmentation. This was accomplished by testing the algorithm on a large set of a hundred and ninety images acquired from four distinct publicly available benchmarks corporal. The accuracy of the developed colour image segmentation algorithm was quantified using four widely used statistical evaluation metrics in terms of precision, F-measure, error and Dice. Promising results were obtained despite the fact that the experimental images were selected from four different corporal and in varying imaging conditions. The results have indeed demonstrated that the performance of the newly developed colour image segmentation algorithm is consistent with an improved performance compared to a number of other saliency and non- saliency state-of-the-art image segmentation algorithms.

The mathematical morphology was applied to high-speed automaton fault diagnosis in this paper, which a new method was proposed to diagnosis high-speed automaton fault based on mathematical morphology and the combination of fuzzy clustering. Take a high-speed automaton fault data in laboratory tests for example; various types were analyzed for the fault signal. The results demonstrated that the combination of mathematical morphology and fuzzy clustering applied on a high-speed automaton can effectively diagnose faults, providing a new way for high-speed automata fault diagnosis.

Mathematical morphology on l-images

Morphological operations are commonly used tools for the extraction of regional features and shape information in pattern recognition and image understanding tasks. In the present paper, a general approach for low-cost hardware-realization of multidimensional binary morphological operations is proposed. The underlying definitions of Minkowski addition and Minkowski subtraction are reduced to linear n-D convolutions, which lead to several alternative implementations. The flexible architecture of modern FPGAs allows the construction of operations with fixed and variable sized structuring elements that handle regions of up to 1000 object elements (e.g. pixels) in parallel. This leads to low-cost hardware realizations of real-time computer vision tasks. Possible clock frequencies of more than 100 MHz provide morphological image processing for camera resolution of 320/spl times/240 pixel at frame rates of over 1000 Hz.