Implementation of a high-performance hardware architecture for binary morphological image processing operations

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.

A new type of nonlinear image processor based on morphological processing is demonstrated. Morphological image processing is based on the successive binary convolution of an image with a selectable convolution kernel. Its field of application includes numerical optical computing and binary image processing. Simple serial commands from a small electronic computer control the selection of the convolution kernels for a given iteration and consequently the parallel processing of very large optical data planes. 1.

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 .

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.

In this paper, novel binary adaptive neighborhood morphological operators are proposed to solve the problem that the fixed structure elements cannot adapt to the whole image when processing images in classical mathematical morphology. The key of adaptive morphology is to select suitable structure elements according to the different attributes of an image. On the basis of image local features and pixel position information, the structure elements selected in this paper can be adaptively adjusted. Besides, the new structure elements have symmetry, namely they are symmetrical about the center point. New binary adaptive neighborhood morphological operators are also defined according to the new structure elements. Comparing with the experimental results of the classical morphological operations, the new operators can retain the details of the image better and be more similar to the original image, which shows the new operators is more effective and practical.

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.

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.

A developed, binary, image-processing technique is proposed, and a dual-channel, optical, real-time morphological processor is developed. Nine binary image processings can be realized fully in parallel. The measures for compensating scale and rotation distortion for pattern recognition are provided. Some applications of optical, morphological binary image processing are studied and experimental results are listed.

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.

Mathematical morphology is a powerful tool for image processing and analysis of binary, greyscale and colour images. In the paper, an efficient new fuzzy model for morphological colour image processing is introduced. A new vector-ordering scheme that uses fuzzy if–then rules is proposed, and then the basic morphological operations of erosion and dilation are defined. The morphological operators presented, which are vector preserving, are less sensitive to image distortion and perform significantly better in noise removal problems than other reported morphological operators. Experimental results demonstrate these advantageous characteristics on real images. Additionally, illustrative examples that exhibit the applicability of the proposed framework to edge-detection problems are given.

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.

A hit-or-miss transform architecture and a canonical Boolean logic format and threshold decomposition algorithm are proposed to process binary and gray-tone images with morphological combined operations. The transformation of morphological combined image processing functions to hit-or-miss transforms is explicit: Binary functions are formulated by a summation of hit-or-miss transforms and gray-tone functions formulated by a double summation. A simple optical hit-or-miss transform processor is suggested on the basis of incoherent two-channel correlation with dual-rail spatial coding. The hit-or-miss transforms can be realized optically in one parallel processing step. Thus, binary and gray-tone image processing functions can be implemented by the repeated use of the optical processor with different structuring elements.

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.

When considering ways to automate the generation of image processing algorithms for object recognition tasks, one critical element is the availability of measures to assess the potential and actual ability of individual operations for making a set of desired discriminations. This paper discusses the analysis and evaluation of grey-level image processing operators or algorithms from the perspective of trying to search automatically through a large space of them for one which satisfactorily performs a given recognition or discrimination task. Performance of an operator may be expressed in terms of accuracy, consistency, and cost, over an entire set of training images.The major issues of evaluating and choosing between operators in this context are discussed, and examples are given of measures which can be used to evaluate classes of operators for applicability, or to evaluate individual operators or parameter settings for actual performance. The paper first describes the form of the analysis for binary morphological operators, and then shows how it may be directly extended to grey-level morphological operators. Several examples are provided to show how grey-level pixel sets may be discriminated on the basis of various combinations of grey level and spatial criteria, as calculated by the basic morphological operators of erosion, dilation, opening, and closing.

The goal of the optic nerve stimulation is to place multielectrode electrodes around the optic nerve and evoke multiplephosphenes covering the entire visual space by using complex patterns of electrical stimulation. This paper mainly studies the best image processing strategies applying to the optic nerve stimulation. Four image processing strategies that could be used in the optic nerve stimulation such as histogram equalization, edge detection, edge following, binary morphological operations. As we learn more about the neural coding of visual system, the better image processing strategies will be applied to the artificial vision.