New fuzzy model for morphological colour image processing

A nonlocal operator model for morphological image processing is proposed in this paper. Compared with the previous nonlocal morphological operators, the derivations of the proposed model are easier to inherit useful properties and physical interpretations from the traditional morphology. Two important properties of the model have been clarified in theory. And for instances, based on the model, two basic morphological operators (i.e., erosion and dilatation) are developed with convincing experimental results. In addition, the generality and usability of the model are also discussed.

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.

Mathematical morphology has been used extensively to process binary and grayscale images. The use of mathematical morphology to process color images, however, is quite limited due to technical problems with ordering vector quantities. This paper describes three morphological techniques for processing color images that have appeared in the literature, in addition to presenting a new technique based on vector projections. The usefulness of these techniques is then illustrated on the task of extracting an object from a color image. Techniques for morphologically processing color images can be also applied to images and signals containing complex data.

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.

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 method for processing microfocus X-ray images is described. It is based on high-frequency filtration and morphological image processing, which increases the contrast of the X-ray details. One of the most informative X-ray techniques is microfocus X-ray. In some cases, microfocus X-ray images cannot be reliably analyzed due to the peculiarities of the shooting method. So, the main disadvantages of microfocus X-ray images are most often an uneven background, distorted brightness characteristics and the presence of noise. The proposed method for enhancing the contrast of fine image details is based on the idea of combining high-frequency filtering and morphological image processing. The method consists of the following steps: noise suppression in the image, high-frequency filtering, morphological image processing, obtaining the resulting image. As a result of applying the method, the brightness of the contours in the image is enhanced. In the resulting image, all objects will have double outlines. The method was tested in the processing of 50 chest radiographs of patients with various pathologies. Radiographs were performed at the Mariinsky Hospital of St. Petersburg using digital stationary and mobile X-ray machines. In most of the radiographs, it was possible to improve the images contrast, to highlight the objects boundaries. Besides, the method was applied in microfocus X-ray tomography to improve the information content of projection data and improve the reconstruction of the 3D image of the research object. In both the first and second cases, the method showed satisfactory results. The developed method makes it possible to significantly increase the information content of microfocus X-ray images. The obtained practical results make it possible to count on broad prospects for the method application, especially in microfocus X-ray.

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.

ABSTRACTThe quaternion Fourier transform has been widely employed in the colour image processing. The use of quaternions allow the analysis of colour images as vector fields. In this paper, the right-sided quaternion Fourier transform and its properties are reviewed. Using the polar form of quaternions, two novel uncertainty principles associated with covariance are established. They prescribe the lower bounds with covariances on the products of the effective widths of quaternionic signals in the space and frequency domains. The results generalize the Heisenberg's uncertainty principle to the 2D quaternionic space.

Multivariate mathematical morphology is increasing becoming a powerful utility for color image processing and analysis. In the paper, a new vector ordering scheme based on distances in the HSI color space is proposed. The methodology first uses nonlinear weighted combination with color distances, where vectors are reduced to scalars with the help of weighted coefficients. These coefficients are entirely determined by the saturation-intensity plane, which fit in with human perception. Then a lexicographical cascade is employed to complete total ordering. On the basis of the ordering approach, new erosion and dilation operations are defined. The performance of proposed is illustrated by means of color edge detection. Experimental results demonstrate its advantageous effectiveness. Additionally, the proposed framework can be also extended for other perceptual color representations based on polar coordinate.

Morphological image analysis is a technique of processing images through shape characteristics (Jain 1989). Because images are regular data structures, morphology algorithm's memory access patterns are predictable. By using read and write patterns, we derive a model of processing to examine inefficiencies in cache processing. We then develop a cache architecture for windowed processing that reduces cache thrashing. Our caching technique, cache tiling, improves efficiency dramatically for small caches independent of compiler optimizations. Programs are not affected, providing a transparent solution to improve caching. A system code, compilers, or profiling programs can determine the blocking necessary for the best performance. An analytical model for morphological processing's memory characteristics is presented that provides for exact cache analysis and prediction. The analytical model is compared to address traces to validate the model. Other algorithms such as inner product, matrix multiplication, and convolution also benefit from the architecture presented herein.

The use of color image processing is divided by two factors. First color is used in object identification and simplify extraction from a scene and color is powerful descriptor.Second, humans can used thousands of color shades and intensities.Color Image Processing is divided into two areas full color and pseudocolor processing.In this processing various color models are used that are based on color recognition,color components etc. A few papers on various applications such as lane detection, face detection, fruit quality evaluation etc based on these color models have been published. A survey on widely used models RGB,HSI, HSV, RGI etc is represented in this paper.

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.

Determining holes and connectivity in binary images

In this paper, a series of processes are performed to assess the degree of infection in a subject’s blood infected with malaria. This process improves the existing testing method’s inefficiencies to assess the results obtained of the infected blood. Image processing algorithm was constructed to establish a standardized test procedure as a way to process the information. The algorithm is built to process the images obtained from the microscopic image of the infected blood and detect the extent of Plasmodium infection which is the cause of Malaria Disease. The images obtained from the infected red blood cells and uninfected red blood cells are processed using the image processing algorithm to distinguish the number of cells infected by malaria in the infected blood. A quantitative analysis is carried out using the results to evaluate the extent of the infection in the blood cells. To establish the relationship between the number of infected and uninfected cells, the algorithm was constructed based on color image processing through HSV color space and morphological image processing techniques. The key configuration for the proposed algorithm is the morphological image processing technique which clearly divide the red blood cells in the micrograph based on the extent of penetration of plasmodium in the blood cells using the clamp splitting method and categorize the infected blood cells.