19 research outputs found

    Color Image Enhancement via Combine Homomorphic Ratio and Histogram Equalization Approaches: Using Underwater Images as Illustrative Examples

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    The histogram is one of the important characteristics of grayscale images, and the histogram equalization is effective method of image enhancement. When processing color images in models, such as the RGB model, the histogram equalization can be applied for each color component and, then, a new color image is composed from processed components. This is a traditional way of processing color images, which does not preserve the existent relation or correlation between colors at each pixel. In this work, a new model of color image enhancement is proposed, by preserving the ratios of colors at all pixels after processing the image. This model is described for the color histogram equalization (HE) and examples of application on color images are given. Our preliminary results show that the application of the model with the HE can be effectively used for enhancing color images, including underwater images. Intensive computer simulations show that for single underwater image enhancement, the presented method increases the image contrast and brightness and indicates a good natural appearance and relatively genuine color

    Image Enhancement by Elliptic Discrete Fourier Transforms

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    This paper describes a method of enhancement of grayscale and color image in the frequency domain by the pair of two elliptic discrete Fourier transforms (EDFT). Unlike the traditional discrete Fourier transform (DFT), the EDFT is parameterized and the parameter defines ellipses (not circles) around which the input data are rotated. Methods of the traditional DFT are widely used in image enhancement, and the transform rotates data of images around the circles. The presented method of image enhancement proposes processing images on different set of ellipses for the direct and inverse transforms. Our preliminary experimental examples show effectiveness of the proposed method. The Illustrative examples of image enhancement are given

    The 1st International Conference on Computational Engineering and Intelligent Systems

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    Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system

    Extracting root system architecture from X-ray micro computed tomography images using visual tracking

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    X-ray micro computed tomography (µCT) is increasingly applied in plant biology as an imaging system that is valuable for the study of root development in soil, since it allows the three-dimensional and non-destructive visualisation of plant root systems. Variations in the X-ray attenuation values of root material and the overlap in measured intensity values between roots and soil caused by water and organic matter represent major challenges to the extraction of root system architecture. We propose a novel technique to recover root system information from X-ray CT data, using a strategy based on a visual tracking framework embedding a modiffed level set method that is evolved using the Jensen-Shannon divergence. The model-guided search arising from the visual tracking approach makes the method less sensitive to the natural ambiguity of X-ray attenuation values in the image data and thus allows a better extraction of the root system. The method is extended by mechanisms that account for plagiatropic response in roots as well as collision between root objects originating from different plants that are grown and interact within the same soil environment. Experimental results on monocot and dicot plants, grown in different soil textural types, show the ability of successfully extracting root system information. Various global root system traits are measured from the extracted data and compared to results obtained with alternative methods
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