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  • Open Access

    ARTICLE

    Characterization and Prediction of Nonlinear Stress-Strain Relation of Geostructures for Seismic Monitoring

    Abdoullah Namdar1,2,3,*

    Structural Durability & Health Monitoring, Vol.15, No.2, pp. 167-182, 2021, DOI:10.32604/sdhm.2021.011127

    Abstract The nonlinearity of the strain energy at an interval period of applying seismic load on the geostructures makes it difficult for a seismic designer to makes appropriate engineering judgments timely. The nonlinear stress and strain analysis of an embankment is needed to evaluate by using a combination of suitable methods. In this study, a large-scale geostructure was seismically simulated and analyzed using the nonlinear finite element method (NFEM), and linear regression method which is a soft computing technique (SC) was applied for evaluating the results of NFEM, and it supports engineering judgment because the design of the geostructures is usually… More >

  • Open Access

    ARTICLE

    Pashto Characters Recognition Using Multi-Class Enabled Support Vector Machine

    Sulaiman Khan1, Shah Nazir1, Habib Ullah Khan2,*, Anwar Hussain1

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2831-2844, 2021, DOI:10.32604/cmc.2021.015054

    Abstract During the last two decades significant work has been reported in the field of cursive language’s recognition especially, in the Arabic, the Urdu and the Persian languages. The unavailability of such work in the Pashto language is because of: the absence of a standard database and of significant research work that ultimately acts as a big barrier for the research community. The slight change in the Pashto characters’ shape is an additional challenge for researchers. This paper presents an efficient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction techniques. These… More >

  • Open Access

    ARTICLE

    Novel Adaptive Binarization Method for Degraded Document Images

    Siti Norul Huda Sheikh Abdullah1, Saad M. Ismail1,2, Mohammad Kamrul Hasan1,*, Palaiahnakote Shivakumara3

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3815-3832, 2021, DOI:10.32604/cmc.2021.014610

    Abstract Achieving a good recognition rate for degraded document images is difficult as degraded document images suffer from low contrast, bleed-through, and nonuniform illumination effects. Unlike the existing baseline thresholding techniques that use fixed thresholds and windows, the proposed method introduces a concept for obtaining dynamic windows according to the image content to achieve better binarization. To enhance a low-contrast image, we proposed a new mean histogram stretching method for suppressing noisy pixels in the background and, simultaneously, increasing pixel contrast at edges or near edges, which results in an enhanced image. For the enhanced image, we propose a new method… More >

  • Open Access

    ARTICLE

    Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images

    Rabia Javed1,2, Mohd Shafry Mohd Rahim1, Tanzila Saba3, Suliman Mohamed Fati3, Amjad Rehman3,*, Usman Tariq4

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2337-2352, 2021, DOI:10.32604/cmc.2021.014677

    Abstract Most of the melanoma cases of skin cancer are the life-threatening form of cancer. It is prevalent among the Caucasian group of people due to their light skin tone. Melanoma is the second most common cancer that hits the age group of 15–29 years. The high number of cases has increased the importance of automated systems for diagnosing. The diagnosis should be fast and accurate for the early treatment of melanoma. It should remove the need for biopsies and provide stable diagnostic results. Automation requires large quantities of images. Skin lesion datasets contain various kinds of dermoscopic images for the… More >

  • Open Access

    ARTICLE

    OPPR: An Outsourcing Privacy-Preserving JPEG Image Retrieval Scheme with Local Histograms in Cloud Environment

    Jian Tang, Zhihua Xia*, Lan Wang, Chengsheng Yuan, Xueli Zhao

    Journal on Big Data, Vol.3, No.1, pp. 21-33, 2021, DOI:10.32604/jbd.2021.015892

    Abstract As the wide application of imaging technology, the number of big image data which may containing private information is growing fast. Due to insufficient computing power and storage space for local server device, many people hand over these images to cloud servers for management. But actually, it is unsafe to store the images to the cloud, so encryption becomes a necessary step before uploading to reduce the risk of privacy leakage. However, it is not conducive to the efficient application of image, especially in the Content-Based Image Retrieval (CBIR) scheme. This paper proposes an outsourcing privacypreserving JPEG CBIR scheme. We… More >

  • Open Access

    ARTICLE

    The Implementation of Optimization Methods for Contrast Enhancement

    Ahmet Elbir1,∗, Hamza Osman Ilhan1, Nizamettin Aydin1

    Computer Systems Science and Engineering, Vol.34, No.2, pp. 101-107, 2019, DOI:10.32604/csse.2019.34.101

    Abstract The performances of the multivariate techniques are directly related to the variable selection process, which is time consuming and requires resources for testing each possible parameter to achieve the best results. Therefore, optimization methods for variable selection process have been proposed in the literature to find the optimal solution in short time by using less system resources. Contrast enhancement is the one of the most important and the parameter dependent image enhancement technique. In this study, two optimization methods are employed for the variable selection for the contrast enhancement technique. Particle swarm optimization (PSO) and artificial bee colony (ABC) optimization… More >

  • Open Access

    ARTICLE

    Tumor Classfication UsingG Automatic Multi-thresholding

    Li-Hong Juanga, Ming-Ni Wub

    Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 257-266, 2018, DOI:10.1080/10798587.2016.1272778

    Abstract In this paper we explore these math approaches for medical image applications. The application of the proposed method for detection tumor will be able to distinguish exactly tumor size and region. In this research, some major design and experimental results of tumor objects detection method for medical brain images is developed to utilize an automatic multi-thresholding method to handle this problem by combining the histogram analysis and the Otsu clustering. The histogram evaluations can decide the superior number of clusters firstly. The Otsu classification algorithm solves the given medical image by continuously separating the input gray-level image by multi-thresholding until… More >

  • Open Access

    ARTICLE

    Ultrasound Speckle Reduction Based on Histogram Curve Matching and Region Growing

    Jinrong Hu1, Zhiqin Lei1, Xiaoying Li2, *, Yongqun He3, Jiliu Zhou1

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 705-722, 2020, DOI:10.32604/cmc.2020.09878

    Abstract The quality of ultrasound scanning images is usually damaged by speckle noise. This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing algorithm. Unlike single statistical moment-based speckle reduction algorithms, this method adaptively smooths the speckle regions while preserving the margin and tissue structure to achieve high detectability. The criterion of a speckle region is defined by the similarity value obtained by matching the histogram of the current processing window and the reference window derived from the speckle region in advance. Then, according to the similarity value and tissue… More >

  • Open Access

    ARTICLE

    An Effective Steganalysis Algorithm for Histogram-Shifting Based Reversible Data Hiding

    Junxiang Wang1, *, Lin Huang1, Ying Zhang1, Yonghong Zhu1, Jiangqun Ni2, Yunqing Shi3

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 325-344, 2020, DOI:10.32604/cmc.2020.09784

    Abstract To measure the security for hot searched reversible data hiding (RDH) technique, especially for the common-used histogram-shifting based RDH (denoted as HS-RDH), several steganalysis schemes are designed to detect whether some secret data has been hidden in a normal-looking image. However, conventional steganalysis schemes focused on the previous RDH algorithms, i.e., some early spatial/pixel domain-based histogram-shifting (HS) schemes, which might cause great changes in statistical characteristics and thus be easy to be detected. For recent improved methods, such as some adaptive prediction error (PE) based embedding schemes, those conventional schemes might be invalid, since those adaptive embedding mechanism would effectively… More >

  • Open Access

    ARTICLE

    A Multi-Level Threshold Method for Edge Detection and Segmentation Based on Entropy

    Mohamed A. El-Sayed1, *, Abdelmgeid A. Ali2, Mohamed E. Hussien3, Hameda A. Sennary3

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 1-16, 2020, DOI:10.32604/cmc.2020.08444

    Abstract The essential tool in image processing, computer vision and machine vision is edge detection, especially in the fields of feature extraction and feature detection. Entropy is a basic area in information theory. The entropy, in image processing field has a role associated with image settings. As an initial step in image processing, the entropy is always used the image’s segmentation to determine the regions of image which is used to separate the background and objects in image. Image segmentation known as the process which divides the image into multiple regions or sets of pixels. Many applications have been development to… More >

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