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  • Open Access

    ARTICLE

    An Efficient Detection Approach of Content Aware Image Resizing

    Ming Lu1, 2, *, Shaozhang Niu1, Zhenguang Gao3

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 887-907, 2020, DOI:10.32604/cmc.2020.09770

    Abstract Content aware image resizing (CAIR) is an excellent technology used widely for image retarget. It can also be used to tamper with images and bring the trust crisis of image content to the public. Once an image is processed by CAIR, the correlation of local neighborhood pixels will be destructive. Although local binary patterns (LBP) can effectively describe the local texture, it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise. Therefore, to deal with the detection of CAIR, a novel forensic method based on improved local ternary patterns (ILTP) feature and gradient… More >

  • Open Access

    ARTICLE

    Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

    A. Renugambal1, *, K. Selva Bhuvaneswari2

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 681-700, 2020, DOI:10.32604/cmc.2020.09519

    Abstract In this study, a novel hybrid Water Cycle Moth-Flame Optimization (WCMFO) algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance (MR) image slices. WCMFO constitutes a hybrid between the two techniques, comprising the water cycle and moth-flame optimization algorithms. The optimal thresholds are obtained by maximizing the between class variance (Otsu’s function) of the image. To test the performance of threshold searching process, the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation. The experimental outcomes infer that it produces better optimal threshold values at a greater and… More >

  • Open Access

    ARTICLE

    Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks

    Duo Ma1, 2, 3, Hongyuan Fang1, 2, 3, *, Binghan Xue1, 2, 3, Fuming Wang1, 2, 3, Mohammed A. Msekh4, Chiu Ling Chan5

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1267-1291, 2020, DOI:10.32604/cmes.2020.09122

    Abstract The crack is a common pavement failure problem. A lack of periodic maintenance will result in extending the cracks and damage the pavement, which will affect the normal use of the road. Therefore, it is significant to establish an efficient intelligent identification model for pavement cracks. The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix. It has been widely used in geotechnical engineering, computer vision, medicine, and other fields. However, there are three major problems in the application of neural networks to crack identification. There are too… More >

  • Open Access

    ARTICLE

    Fast Single Image Haze Removal Method for Inhomogeneous Environment Using Variable Scattering Coefficient

    Rashmi Gupta1, Manju Khari1, Vipul Gupta1, Elena Verdú2, Xing Wu3, Enrique Herrera-Viedma4, Rubén González Crespo2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1175-1192, 2020, DOI:10.32604/cmes.2020.010092

    Abstract The images capture in a bad environment usually loses its fidelity and contrast. As the light rays travel towards its destination they get scattered several times due to the tiny particles of fog and pollutants in the environment, therefore the energy gets lost due to multiple scattering till it arrives its destination, and this degrades the images. So the images taken in bad weather appear in bad quality. Therefore, single image haze removal is quite a bit tough task. Significant research has been done in the haze removal algorithm but in all the techniques, the coefficient of scattering is taken… More >

  • Open Access

    ARTICLE

    Knowledge-based reconstruction for measurement of right ventricular volumes on cardiovascular magnetic resonance images in a mixed population

    Elise D. Pieterman1,2, Ricardo P. J. Budde2, Danielle Robbers-Visser1,2, Ron T. van Domburg3, Willem A. Helbing1,2

    Congenital Heart Disease, Vol.12, No.5, pp. 561-569, 2017, DOI:10.1111/chd.12484

    Abstract Objective: Follow-up of right ventricular performance is important for patients with congenital heart disease. Cardiac magnetic resonance imaging is optimal for this purpose. However, observerdependency of manual analysis of right ventricular volumes limit its use. Knowledge-based reconstruction is a new semiautomatic analysis tool that uses a database including knowledge of right ventricular shape in various congenital heart diseases. We evaluated whether knowledge-based reconstruction is a good alternative for conventional analysis.
    Design: To assess the inter- and intra-observer variability and agreement of knowledge-based versus conventional analysis of magnetic resonance right ventricular volumes, analysis was done by two observers in a mixed… More >

  • Open Access

    ARTICLE

    Personalized News Recommendation Based on the Text and Image Integration

    Kehua Yang1, *, Shaosong Long1, Wei Zhang1, Jiqing Yao2, Jing Liu1

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 557-570, 2020, DOI:10.32604/cmc.2020.09907

    Abstract The personalized news recommendation has been very popular in the news recommendation field. In most research, the picture information in the news is ignored, but the information conveyed to the users through pictures is more intuitive and more likely to affect the users’ reading interests than the one in the textual form. Therefore, in this paper, a model that combines images and texts in the news is proposed. In this model, the new tags are extracted from the images and texts in the news, and based on these new tags, an adaptive tag (AT) algorithm is proposed. The AT algorithm… More >

  • Open Access

    ARTICLE

    A New Sequential Image Prediction Method Based on LSTM and DCGAN

    Wei Fang1, 2, Feihong Zhang1, *, Yewen Ding1, Jack Sheng3

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 217-231, 2020, DOI:10.32604/cmc.2020.06395

    Abstract Image recognition technology is an important field of artificial intelligence. Combined with the development of machine learning technology in recent years, it has great researches value and commercial value. As a matter of fact, a single recognition function can no longer meet people’s needs, and accurate image prediction is the trend that people pursue. This paper is based on Long Short-Term Memory (LSTM) and Deep Convolution Generative Adversarial Networks (DCGAN), studies and implements a prediction model by using radar image data. We adopt a stack cascading strategy in designing network connection which can control of parameter convergence better. This new… More >

  • Open Access

    ARTICLE

    A Novel Approach of Image Steganography for Secure Communication Based on LSB Substitution Technique

    Shahid Rahman1, Fahad Masood2, Wajid Ullah Khan2, Niamat Ullah1, Fazal Qudus Khan3, Georgios Tsaramirsis3, Sadeeq Jan4, *, Majid Ashraf5

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 31-61, 2020, DOI:10.32604/cmc.2020.09186

    Abstract Steganography aims to hide the messages from unauthorized persons for various purposes, e.g., military correspondence, financial transaction data. Securing the data during transmission is of utmost importance these days. The confidentiality, integrity, and availability of the data are at risk because of the emerging technologies and complexity in software applications, and therefore, there is a need to secure such systems and data. There are various methodologies to deal with security issues when utilizing an open system like the Internet. This research proposes a new technique in steganography within RGB shading space to achieve enhanced security compared with existing systems. We… More >

  • Open Access

    ARTICLE

    Visual Relationship Detection with Contextual Information

    Yugang Li1, 2, *, Yongbin Wang1, Zhe Chen2, Yuting Zhu3

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1575-1589, 2020, DOI:10.32604/cmc.2020.07451

    Abstract Understanding an image goes beyond recognizing and locating the objects in it, the relationships between objects also very important in image understanding. Most previous methods have focused on recognizing local predictions of the relationships. But real-world image relationships often determined by the surrounding objects and other contextual information. In this work, we employ this insight to propose a novel framework to deal with the problem of visual relationship detection. The core of the framework is a relationship inference network, which is a recurrent structure designed for combining the global contextual information of the object to infer the relationship of the… More >

  • Open Access

    ARTICLE

    High Resolution SAR Image Algorithm with Sample Length Constraints for the Range Direction

    Zhenli Wang1, *, Qun Wang1, Fujuan Li1, Shuai Wang2

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1533-1543, 2020, DOI:10.32604/cmc.2020.09721

    Abstract The traditional Range Doppler (RD) algorithm is unable to meet practical needs owing to the limit of resolution. The order of fractional Fourier Transform (FrFT) and the length of sampling signals affect SAR imaging performance when FrFT is applied to RD algorithm. To overcome the above shortcomings, the purpose of this paper is to propose a high-resolution SAR image algorithm by using the optimal order of FrFT and the sample length constraints for the range direction. The expression of the optimal order of SAR range signals via FrFT is deduced in detail. The initial sample length and its constraints are… More >

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