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  • Open Access

    ARTICLE

    Image Inpainting Detection Based on High-Pass Filter Attention Network

    Can Xiao1,2, Feng Li1,2,*, Dengyong Zhang1,2, Pu Huang1,2, Xiangling Ding3, Victor S. Sheng4

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1145-1154, 2022, DOI:10.32604/csse.2022.027249

    Abstract Image inpainting based on deep learning has been greatly improved. The original purpose of image inpainting was to repair some broken photos, such as inpainting artifacts. However, it may also be used for malicious operations, such as destroying evidence. Therefore, detection and localization of image inpainting operations are essential. Recent research shows that high-pass filtering full convolutional network (HPFCN) is applied to image inpainting detection and achieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, we introduce the squeezed excitation blocks (SE) and propose a high-pass… More >

  • Open Access

    ARTICLE

    Research on the Identification of Hand-Painted and Machine-Printed Thangka Using CBIR

    Chunhua Pan1,*, Yi Cao2, Jinglong Ren3

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1081-1091, 2022, DOI:10.32604/iasc.2022.028763

    Abstract Thangka is a unique painting art form in Tibetan culture. As Thangka was awarded as the first batch of national intangible cultural heritage, it has been brought into focus. Unfortunately, illegal merchants sell fake Thangkas at high prices for profit. Therefore, identifying hand-painted Thangkas from machine-printed fake Thangkas is important for protecting national intangible cultural heritage. The paper uses Content-Based Image Retrieval (CBIR) techniques to analyze the color, shape, texture, and other characteristics of hand-painted and machine- printed Thangka images, in order to identify Thangkas. Based on the database collected and established by this project team, we use Local Binary… More >

  • Open Access

    ARTICLE

    An Efficient Video Inpainting Approach Using Deep Belief Network

    M. Nuthal Srinivasan1,*, M. Chinnadurai2

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 515-529, 2022, DOI:10.32604/csse.2022.023109

    Abstract The video inpainting process helps in several video editing and restoration processes like unwanted object removal, scratch or damage rebuilding, and retargeting. It intends to fill spatio-temporal holes with reasonable content in the video. Inspite of the recent advancements of deep learning for image inpainting, it is challenging to outspread the techniques into the videos owing to the extra time dimensions. In this view, this paper presents an efficient video inpainting approach using beetle antenna search with deep belief network (VIA-BASDBN). The proposed VIA-BASDBN technique initially converts the videos into a set of frames and they are again split into… More >

  • Open Access

    ARTICLE

    Spectral Matching Classification Method of Multi-State Similar Pigments Based on Feature Differences

    Meng Da1, Huiqin Wang1,*, Ke Wang1, Zhan Wang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 513-527, 2022, DOI:10.32604/cmes.2022.019040

    Abstract The properties of the same pigments in murals are affected by different concentrations and particle diameters, which cause the shape of the spectral reflectance data curve to vary, thus influencing the outcome of matching calculations. This paper proposes a spectral matching classification method of multi-state similar pigments based on feature differences. Fast principal component analysis (FPCA) was used to calculate the eigenvalue variance of pigment spectral reflectance, then applied to the original reflectance values for parameter characterization. We first projected the original spectral reflectance from the spectral space to the characteristic variance space to identify the spectral curve. Secondly, the… More >

  • Open Access

    ARTICLE

    Perceptual Image Outpainting Assisted by Low-Level Feature Fusion and Multi-Patch Discriminator

    Xiaojie Li1, Yongpeng Ren1, Hongping Ren1, Canghong Shi2, Xian Zhang1, Lutao Wang1, Imran Mumtaz3, Xi Wu1

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5021-5037, 2022, DOI:10.32604/cmc.2022.023071

    Abstract Recently, deep learning-based image outpainting has made greatly notable improvements in computer vision field. However, due to the lack of fully extracting image information, the existing methods often generate unnatural and blurry outpainting results in most cases. To solve this issue, we propose a perceptual image outpainting method, which effectively takes the advantage of low-level feature fusion and multi-patch discriminator. Specifically, we first fuse the texture information in the low-level feature map of encoder, and simultaneously incorporate these aggregated features reusability with semantic (or structural) information of deep feature map such that we could utilize more sophisticated texture information to… More >

  • Open Access

    ARTICLE

    Stroke Based Painterly Rendering with Mass Data through Auto Warping Generation

    Taemin Lee1, Beomsik Kim2, Sanghyun Seo3, Kyunghyun Yoon4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1441-1457, 2022, DOI:10.32604/cmes.2022.018010

    Abstract Painting is done according to the artist's style. The most representative of the style is the texture and shape of the brush stroke. Computer simulations allow the artist's painting to be produced by taking this stroke and pasting it onto the image. This is called stroke-based rendering. The quality of the result depends on the number or quality of this stroke, since the stroke is taken to create the image. It is not easy to render using a large amount of information, as there is a limit to having a stroke scanned. In this work, we intend to produce rendering… More >

  • Open Access

    ARTICLE

    Uncertainties in the Mercury Mass Balance in a Coal-Based IGCC Power Plant (Puertollano, Spain)

    José María Esbrí*, Alba Martinez-Coronado, Sofía Rivera Jurado, Eva García-Noguero, Pablo Higueras

    Energy Engineering, Vol.118, No.4, pp. 1223-1235, 2021, DOI:10.32604/EE.2021.015781

    Abstract Mercury (Hg) is a global pollutant that is subject to strict regulations to reduce anthropogenic emissions. The production of energy represents an important activity that leads to Hg emissions into the atmosphere. Of all the systems used, IGCC plants are the most promising for reducing Hg emissions, since it is possible to remove Hg from syngas prior to combustion. The aim of the present work was to evaluate the presence of Hg in the main streams of an experimental IGCC plant (ELCOGAS, Puertollano) in order to quantify Hg emissions and investigate the possibility of reducing them. The main streams of… More >

  • Open Access

    ARTICLE

    UFC-Net with Fully-Connected Layers and Hadamard Identity Skip Connection for Image Inpainting

    Chung-Il Kim1, Jehyeok Rew2, Yongjang Cho2, Eenjun Hwang2,*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3447-3463, 2021, DOI:10.32604/cmc.2021.017633

    Abstract Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas. Although its performance has been improved significantly using diverse convolutional neural network (CNN)-based models, these models have difficulty filling in some erased areas due to the kernel size of the CNN. If the kernel size is too narrow for the blank area, the models cannot consider the entire surrounding area, only partial areas or none at all. This issue leads to typical problems of inpainting, such as pixel reconstruction failure and unintended filling.… More >

  • Open Access

    ARTICLE

    A Machine Learning Approach for Expression Detection in Healthcare Monitoring Systems

    Muhammad Kashif1, Ayyaz Hussain2, Asim Munir1, Abdul Basit Siddiqui3, Aaqif Afzaal Abbasi4, Muhammad Aakif5, Arif Jamal Malik4, Fayez Eid Alazemi6, Oh-Young Song7,*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2123-2139, 2021, DOI:10.32604/cmc.2021.014782

    Abstract Expression detection plays a vital role to determine the patient’s condition in healthcare systems. It helps the monitoring teams to respond swiftly in case of emergency. Due to the lack of suitable methods, results are often compromised in an unconstrained environment because of pose, scale, occlusion and illumination variations in the image of the face of the patient. A novel patch-based multiple local binary patterns (LBP) feature extraction technique is proposed for analyzing human behavior using facial expression recognition. It consists of three-patch [TPLBP] and four-patch LBPs [FPLBP] based feature engineering respectively. Image representation is encoded from local patch statistics… More >

  • Open Access

    ARTICLE

    SEIHCRD Model for COVID-19 Spread Scenarios, Disease Predictions and Estimates the Basic Reproduction Number, Case Fatality Rate, Hospital, and ICU Beds Requirement

    Avaneesh Singh*, Manish Kumar Bajpai

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 991-1031, 2020, DOI:10.32604/cmes.2020.012503

    Abstract We have proposed a new mathematical method, the SEIHCRD model, which has an excellent potential to predict the incidence of COVID-19 diseases. Our proposed SEIHCRD model is an extension of the SEIR model. Three-compartments have added death, hospitalized, and critical, which improves the basic understanding of disease spread and results. We have studied COVID-19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. After estimating model parameters based on available clinical data, the model will propagate and forecast dynamic evolution. The model calculates the… More >

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