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  • Open Access


    Clustering Reference Images Based on Covisibility for Visual Localization

    Sangyun Lee1, Junekoo Kang2, Hyunki Hong2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2705-2725, 2023, DOI:10.32604/cmc.2023.034136

    Abstract In feature-based visual localization for small-scale scenes, local descriptors are used to estimate the camera pose of a query image. For large and ambiguous environments, learning-based hierarchical networks that employ local as well as global descriptors to reduce the search space of database images into a smaller set of reference views have been introduced. However, since global descriptors are generated using visual features, reference images with some of these features may be erroneously selected. In order to address this limitation, this paper proposes two clustering methods based on how often features appear as well as their covisibility. For both approaches,… More >

  • Open Access


    Multi-Scale Blind Image Quality Predictor Based on Pyramidal Convolution

    Feng Yuan, Xiao Shao*

    Journal on Big Data, Vol.2, No.4, pp. 167-176, 2020, DOI:10.32604/jbd.2020.015357

    Abstract Traditional image quality assessment methods use the hand-crafted features to predict the image quality score, which cannot perform well in many scenes. Since deep learning promotes the development of many computer vision tasks, many IQA methods start to utilize the deep convolutional neural networks (CNN) for IQA task. In this paper, a CNN-based multi-scale blind image quality predictor is proposed to extract more effectivity multi-scale distortion features through the pyramidal convolution, which consists of two tasks: A distortion recognition task and a quality regression task. For the first task, image distortion type is obtained by the fully connected layer. For… More >

  • Open Access


    Highly Accurate Recognition of Handwritten Arabic Decimal Numbers Based on a Self-Organizing Maps Approach

    Amin Alqudah1,2, Hussein R. Al-Zoubi2, Mahmood A. Al-Khassaweneh2,3, Mohammed Al-Qodah1

    Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 493-505, 2018, DOI:10.31209/2018.100000005

    Abstract Handwritten numeral recognition is one of the most popular fields of research in automation because it is used in many applications. Indeed, automation has continually received substantial attention from researchers. Therefore, great efforts have been made to devise accurate recognition methods with high recognition ratios. In this paper, we propose a method for integrating the correlation coefficient with a Self-Organizing Maps (SOM)-based technique to recognize offline handwritten Arabic decimal digits. The simulation results show very high recognition rates compared with the rates achieved by other existing methods. More >

  • Open Access


    Towards No-Reference Image Quality Assessment Based on Multi-Scale Convolutional Neural Network

    Yao Ma1, Xibiao Cai1, *, Fuming Sun2

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 201-216, 2020, DOI:10.32604/cmes.2020.07867

    Abstract Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems. Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information. Actually, the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image. In light of this, we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network, which integrates both global information and local information of an image. We first adopt the image pyramid method to generate four scale… More >

  • Open Access


    Perceptual Gradient Similarity Deviation for Full Reference Image Quality Assessment

    Manyu Jin1, Tao Wang1, Zexuan Ji1,*, Xiaobo Shen2

    CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 501-515, 2018, DOI: 10.3970/cmc.2018.02371

    Abstract Perceptual image quality assessment (IQA) is one of the most indispensable yet challenging problems in image processing and computer vision. It is quite necessary to develop automatic and efficient approaches that can accurately predict perceptual image quality consistently with human subjective evaluation. To further improve the prediction accuracy for the distortion of color images, in this paper, we propose a novel effective and efficient IQA model, called perceptual gradient similarity deviation (PGSD). Based on the gradient magnitude similarity, we proposed a gradient direction selection method to automatically determine the pixel-wise perceptual gradient. The luminance and chrominance channels are both took… More >

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