Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1,370)
  • 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


    Text Detection and Classification from Low Quality Natural Images

    Ujala Yasmeen1, Jamal Hussain Shah1, Muhammad Attique Khan2, Ghulam Jillani Ansari1, Saeed ur Rehman1, Muhammad Sharif1, Seifedine Kadry3, Yunyoung Nam4,*

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1251-1266, 2020, DOI:10.32604/iasc.2020.012775

    Abstract Detection of textual data from scene text images is a very thought-provoking issue in the field of computer graphics and visualization. This challenge is even more complicated when edge intelligent devices are involved in the process. The low-quality image having challenges such as blur, low resolution, and contrast make it more difficult for text detection and classification. Therefore, such exigent aspect is considered in the study. The technology proposed is comprised of three main contributions. (a) After synthetic blurring, the blurred image is preprocessed, and then the deblurring process is applied to recover the image. (b) Subsequently, the standard maximal… More >

  • Open Access


    Battlefield Situation Information Recommendation Based on Recall-Ranking

    Chunhua Zhou*, Jianjing Shen, Yuncheng Wang, Xiaofeng Guo

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1429-1440, 2020, DOI:10.32604/iasc.2020.011757

    Abstract With the rapid development of information technology, battlefield situation data presents the characteristics of “4V” such as Volume, Variety, Value and Velocity. While enhancing situational awareness, it also brings many challenges to battlefield situation information recommendation (BSIR), such as big data volume, high timeliness, implicit feedback and no negative feedback. Focusing on the challenges faced by BSIR, we propose a two-stage BSIR model based on deep neural network (DNN). The model utilizes DNN to extract the nonlinear relationship between the data features effectively, mine the potential content features, and then improves the accuracy of recommendation. These two stages are the… More >

  • Open Access


    Review of Image-Based Person Re-Identification in Deep Learning

    Junchuan Yang*

    Journal of New Media, Vol.2, No.4, pp. 137-148, 2020, DOI:10.32604/jnm.2020.014278

    Abstract Person Re-identification (re-ID) is a hot research topic in the field of computer vision now, which can be regarded as a sub-problem of image retrieval. The goal of person re-ID is to give a monitoring pedestrian image and retrieve other images of the pedestrian across the device. At present, person re-ID is mainly divided into two categories. One is the traditional methods, which relies heavily on manual features. The other is to use deep learning technology to solve. Because traditional methods mainly rely on manual feature, they cannot adapt well to a complex environment with a large amount of data.… More >

  • Open Access


    Three-Dimensional Measurement Using Structured Light Based on Deep Learning

    Tao Zhang1,*, Jinxing Niu1, Shuo Liu1, Taotao Pan1, Brij B. Gupta2,3

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 271-280, 2021, DOI:10.32604/csse.2021.014181

    Abstract Three-dimensional (3D) reconstruction using structured light projection has the characteristics of non-contact, high precision, easy operation, and strong real-time performance. However, for actual measurement, projection modulated images are disturbed by electronic noise or other interference, which reduces the precision of the measurement system. To solve this problem, a 3D measurement algorithm of structured light based on deep learning is proposed. The end-to-end multi-convolution neural network model is designed to separately extract the coarse- and fine-layer features of a 3D image. The point-cloud model is obtained by nonlinear regression. The weighting coefficient loss function is introduced to the multi-convolution neural network,… More >

  • Open Access


    Effective Latent Representation for Prediction of Remaining Useful Life

    Qihang Wang, Gang Wu*

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 225-237, 2021, DOI:10.32604/csse.2021.014100

    Abstract AI approaches have been introduced to predict the remaining useful life (RUL) of a machine in modern industrial areas. To apply them well, challenges regarding the high dimension of the data space and noisy data should be met to improve model efficiency and accuracy. In this study, we propose an end-to-end model, termed ACB, for RUL predictions; it combines an autoencoder, convolutional neural network (CNN), and bidirectional long short-term memory. A new penalized root mean square error loss function is included to avoid an overestimation of the RUL. With the CNN-based autoencoder, a high-dimensional data space can be mapped into… More >

  • Open Access


    Automatic Channel Detection Using DNN on 2D Seismic Data

    Fahd A. Alhaidari1, Saleh A. Al-Dossary2, Ilyas A. Salih1,*, Abdlrhman M. Salem1, Ahmed S. Bokir1, Mahmoud O. Fares1, Mohammed I. Ahmed1, Mohammed S. Ahmed1

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 57-67, 2021, DOI:10.32604/csse.2021.013843

    Abstract Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image… More >

  • Open Access


    Multi-Scale Boxes Loss for Object Detection in Smart Energy

    Zhiyong Dai1,*, Jianjun Yi1, Yajun Zhang1, Liang He2

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 887-903, 2020, DOI:10.32604/iasc.2020.010122

    Abstract The rapid development of Internet of Things (IoT) technologies has boosted smart energy networks in recent years. However, power line surveillance systems still suffer from the low accuracy and efficiency of the power line area recognition and risk objects detection. This paper proposes a new customized loss function to tackle the disequilibrium of the size of objects on multi-scale feature maps in the deep learning-based detectors. To validate the new concept and improve the efficiency, we also presented a new object detection model. Experimental results are provided to exhibit the advantage of our proposed method in both accuracy and efficiency. More >

  • Open Access


    An Overview of Face Manipulation Detection

    Xingwang Ju*

    Journal of Cyber Security, Vol.2, No.4, pp. 197-207, 2020, DOI:10.32604/jcs.2020.014310

    Abstract Due to the power of editing tools, new types of fake faces are being created and synthesized, which has attracted great attention on social media. It is reasonable to acknowledge that one human cannot distinguish whether the face is manipulated from the real faces. Therefore, the detection of face manipulation becomes a critical issue in digital media forensics. This paper provides an overview of recent deep learning detection models for face manipulation. Some public dataset used for face manipulation detection is introduced. On this basis, the challenges for the research and the potential future directions are analyzed and discussed. More >

  • Open Access


    A Two-Stage Highly Robust Text Steganalysis Model

    Enlu Li1, Zhangjie Fu1,2,3,*, Siyu Chen1, Junfu Chen1

    Journal of Cyber Security, Vol.2, No.4, pp. 183-190, 2020, DOI:10.32604/jcs.2020.015010

    Abstract With the development of natural language processing, deep learning, and other technologies, text steganography is rapidly developing. However, adversarial attack methods have emerged that gives text steganography the ability to actively spoof steganalysis. If terrorists use the text steganography method to spread terrorist messages, it will greatly disturb social stability. Steganalysis methods, especially those for resisting adversarial attacks, need to be further improved. In this paper, we propose a two-stage highly robust model for text steganalysis. The proposed method analyzes and extracts anomalous features at both intra-sentential and inter-sentential levels. In the first phase, every sentence is first transformed into… More >

Displaying 1261-1270 on page 127 of 1370. Per Page