Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (23)
  • Open Access


    Image Recognition Based on Deep Learning with Thermal Camera Sensing

    Wen-Tsai Sung1, Chin-Hsuan Lin1, Sung-Jung Hsiao2,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 505-520, 2023, DOI:10.32604/csse.2023.034781

    Abstract As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The More >

  • Open Access


    An Effective Machine-Learning Based Feature Extraction/Recognition Model for Fetal Heart Defect Detection from 2D Ultrasonic Imageries

    Bingzheng Wu1, Peizhong Liu1, Huiling Wu2, Shunlan Liu2, Shaozheng He2, Guorong Lv2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1069-1089, 2023, DOI:10.32604/cmes.2022.020870

    Abstract Congenital heart defect, accounting for about 30% of congenital defects, is the most common one. Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns. In Fetal and Neonatal Cardiology, medical imaging technology (2D ultrasonic, MRI) has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis. It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane (FHUSP) manually. Compared with manual identification, automatic identification through artificial intelligence can save a lot of time, ensure the efficiency… More >

  • Open Access


    Slope Collapse Detection Method Based on Deep Learning Technology

    Xindai An1, Di Wu1,2,*, Xiangwen Xie1, Kefeng Song1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1091-1103, 2023, DOI:10.32604/cmes.2022.020670

    Abstract So far, slope collapse detection mainly depends on manpower, which has the following drawbacks: (1) low reliability, (2) high risk of human safe, (3) high labor cost. To improve the efficiency and reduce the human investment of slope collapse detection, this paper proposes an intelligent detection method based on deep learning technology for the task. In this method, we first use the deep learning-based image segmentation technology to find the slope area from the captured scene image. Then the foreground motion detection method is used for detecting the motion of the slope area. Finally, we More >

  • Open Access


    Fine-grained Ship Image Recognition Based on BCNN with Inception and AM-Softmax

    Zhilin Zhang1, Ting Zhang1, Zhaoying Liu1,*, Peijie Zhang1, Shanshan Tu1, Yujian Li2, Muhammad Waqas3

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1527-1539, 2022, DOI:10.32604/cmc.2022.029297

    Abstract The fine-grained ship image recognition task aims to identify various classes of ships. However, small inter-class, large intra-class differences between ships, and lacking of training samples are the reasons that make the task difficult. Therefore, to enhance the accuracy of the fine-grained ship image recognition, we design a fine-grained ship image recognition network based on bilinear convolutional neural network (BCNN) with Inception and additive margin Softmax (AM-Softmax). This network improves the BCNN in two aspects. Firstly, by introducing Inception branches to the BCNN network, it is helpful to enhance the ability of extracting comprehensive features… More >

  • Open Access


    Design of Higher Order Matched FIR Filter Using Odd and Even Phase Process

    V. Magesh1,*, N. Duraipandian2

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1499-1510, 2022, DOI:10.32604/iasc.2022.020552

    Abstract The current research paper discusses the implementation of higher order-matched filter design using odd and even phase processes for efficient area and time delay reduction. Matched filters are widely used tools in the recognition of specified task. When higher order taps are implemented upon the transposed form of matched filters, it can enhance the image recognition application and its performance in terms of identification and accuracy. The proposed method i.e., odd and even phases’ process of FIR filter can reduce the number of multipliers and adders, used in existing system. The main advantage of using… More >

  • Open Access


    Realization of Mobile Augmented Reality System Based on Image Recognition

    Shanshan Liu1, Yukun Cao1, Lu Gao1, Jian Xu1,2,*, Wu Zeng1,2

    Journal of Information Hiding and Privacy Protection, Vol.3, No.2, pp. 55-59, 2021, DOI:10.32604/jihpp.2021.017254

    Abstract With the development of computation technology, the augmented reality (AR) is widely applied in many fields as well as the image recognition. However, the AR application on mobile platform is not developed enough in the past decades due to the capability of the mobile processors. In recent years, the performance of mobile processors has changed rapidly, which makes it comparable to the desktop processors. This paper proposed and realized an AR system to be used on the Android mobile platform based on the image recognition through EasyAR engine and Unity 3D development tools. In this More >

  • Open Access


    Generation of Synthetic Images of Randomly Stacked Object Scenes for Network Training Applications

    Yajun Zhang1,*, Jianjun Yi1, Jiahao Zhang1, Yuanhao Chen1, Liang He2

    Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 425-439, 2021, DOI:10.32604/iasc.2021.013795

    Abstract Image recognition algorithms based on deep learning have been widely developed in recent years owing to their capability of automatically capturing recognition features from image datasets and constantly improving the accuracy and efficiency of the image recognition process. However, the task of training deep learning networks is time-consuming and expensive because large training datasets are generally required, and extensive manpower is needed to annotate each of the images in the training dataset to support the supervised learning process. This task is particularly arduous when the image scenes involve randomly stacked objects. The present work addresses… More >

  • Open Access


    Intelligent Prediction Approach for Diabetic Retinopathy Using Deep Learning Based Convolutional Neural Networks Algorithm by Means of Retina Photographs

    G. Arun Sampaul Thomas1, Y. Harold Robinson2, E. Golden Julie3, Vimal Shanmuganathan4, Seungmin Rho5, Yunyoung Nam6,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1613-1629, 2021, DOI:10.32604/cmc.2020.013443

    Abstract Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed, leak fluid and vision impairment. Symptoms of retinopathy are blurred vision, changes in color perception, red spots, and eye pain and it cannot be detected with a naked eye. In this paper, a new methodology based on Convolutional Neural Networks (CNN) is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate diagnoses. The CNN model is trained by different images of eyes that have More >

  • Open Access


    Image Recognition of Citrus Diseases Based on Deep Learning

    Zongshuai Liu1, Xuyu Xiang1,2,*, Jiaohua Qin1, Yun Tan1, Qin Zhang1, Neal N. Xiong3

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 457-466, 2021, DOI:10.32604/cmc.2020.012165

    Abstract In recent years, with the development of machine learning and deep learning, it is possible to identify and even control crop diseases by using electronic devices instead of manual observation. In this paper, an image recognition method of citrus diseases based on deep learning is proposed. We built a citrus image dataset including six common citrus diseases. The deep learning network is used to train and learn these images, which can effectively identify and classify crop diseases. In the experiment, we use MobileNetV2 model as the primary network and compare it with other network models More >

  • Open Access


    An Improved Deep Fusion CNN for Image Recognition

    Rongyu Chen1, Lili Pan1, *, Cong Li1, Yan Zhou1, Aibin Chen1, Eric Beckman2

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1691-1706, 2020, DOI:10.32604/cmc.2020.011706

    Abstract With the development of Deep Convolutional Neural Networks (DCNNs), the extracted features for image recognition tasks have shifted from low-level features to the high-level semantic features of DCNNs. Previous studies have shown that the deeper the network is, the more abstract the features are. However, the recognition ability of deep features would be limited by insufficient training samples. To address this problem, this paper derives an improved Deep Fusion Convolutional Neural Network (DF-Net) which can make full use of the differences and complementarities during network learning and enhance feature expression under the condition of limited… More >

Displaying 11-20 on page 2 of 23. Per Page