Zhiqiang Wu^*, Bing Xie

Computer Systems Science and Engineering, Vol.47, No.1, pp. 299-316, 2023, DOI:10.32604/csse.2023.038586

Abstract Image has become an essential medium for expressing meaning and disseminating information. Many images are uploaded to the Internet, among which some are pornographic, causing adverse effects on public psychological health. To create a clean and positive Internet environment, network enforcement agencies need an automatic and efficient pornographic image recognition tool. Previous studies on pornographic images mainly rely on convolutional neural networks (CNN). Because of CNN’s many parameters, they must rely on a large labeled training dataset, which takes work to build. To reduce the effect of the database on the recognition performance of pornographic images, many researchers view pornographic… More >

Muhammad Aamir¹, Ziaur Rahman^1,*, Waheed Ahmed Abro², Uzair Aslam Bhatti³, Zaheer Ahmed Dayo¹, Muhammad Ishfaq¹

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6351-6373, 2023, DOI:10.32604/cmc.2023.038173

Abstract Object detection in images has been identified as a critical area of research in computer vision image processing. Research has developed several novel methods for determining an object’s location and category from an image. However, there is still room for improvement in terms of detection efficiency. This study aims to develop a technique for detecting objects in images. To enhance overall detection performance, we considered object detection a two-fold problem, including localization and classification. The proposed method generates class-independent, high-quality, and precise proposals using an agglomerative clustering technique. We then combine these proposals with the relevant input image to train… More >

Xiuli Si¹, Biao Hong¹, Yuanhui Hu¹, Lidong Chu^2,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6101-6118, 2023, DOI:10.32604/cmc.2023.036829

Abstract Currently, one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development. Therefore, further research in the field of crop disease and pest detection is necessary to address the mentioned problem. Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses, this article proposes a Model-Agnostic Meta-Learning (MAML) attention model based on the meta-learning paradigm. The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention (ECA) module. The module follows the… More >

Wen-Tsai Sung¹, Chin-Hsuan Lin¹, Sung-Jung Hsiao^2,*

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 proposed system reduces the inefficiency… More >

Bingzheng Wu¹, Peizhong Liu¹, Huiling Wu², Shunlan Liu², Shaozheng He², Guorong Lv^2,3,*

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 of diagnosis, and improve the… More >

Xindai An¹, Di Wu^1,2,*, 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 design a lightweight convolutional neural… More >

Zhilin Zhang¹, Ting Zhang¹, Zhaoying Liu^1,*, 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 from ships. Secondly, by adding… More >

V. Magesh^1,*, 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 higher order tap-matched filter is… More >

Shanshan Liu¹, Yukun Cao¹, Lu Gao¹, Jian Xu^1,2,*, Wu Zeng^1,2

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 system, the image recognition could… More >

Yajun Zhang^1,*, 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 this issue by developing a… More >