Xiaosha Qi¹, Genlin Ji^2,*, Jie Zhang², Bo Sheng³

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1861-1875, 2022, DOI:10.32604/iasc.2022.027182

Abstract Video anomaly detection is essential to distinguish abnormal events in large volumes of surveillance video and can benefit many fields such as traffic management, public security and failure detection. However, traditional video anomaly detection methods are unable to accurately detect and locate abnormal events in real scenarios, while existing deep learning methods are likely to omit important information when extracting features. In order to avoid omitting important features and improve the accuracy of abnormal event detection and localization, this paper proposes a novel method called Multi Chunk Learning based Skip Connected Convolutional Auto Encoder (MCSCAE). The proposed method improves the… More >

Linfang Yu, Zhen Qin^*, Yi Ding, Zhiguang Qin

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 805-828, 2021, DOI:10.32604/cmes.2021.017332

Abstract As an important part of the new generation of information technology, the Internet of Things (IoT) has been widely concerned and regarded as an enabling technology of the next generation of health care system. The fundus photography equipment is connected to the cloud platform through the IoT, so as to realize the real-time uploading of fundus images and the rapid issuance of diagnostic suggestions by artificial intelligence. At the same time, important security and privacy issues have emerged. The data uploaded to the cloud platform involves more personal attributes, health status and medical application data of patients. Once leaked, abused… More >

Chung-Il Kim¹, Jehyeok Rew², Yongjang Cho², Eenjun Hwang^2,*

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 >

Runzhe Tao^{1, *}, Yonghong Zhang¹, Lihua Wang¹, Pengyan Cai¹, Haowen Tan²

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2455-2474, 2020, DOI:10.32604/cmc.2020.011526

Abstract Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring. UNet, an advanced machine learning (ML) method, is used to develop a robust and rapid algorithm for precipitating cloud detection based on the new-generation geostationary satellite of FengYun-4A (FY-4A). First, in this algorithm, the real-time multi-band infrared brightness temperature from FY-4A combined with the data of Digital Elevation Model (DEM) has been used as predictor variables for our model. Second, the efficiency of the feature was improved by changing the traditional convolution layer serial connection method of… More >

Yecai Guo^1,2,*, Chen Li^1,2, Qi Liu³

CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 829-843, 2019, DOI:10.32604/cmc.2019.03729

Abstract Visual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems. Hence, it is necessary to address the problem of eliminating rain streaks from the individual rainy image. In this work, a deep convolution neural network (CNN) based method is introduced, called Rain-Removal Net (R²N), to solve the single image de-raining issue. Firstly, we decomposed the rainy image into its high-frequency detail layer and low-frequency base layer. Then, we used the high-frequency detail layer to input the carefully designed CNN architecture to learn the mapping between it and its… More >