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  • Open Access


    Segmentation of Remote Sensing Images Based on U-Net Multi-Task Learning

    Ni Ruiwen1, Mu Ye1,2,3,4,*, Li Ji1, Zhang Tong1, Luo Tianye1, Feng Ruilong1, Gong He1,2,3,4, Hu Tianli1,2,3,4, Sun Yu1,2,3,4, Guo Ying1,2,3,4, Li Shijun5,6, Thobela Louis Tyasi7

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3263-3274, 2022, DOI:10.32604/cmc.2022.026881

    Abstract In order to accurately segment architectural features in high-resolution remote sensing images, a semantic segmentation method based on U-net network multi-task learning is proposed. First, a boundary distance map was generated based on the remote sensing image of the ground truth map of the building. The remote sensing image and its truth map were used as the input in the U-net network, followed by the addition of the building ground prediction layer at the end of the U-net network. Based on the ResNet network, a multi-task network with the boundary distance prediction layer was built. Experiments involving the ISPRS aerial… More >

  • Open Access


    Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

    Kyamelia Roy1, Sheli Sinha Chaudhuri1, Sayan Pramanik2, Soumen Banerjee2,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 647-662, 2023, DOI:10.32604/csse.2023.024997

    Abstract In recent years, computer vision finds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture. Automatic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies. Waterways being an important medium of transport require continuous monitoring for protection of national security. The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea. This paper proposes a deep learning based model capable enough to… More >

  • Open Access


    Modified Anam-Net Based Lightweight Deep Learning Model for Retinal Vessel Segmentation

    Syed Irtaza Haider1, Khursheed Aurangzeb2,*, Musaed Alhussein2

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1501-1526, 2022, DOI:10.32604/cmc.2022.025479

    Abstract The accurate segmentation of retinal vessels is a challenging task due to the presence of various pathologies as well as the low-contrast of thin vessels and non-uniform illumination. In recent years, encoder-decoder networks have achieved outstanding performance in retinal vessel segmentation at the cost of high computational complexity. To address the aforementioned challenges and to reduce the computational complexity, we propose a lightweight convolutional neural network (CNN)-based encoder-decoder deep learning model for accurate retinal vessels segmentation. The proposed deep learning model consists of encoder-decoder architecture along with bottleneck layers that consist of depth-wise squeezing, followed by full-convolution, and finally depth-wise… More >

  • Open Access


    Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network

    M. M. Lotfy1, Hazem M. El-Bakry2, M. M. Elgayar3, Shaker El-Sappagh4,5, G. Abdallah M. I1, A. A. Soliman1, Kyung Sup Kwak6,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1141-1158, 2022, DOI:10.32604/cmc.2022.024193

    Abstract Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people, and its high number of deaths also by 7%. For that purpose, a proposed model of several stages was developed. The first stage is optimizing the images using dynamic adaptive histogram equalization, performing a semantic segmentation using DeepLabv3Plus, then augmenting the data by flipping it horizontally, rotating it, then flipping it vertically. The second stage builds a custom convolutional neural network model using several pre-trained ImageNet. Finally, the model compares the pre-trained data to the new output, while repeatedly… More >

  • Open Access


    Optimized Deep Learning Model for Fire Semantic Segmentation

    Songbin Li1,*, Peng Liu1, Qiandong Yan1, Ruiling Qian2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4999-5013, 2022, DOI:10.32604/cmc.2022.026498

    Abstract Recent convolutional neural networks (CNNs) based deep learning has significantly promoted fire detection. Existing fire detection methods can efficiently recognize and locate the fire. However, the accurate flame boundary and shape information is hard to obtain by them, which makes it difficult to conduct automated fire region analysis, prediction, and early warning. To this end, we propose a fire semantic segmentation method based on Global Position Guidance (GPG) and Multi-path explicit Edge information Interaction (MEI). Specifically, to solve the problem of local segmentation errors in low-level feature space, a top-down global position guidance module is used to restrain the offset… More >

  • Open Access


    Automated Crack Detection via Semantic Segmentation Approaches Using Advanced U-Net Architecture

    Honggeun Ji1,2, Jina Kim3, Syjung Hwang4, Eunil Park1,4,*

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 593-607, 2022, DOI:10.32604/iasc.2022.024405

    Abstract Cracks affect the robustness and adaptability of various infrastructures, including buildings, bridge piers, pavement, and pipelines. Therefore, the robustness and the reliability of automated crack detection are essential. In this study, we conducted image segmentation using various crack datasets by applying the advanced architecture of U-Net. First, we collected and integrated crack datasets from prior studies, including the cracks in buildings and pavements. For effective localization and detection of cracks, we used U-Net-based neural networks, ResU-Net, VGGU-Net, and EfficientU-Net. The models were evaluated by the five-fold cross-validation using several evaluation metrics including mean pixel accuracy (MPA), mean intersection over union… More >

  • Open Access


    A Fast Panoptic Segmentation Network for Self-Driving Scene Understanding

    Abdul Majid1, Sumaira Kausar1,*, Samabia Tehsin1, Amina Jameel2

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 27-43, 2022, DOI:10.32604/csse.2022.022590

    Abstract In recent years, a gain in popularity and significance of science understanding has been observed due to the high paced progress in computer vision techniques and technologies. The primary focus of computer vision based scene understanding is to label each and every pixel in an image as the category of the object it belongs to. So it is required to combine segmentation and detection in a single framework. Recently many successful computer vision methods has been developed to aid scene understanding for a variety of real world application. Scene understanding systems typically involves detection and segmentation of different natural and… More >

  • Open Access


    Make U-Net Greater: An Easy-to-Embed Approach to Improve Segmentation Performance Using Hypergraph

    Jing Peng1,2,3, Jingfu Yang2, Chaoyang Xia2, Xiaojie Li2, Yanfen Guo2, Ying Fu2, Xinlai Chen4, Zhe Cui1,3,*

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 319-333, 2022, DOI:10.32604/csse.2022.022314

    Abstract Cardiac anatomy segmentation is essential for cardiomyopathy clinical diagnosis and treatment planning. Thus, accurate delineation of target volumes at risk in cardiac anatomy is important. However, manual delineation is a time-consuming and labor-intensive process for cardiologists and has been shown to lead to significant inter-and intra-practitioner variability. Thus, computer-aided or fully automatic segmentation methods are required. They can significantly economize on manpower and improve treatment efficiency. Recently, deep convolutional neural network (CNN) based methods have achieved remarkable successes in various kinds of vision tasks, such as classification, segmentation and object detection. Semantic segmentation can be considered as a pixel-wise task,… More >

  • Open Access


    Unsupervised Semantic Segmentation Method of User Interface Component of Games

    Shinjin Kang1, Jongin Choi2,*

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1089-1105, 2022, DOI:10.32604/iasc.2022.019979

    Abstract The game user interface (UI) provides a large volume of information necessary to analyze the game screen. The availability of such information can be functional in vision-based machine learning algorithms. With this, there will be an enhancement in the application power of vision deep learning neural networks. Therefore, this paper proposes a game UI segmentation technique based on unsupervised learning. We developed synthetic labeling created on the game engine, image-to-image translation and segmented UI components in the game. The network learned in this manner can segment the target UI area in the target game regardless of the location of the… More >

  • Open Access


    A Multi-Task Network for Cardiac Magnetic Resonance Image Segmentation and Classification

    Jing Peng1,2,4, Chaoyang Xia2, Yuanwei Xu3, Xiaojie Li2, Xi Wu2, Xiao Han1,4, Xinlai Chen5, Yucheng Chen3, Zhe Cui1,4,*

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 259-272, 2021, DOI:10.32604/iasc.2021.016749

    Abstract Cardiomyopathy is a group of diseases that affect the heart and can cause serious health problems. Segmentation and classification are important for automating the clinical diagnosis and treatment planning for cardiomyopathy. However, this automation is difficult because of the poor quality of cardiac magnetic resonance (CMR) imaging data and varying dimensions caused by movement of the ventricle. To address these problems, a deep multi-task framework based on a convolutional neural network (CNN) is proposed to segment the left ventricle (LV) myocardium and classify cardiopathy simultaneously. The proposed model consists of a longitudinal encoder–decoder structure that obtains high- and low-level features… More >

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