S. Eliza Femi Sherley^1,*, J. M. Karthikeyan¹, N. Bharath Raj¹, R. Prabakaran², A. Abinaya¹, S. V. V. Lakshmi³

Journal on Artificial Intelligence, Vol.4, No.4, pp. 215-227, 2022, DOI:10.32604/jai.2022.034931 - 25 May 2023

Abstract Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic (labels/categories) details before and after the timelines are analyzed. Periodical land change analysis is used for many real time applications for valuation purposes. Majority of the research works are focused on Convolutional Neural Networks (CNN) which tries to analyze changes alone. Semantic information of changes appears to be missing, there by absence of communication between the different semantic timelines and changes detected over the region happens.… More >

Siddiqui Muhammad Yasir¹, Hyunsik Ahn^2,*

Journal on Artificial Intelligence, Vol.4, No.2, pp. 99-114, 2022, DOI:10.32604/jai.2022.031235 - 18 July 2022

Abstract The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation. Segmentation is challenging with point cloud data due to substantial redundancy, fluctuating sample density and lack of apparent organization. The research area has a wide range of robotics applications, including intelligent vehicles, autonomous mapping and navigation. A number of researchers have introduced various methodologies and algorithms. Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I. methods. However, due to the specific… More >

Ni Ruiwen¹, Mu Ye^1,2,3,4,*, Li Ji¹, Zhang Tong¹, Luo Tianye¹, Feng Ruilong¹, Gong He^1,2,3,4, Hu Tianli^1,2,3,4, Sun Yu^1,2,3,4, Guo Ying^1,2,3,4, Li Shijun^5,6, Thobela Louis Tyasi⁷

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3263-3274, 2022, DOI:10.32604/cmc.2022.026881 - 16 June 2022

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. More >

Syed Irtaza Haider¹, Khursheed Aurangzeb^2,*, Musaed Alhussein²

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1501-1526, 2022, DOI:10.32604/cmc.2022.025479 - 18 May 2022

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… More >

M. M. Lotfy¹, Hazem M. El-Bakry², M. M. Elgayar³, Shaker El-Sappagh^4,5, G. Abdallah M. I¹, A. A. Soliman¹, Kyung Sup Kwak^6,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1141-1158, 2022, DOI:10.32604/cmc.2022.024193 - 18 May 2022

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 More >

Songbin Li^1,*, Peng Liu¹, Qiandong Yan¹, Ruiling Qian²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4999-5013, 2022, DOI:10.32604/cmc.2022.026498 - 21 April 2022

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 More >

Honggeun Ji^1,2, Jina Kim³, Syjung Hwang⁴, Eunil Park^1,4,*

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 593-607, 2022, DOI:10.32604/iasc.2022.024405 - 15 April 2022

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… More >

Abdul Majid¹, Sumaira Kausar^1,*, Samabia Tehsin¹, Amina Jameel²

Computer Systems Science and Engineering, Vol.43, No.1, pp. 27-43, 2022, DOI:10.32604/csse.2022.022590 - 23 March 2022

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… More >

Jing Peng^1,2,3, Jingfu Yang², Chaoyang Xia², Xiaojie Li², Yanfen Guo², Ying Fu², Xinlai Chen⁴, Zhe Cui^1,3,*

Computer Systems Science and Engineering, Vol.42, No.1, pp. 319-333, 2022, DOI:10.32604/csse.2022.022314 - 02 December 2021

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… More >

Shinjin Kang¹, Jongin Choi^2,*

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1089-1105, 2022, DOI:10.32604/iasc.2022.019979 - 22 September 2021

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 More >