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Ku Muhammad Naim Ku Khalif, Woo Chaw Seng, Alexander Gegov, Ahmad Syafadhli Abu Bakar and Nur Adibah Shahrul
Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of ...
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Yan Zhang, Wenhan Zhao, Bo Sun, Ying Zhang and Wen Wen
Point cloud upsampling algorithms can improve the resolution of point clouds and generate dense and uniform point clouds, and are an important image processing technology. Significant progress has been made in point cloud upsampling research in recent ye...
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Shucong Liu, Hongjun Wang and Xiang Zhang
In gas turbine rotor systems, an intelligent data-driven fault diagnosis method is an important means to monitor the health status of the gas turbine, and it is necessary to obtain sufficient fault data to train the intelligent diagnosis model. In the ac...
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Hong-Chan Chang, Yi-Che Wang, Yu-Yang Shih and Cheng-Chien Kuo
A homemade defective model of an induction motor was created by the laboratory team to acquire the vibration acceleration signals of five operating states of an induction motor under different loads. Two major learning models, namely a deep convolutional...
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Yan Zhang, Shiyun Wa, Pengshuo Sun and Yaojun Wang
To address the current situation, in which pear defect detection is still based on a workforce with low efficiency, we propose the use of the CNN model to detect pear defects. Since it is challenging to obtain defect images in the implementation process,...
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Rina Komatsu and Tad Gonsalves
Digital images often become corrupted by undesirable noise during the process of acquisition, compression, storage, and transmission. Although the kinds of digital noise are varied, current denoising studies focus on denoising only a single and specific ...
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Wang Xi, Guillaume Devineau, Fabien Moutarde and Jie Yang
Generative models for images, audio, text, and other low-dimension data have achieved great success in recent years. Generating artificial human movements can also be useful for many applications, including improvement of data augmentation methods for hu...
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Maryam Omar, Hafeez Ur Rehman, Omar Bin Samin, Moutaz Alazab, Gianfranco Politano and Alfredo Benso
Text-to-image synthesis is one of the most critical and challenging problems of generative modeling. It is of substantial importance in the area of automatic learning, especially for image creation, modification, analysis and optimization. A number of wo...
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Euclides Carlos Pinto Neto, Derick Moreira Baum, Jorge Rady de Almeida, Jr., João Batista Camargo, Jr. and Paulo Sergio Cugnasca
Currently, the increasing number of daily flights emphasizes the importance of air transportation. Furthermore, Air Traffic Management (ATM) enables air carriers to operate safely and efficiently through the multiple services provided. Advanced analytic ...
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Yifan Liu, Weiliang Gao, Tingting Zhao, Zhiyong Wang and Zhihua Wang
The aim of this study is to enhance the efficiency and lower the expense of detecting cracks in large-scale concrete structures. A rapid crack detection method based on deep learning is proposed. A large number of artificial samples from existing concret...
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Yule Chen, Hong Liang and Shuo Pang
Underwater target classification methods based on deep learning suffer from obvious model overfitting and low recognition accuracy in the case of small samples and complex underwater environments. This paper proposes a novel classification network (Effic...
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Asif Hussain Khan, Christian Micheloni and Niki Martinel
Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version. Most of the existing blind SR techniques use a degradation estimator network to expl...
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Catur Supriyanto, Abu Salam, Junta Zeniarja and Adi Wijaya
This research paper presents a deep-learning approach to early detection of skin cancer using image augmentation techniques. We introduce a two-stage image augmentation process utilizing geometric augmentation and a generative adversarial network (GAN) t...
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Sai Sambasiva Rao Bairaboina and Srinivasa Rao Battula
White blood cells (WBCs) must be evaluated to determine how well the human immune system performs. Abnormal WBC counts may indicate malignancy, tuberculosis, severe anemia, cancer, and other serious diseases. To get an early diagnosis and to check if WBC...
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Wei Liu, Junxing Cao, Jiachun You and Haibo Wang
Vector decomposition of P- and S-wave modes from elastic seismic wavefields is a key step in elastic reverse-time migration (ERTM) to effectively improve the multi-wave imaging accuracy. Most previously developed methods based on the apparent velocities ...
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Hyunkyung Shin, Hyeonung Shin, Wonje Choi, Jaesung Park, Minjae Park, Euiyul Koh and Honguk Woo
The automatic analysis of medical data and images to help diagnosis has recently become a major area in the application of deep learning. In general, deep learning techniques can be effective when a large high-quality dataset is available for model train...
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Hafiz Suliman Munawar, Fahim Ullah, Amirhossein Heravi, Muhammad Jamaluddin Thaheem and Ahsen Maqsoom
Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of da...
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L. G. Divyanth, D. S. Guru, Peeyush Soni, Rajendra Machavaram, Mohammad Nadimi and Jitendra Paliwal
Applications of deep-learning models in machine visions for crop/weed identification have remarkably upgraded the authenticity of precise weed management. However, compelling data are required to obtain the desired result from this highly data-driven ope...
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Sagar Kora Venu and Sridhar Ravula
Medical image datasets are usually imbalanced due to the high costs of obtaining the data and time-consuming annotations. Training a deep neural network model on such datasets to accurately classify the medical condition does not yield the desired result...
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Armando Levid Rodríguez-Santiago, José Aníbal Arias-Aguilar, Hiroshi Takemura and Alberto Elías Petrilli-Barceló
In this paper, an approach through a Deep Learning architecture for the three-dimensional reconstruction of outdoor environments in challenging terrain conditions is presented. The architecture proposed is configured as an Autoencoder. However, instead o...
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