18 papers · ranked by Valyu relevance
Shilpa Rani, Deepika Ghai, Sandeep Kumar, MVV Prasad Kantipudi + 2 more
In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object recognition and visual stimulation. This led to the development of a computer-specific pattern recognition method in this study for identifying objects in medical images…
Ricardo Pizarro, Haz-Edine Assemlal, Sethu K. Boopathy Jegathambal, Thomas Jubault + 3 more
Magnetic resonance imaging (MRI) is increasingly being used to delineate morphological changes underlying neurological disorders. Successfully detecting these changes depends on the MRI data quality. Unfortunately, image artifacts frequently compromise the MRI utility, making it critical to screen the data. Currently…
Mohamed A. Abdel-Moneim, Khalil F. Ramadan, El-Sayed M. El-Rabaie, Fathi E. Abd El-Samie + 1 more
Underwater acoustic (UWA) communication systems operate in severe channel impairments, including strong multipath propagation, long delay spreads, frequency selectivity, Doppler effects, and high ambient noise. These challenges significantly complicate automatic modulation classification (AMC), especially in dense…
Mengwei Ren, Liang Niu, Yi Fang
Recently researchers have been shifting their focus towards learned 3D shape descriptors from hand-craft ones to better address challenging issues of the deformation and structural variation inherently present in 3D objects. 3D geometric data are often transformed to 3D Voxel grids with regular format in order to be…
Mathieu Aubry, Bryan Russell
We introduce an approach for analyzing the variation of features generated by convolutional neural networks (CNNs) with respect to scene factors that occur in natural images. Such factors may include object style, 3D viewpoint, color, and scene lighting configuration. Our approach analyzes CNN feature responses…
Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli + 2 more
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which…
Ibon Merino, Jon Azpiazu, Anthony Remazeilles, Basilio Sierra + 1 more
'Sheryl Berlin Brahnam'] Deep learning methods have been successfully applied to image processing, mainly using 2D vision sensors. Recently, the rise of depth cameras and other similar 3D sensors has opened the field for new perception techniques. Nevertheless, 3D convolutional neural networks perform slightly worse…
Nobuhiko Wagatsuma, Akinori Hidaka, Hiroshi Tamura
Analysis and understanding of trained deep neural networks (DNNs) can deepen our understanding of the visual mechanisms involved in primate visual perception. However, due to the limited availability of neural activity data recorded from various cortical areas, the correspondence between the characteristics of…
Eman Ahmed, Alexandre Saint, Abd El Rahman Shabayek, Kseniya Cherenkova + 4 more
'Kseniya Cherenkova' 'Rig Das' 'Gleb Gusev' 'Djamila Aouada' 'Björn Ottersten'] Abstract: 3D data is a valuable asset the computer vision filed as it provides rich information about the full geometry of sensed objects and scenes. Recently, with the availability of both large 3D datasets and computational power, it is…
Fenil R. Doshi, Talia Konkle, George A. Alvarez
Deep neural network models provide a powerful experimental platform for exploring core mechanisms underlying human visual perception, such as perceptual grouping and contour integration — the process of linking local edge elements to arrive at a unified perceptual representation of a complete contour. Here, we…
Shan Xu, Yiyuan Zhang, Zonglei Zhen, Jia Liu
Can faces be accurately recognized with zero experience on faces? The answer to this question is critical because it examines the role of experiences in the formation of domain-specific modules in the brain. However, thorough investigation with human and non-human animals on this issue cannot easily dissociate the…
Hemaxi Narotamo, Margarida Silveira, Cláudio A. Franco
Analysis of vascular networks is an essential step to unravel the mechanisms regulating the physiological and pathological organization of blood vessels. So far, most of the analyses are performed using 2D projections of 3D networks, a strategy that has several obvious shortcomings. For instance, it does not capture…
Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger
We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of…
Afolabi J. Owoloye, Funmilayo C. Ligali, Ojochenemi A. Enejoh, Oluwafemi Agosile + 4 more
Early diagnosis of malaria is crucial for effective control and elimination efforts. Microscopy is a reliable field-adaptable malaria diagnostic method. However, microscopy results are only as good as the quality of slides and images obtained from thick and thin smears. In this study, we developed deep learning…
Wenhao Tang, Junding Sun, Shuihua Wang, Yudong Zhang
In recent years, the rapid development of deep learning has led to a wide range of applications in medical image classification. The variants of neural network models with ever-increasing performance share some commonalities: to try to mitigate overfitting, improve generalization, avoid gradient vanishing and…
Xiaoke Shen, Ioannis Stamos
Instance segmentation and object detection are significant problems in the fields of computer vision and robotics. We address those problems by proposing a novel object segmentation and detection system. First, we detect 2D objects based on RGB, depth only, or RGB-D images. A 3D convolutional-based system, named…
Laixiang Xu, Yanyan Dong, Madineh Bijani, Yang Zhang + 2 more
Accurate monitoring of microalgae is essential for assessing marine ecological health and preventing harmful algal blooms in ocean engineering. Current in situ identification methods often suffer from limited discriminative feature extraction and inadequate adaptation to complex underwater imaging conditions. This…
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Topology is key to the determination of many physical and chemical properties of materials, such as electrical and optical properties, magnetic properties, thermal and mechanical behaviour, etc. However, despite the growing number of databases of crystalline materials available, there has been very little systematic…