152 Result(s)
within
Conference Paper
M. **gliang
Computer Science
Pattern Recognition
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Chapter and Conference Paper
RF-Based Drone Detection with Deep Neural Network: Review and Case Study
Drones have been widely used in many application scenarios, such as logistics and on-demand instant delivery, surveillance, traffic monitoring, firefighting, photography, and recreation. On the other hand, the...
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Chapter and Conference Paper
The Tenth Visual Object Tracking VOT2022 Challenge Results
The Visual Object Tracking challenge VOT2022 is the tenth annual tracker benchmarking activity organized by the VOT initiative. Results of 93 entries are presented; many are state-of-the-art trackers published...
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Chapter and Conference Paper
Deep Learning for Accurate Corner Detection in Computer Vision-Based Inspection
This paper describes application of deep learning for accurate detection of corner points in images and its application for an inspection system developed for the worker training and assessment. In our local b...
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Chapter and Conference Paper
Over-and-Under Complete Convolutional RNN for MRI Reconstruction
Reconstructing magnetic resonance (MR) images from under-sampled data is a challenging problem due to various artifacts introduced by the under-sampling operation. Recent deep learning-based methods for MR ima...
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Chapter and Conference Paper
Lossless Image Compression Using a Multi-scale Progressive Statistical Model
Lossless image compression is an important technique for image storage and transmission when information loss is not allowed. With the fast development of deep learning techniques, deep neural networks have be...
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Chapter and Conference Paper
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction
Automatically segmenting sub-regions of gliomas (necrosis, edema and enhancing tumor) and accurately predicting overall survival (OS) time from multimodal MRI sequences have important clinical significance in ...
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Chapter and Conference Paper
Lesion Mask-Based Simultaneous Synthesis of Anatomic and Molecular MR Images Using a GAN
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas for patients with malignant gliomas in neuro-oncology with the help of conventional an...
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Chapter and Conference Paper
Few Is Enough: Task-Augmented Active Meta-learning for Brain Cell Classification
Deep Neural Networks (or DNNs) must constantly cope with distribution changes in the input data when the task of interest or the data collection protocol changes. Retraining a network from scratch to combat th...
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Chapter and Conference Paper
Brain Tumor Classification with Tumor Segmentations and a Dual Path Residual Convolutional Neural Network from MRI and Pathology Images
Brain tumor classification plays an important role in brain cancer diagnosis and treatment. Pathologists typically have to work through numerous pathology images that can be in the order of hundreds or thousan...
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Chapter and Conference Paper
VisDrone-MOT2020: The Vision Meets Drone Multiple Object Tracking Challenge Results
The Vision Meets Drone (VisDrone2020) Multiple Object Tracking (MOT) is the third annual UAV MOT tracking evaluation activity organized by the VisDrone team, in conjunction with European Conference on Computer...
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Chapter and Conference Paper
Seeing Things in Random-Dot Videos
The human visual system correctly groups features and can even interpret random-dot videos induced by imaging natural dynamic scenes. Remarkably, this happens even if perception completely fails when the same ...
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Chapter and Conference Paper
MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net
Accurate hepatic vessel segmentation and registration using ultrasound (US) can contribute to beneficial navigation during hepatic surgery. However, it is challenging due to noise and speckle in US imaging and...
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Chapter and Conference Paper
Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
During fetoscopic laser photocoagulation, a treatment for twin-to-twin transfusion syndrome (TTTS), the clinician first identifies abnormal placental vascular connections and laser ablates them to regulate blo...
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Chapter and Conference Paper
The Sixth Visual Object Tracking VOT2018 Challenge Results
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers...
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Chapter and Conference Paper
Hydranet: Data Augmentation for Regression Neural Networks
Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annotator expertise is often scar...
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Chapter and Conference Paper
Generative Adversarial Network for Segmentation of Motion Affected Neonatal Brain MRI
Automatic neonatal brain tissue segmentation in preterm born infants is a prerequisite for evaluation of brain development. However, automatic segmentation is often hampered by motion artifacts caused by infan...
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Chapter and Conference Paper
Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices
In this paper, we present a fully convolutional densely connected network (Tiramisu) for multiple sclerosis (MS) lesion segmentation. Different from existing methods, we use stacked slices from all three anato...
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Chapter and Conference Paper
Optimal Experimental Design for Biophysical Modelling in Multidimensional Diffusion MRI
Computational models of biophysical tissue properties have been widely used in diffusion MRI (dMRI) research to elucidate the link between microstructural properties and MR signal formation. For brain tissue,...
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Chapter and Conference Paper
Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network
Localization of focal vascular lesions on brain MRI is an important component of research on the etiology of neurological disorders. However, manual annotation of lesions can be challenging, time-consuming an...
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Chapter and Conference Paper
Variational AutoEncoder for Regression: Application to Brain Aging Analysis
While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified...
