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Ellipse-FCN: Oil Tanks Detection from Remote Sensing Images with Fully Convolution Network

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
Oil is an essential asset for every country, and plays a key role in world trade system. The detection of oil tanks is a very important task for both military and commerce. Recently, researchers have shown an increasing interest in oil tanks detection in remote sensing imagery.
Ziteng Cui +4 more
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Evaluation on Auto-segmentation of the Clinical Target Volume (CTV) for Graves' Ophthalmopathy (GO) with a Fully Convolutional Network (FCN) on CT Images

Current Medical Imaging Formerly Current Medical Imaging Reviews, 2021
Purpose: The aim of this study is to evaluate the accuracy and dosimetric effects for auto- segmentation of the CTV for GO in CT images based on FCN. Methods: An FCN-8s network architecture for auto-segmentation was built based on Caffe. CT images of 121 patients with GO who have received radiotherapy at the West China Hospital of Sichuan University
Jialiang, Jiang +5 more
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A fully convolutional network (FCN) based automated ischemic stroke segment method using chemical exchange saturation transfer imaging

Medical Physics, 2022
AbstractBackgroundChemical exchange saturation transfer (CEST) MRI is a promising imaging modality in ischemic stroke detection due to its sensitivity in sensing postischemic pH alteration. However, the accurate segmentation of pH‐altered regions remains difficult due to the complicated sources in water signal changes of CEST MRI.
Yingcheng, Zhao +5 more
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HF-FCN: Hierarchically Fused Fully Convolutional Network for Robust Building Extraction

2017
Automatic building extraction from remote sensing images plays an important role in a diverse range of applications. However, it is significantly challenging to extract arbitrary-size buildings with largely variant appearances or occlusions. In this paper, we propose a robust system employing a novel hierarchically fused fully convolutional network (HF-
Tongchun Zuo, Juntao Feng, Xuejin Chen
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RR-FCN: Rotational Region-Based Fully Convolutional Networks for Object Detection

2018
In this paper, we present rotational region-based fully convolutional networks (RR-FCN) for object detection. In contrast to previous detectors that do not consider rotation, our region-based detector incorporates rotational invariance into networks efficiently and generate more appropriate features according to the rotation angle.
Dingqian Zhang +3 more
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A fully convolutional networks (FCN) based image segmentation algorithm in binocular imaging system

2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 2018
This paper proposes an image segmentation algorithm with fully convolutional networks (FCN) in binocular imaging system under various circumstance. Image segmentation is perfectly solved by semantic segmentation. FCN classifies the pixels, so as to achieve the level of image semantic segmentation.
Liu Yuanyuan +4 more
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Glioblastomas brain Tumor Segmentation using Optimized U-Net based on Deep Fully Convolutional Networks (D-FCNs)

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2020
Manual segmentation during clinical diagnosis, is considered as time-consuming and depend to the neuroradiologists level of expertise, however due to the large spatial and structural variability of brain tumors in shapes and sizes besides to the tumor sub-region voxels’high in-homogeneity could make a reliable and accurate and automated segmentation a ...
Hiba Mzoughi +3 more
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AGR-FCN: Adversarial Generated Region based on Fully Convolutional Networks for Single- and Multiple-Instance Object Detection

2019 IEEE International Conference on Imaging Systems and Techniques (IST), 2019
Addressing the problem that object instance detection has poor detection effect on occluded objects in unstructured environment when using deep learning network, we explore the use of the strategy of adversarial learning in this paper. A three-step pipeline is carried to build a novel learning framework denoted as Adversarial Generated Region-based ...
Rui Wang 0039 +3 more
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Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance images

Australasian Physical & Engineering Sciences in Medicine, 2018
Segmentation of colorectal tumors is the basis of preoperative prediction, staging, and therapeutic response evaluation. Due to the blurred boundary between lesions and normal colorectal tissue, it is hard to realize accurate segmentation. Routinely manual or semi-manual segmentation methods are extremely tedious, time-consuming, and highly operator ...
Junming Jian +7 more
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Fully convolutional network (FCN) model to extract clear speech signals on non-stationary noises of human conversations for cochlear implants

2017 IEEE MIT Undergraduate Research Technology Conference (URTC), 2017
Cochlear implant (CI) electronically stimulates the nerve to help those with severe hearing lost. However, under noisy backgrounds, speech perception tasks have remained difficult for CI users. Therefore, speech enhancement (SE) is a critical component to improve speech perception examining through different noise scenarios. In this study, we developed
Tsai Yi-Ting, Liao Lauren Diana
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deep learning
fully convolutional network
convolutional neural network

computer vision and pattern recognition cs.cv
fos: computer and information sciences
semantic segmentation

3. good health
2. zero hunger
fcn