Results 81 to 90 of about 6,343,053 (206)

Fully Convolutional Network with Superpixel Parsing for Fashion Web Image Segmentation

, 2016
International audienceIn this paper we introduce a new method for extracting deformable clothing items from still images by extending the output of a Fully Convolutional Neural Network (FCN) to infer context from local units (superpixels).
Yang, Lixuan, Rodriguez, Helena, Marin Ferecatu, Crucianu, Michel, Michel Crucianu, Helena Rodriguez, Ferecatu, Marin, Lixuan Yang   +7 more
core   +1 more source

Efficient Symmetry-driven Fully Convolutional Network for Multimodal Brain Tumor Segmentation [PDF]

, 2017
In this paper, we present a novel and efficient method for brain tumor (and sub regions) segmentation in multimodal MR images based on a fully convolutional network (FCN) that enables end-to-end training and fast inference.
Zheng, Weishi, Haocheng Shen, Zhang, Jianguo, Weishi Zheng, Jianguo Zhang, Shen, Haocheng   +5 more
core   +1 more source

Non‐Perturbative Bacterial Identification Directly From Solid Agar Plates Using Raman Micro‐Spectroscopy and Machine Learning

Small Methods, EarlyView.
This work demonstrates a simple, rapid bacterial identification directly from unopened solid agar culture that neither perturbs culture conditions nor requires extrinsic labels, promising seamless integration of Raman spectroscopy into clinical and microbiology laboratory workflows.
Jeong Hee Kim, Jia Dong, Marissa Morales, Loza Tadesse   +3 more
wiley   +1 more source

Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery

Remote Sensing, 2017
Convolutional neural networks (CNNs) have demonstrated their ability object detection of very high resolution remote sensing images. However, CNNs have obvious limitations for modeling geometric variations in remote sensing targets.
Zhaozhuo Xu, Xin Xu, Lei Wang, Rui Yang, Fangling Pu   +4 more
doaj   +1 more source

Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN) [PDF]

Journal of Visual Communication and Image Representation, 2017
In this work, we propose a technique that utilizes a fully convolutional network (FCN) to localize image splicing attacks. We first evaluated a single-task FCN (SFCN) trained only on the surface label.
Ronald Salloum, Yuzhuo Ren, C.-C. Jay Kuo   +2 more
semanticscholar   +1 more source

FCN-VGG16 network with skip architecture.

, 2018
FCN-VGG16 network with skip architecture.
Lei Zhang (38117), Xiaoling Deng (713813), Jizhong Deng (3425030), Huasheng Huang (3425024), Yubin Lan (4722732), Aqing Yang (4722735)   +5 more
core   +1 more source

Generating Multi‐Depth 3D Holograms Using a Fully Convolutional Neural Network

Advancement of science
Efficiently generating 3D holograms is one of the most challenging research topics in the field of holography. This work introduces a method for generating multi‐depth phase‐only holograms using a fully convolutional neural network (FCN).
Xing-Peng Yan, Xinlei Liu, Jiaqi Li, Ya-Nan Zhang, He-Bin Chang, Tao Jing, Hairong Hu, Qiang Qu, Xi Wang, Xiaoyu Jiang   +9 more
semanticscholar   +1 more source

Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network

Comput. Aided Civ. Infrastructure Eng., 2019
Deep learning‐based structural damage detection methods overcome the limitation of inferior adaptability caused by extensively varying real‐world situations (e.g., lighting and shadow changes).
Sheng-Yuan Li, Xue-Feng Zhao, Guangyi Zhou   +2 more
semanticscholar   +1 more source

AML‐Net: Attention‐based multi‐scale lightweight model for brain tumour segmentation in internet of medical things

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Brain tumour segmentation employing MRI images is important for disease diagnosis, monitoring, and treatment planning. Till now, many encoder‐decoder architectures have been developed for this purpose, with U‐Net being the most extensively utilised. However, these architectures require a lot of parameters to train and have a semantic gap. Some
Muhammad Zeeshan Aslam, Basit Raza, Muhammad Faheem, Aadil Raza   +3 more
wiley   +1 more source

Ship Detection Based on Improved R-FCN

Jisuanji kexue yu tansuo, 2020
Aiming at the problem of detecting different sizes and types of ships in complex sea conditions, a ship detection method based on deep learning is proposed, which is mainly for the improvement of regional fully convolutional networks (R-FCN).
HUANG Zhijun, SANG Qingbing
doaj   +1 more source