Results 51 to 60 of about 18,989 (190)

Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)

, 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.
Kuo, C. -C. Jay, Ren, Yuzhuo, Salloum, Ronald   +2 more
core   +1 more source

Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks

, 2018
Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients. In this work, we describe a custom-build 3D fully convolutional network (FCN) that
Fujiwara, Michitaka, Hayashi, Yuichiro, Kitasaka, Takayuki, Misawa, Kazunari, Mori, Kensaku, Oda, Hirohisa, Oda, Masahiro, Roth, Holger, Shimizu, Natsuki   +8 more
core   +1 more source

Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders

IEEE Access, 2019
The electrocardiogram (ECG) is an efficient and noninvasive indicator for arrhythmia detection and prevention. In real-world scenarios, ECG signals are prone to be contaminated with various noises, which may lead to wrong interpretation.
Hsin-Tien Chiang, Yi-Yen Hsieh, Szu-Wei Fu, Kuo-Hsuan Hung, Yu Tsao, Shao-Yi Chien   +5 more
doaj   +1 more source

A Deep Primal-Dual Network for Guided Depth Super-Resolution

, 2016
In this paper we present a novel method to increase the spatial resolution of depth images. We combine a deep fully convolutional network with a non-local variational method in a deep primal-dual network.
Bischof, Horst, Ferstl, David, Riegler, Gernot, Rüther, Matthias   +3 more
core   +1 more source

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

Advanced Intelligent Discovery, EarlyView.
This paper presents a computer vision (deep learning) pipeline integrating YOLOv8 and YOLOv9 for automated detection, segmentation, and analysis of rosette cellulose synthase complexes in freeze‐fracture electron microscopy images. The study explores curated dataset expansion for model improvement and highlights pipeline accuracy, speed ...
Siri Mudunuri, Leala Carbonneau, Eric M. Roberts, Alison W. Roberts, Candace H. Haigler, Alexey V. Gulyuk, Yaroslava G. Yingling   +6 more
wiley   +1 more source

Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map

Sensors
Region proposal-based detectors, such as Region-Convolutional Neural Networks (R-CNNs), Fast R-CNNs, Faster R-CNNs, and Region-Based Fully Convolutional Networks (R-FCNs), employ a two-stage process involving region proposal generation followed by ...
Zhihao Su, Afzan Adam, Mohammad Faidzul Nasrudin, Anton Satria Prabuwono   +3 more
doaj   +1 more source

ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation

, 2016
Large-scale data is of crucial importance for learning semantic segmentation models, but annotating per-pixel masks is a tedious and inefficient procedure.
Dai, Jifeng, He, Kaiming, Jia, Jiaya, Lin, Di, Sun, Jian   +4 more
core   +1 more source

Disentangling Coincident Cell Events Using Deep Transfer Learning and Compressive Sensing

Advanced Intelligent Systems, EarlyView.
Overlapping cells during detection distort single‐cell measurements and reduce diagnostic accuracy. A hybrid framework combining a fully convolutional neural network with compressive sensing to disentangle overlapping signals directly from raw time‐series data is presented.
Moritz Leuthner, Rafael Vorländer, Oliver Hayden   +2 more
wiley   +1 more source

Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection

, 2018
Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving. Recently, many deep learning methods spring up for this task because they can extract high-level local features to find road regions from raw RGB data ...
Gao, Junyu, Wang, Qi, Yuan, Yuan
core   +1 more source

Tomtit‐Raven Evolutionary Selector‐Reinforced Attention‐Driven: A High‐Performance and Computationally Efficient Cyber Threat Detection Framework for Smart Grids

Energy Science &Engineering, EarlyView.
Overview of the proposed work. ABSTRACT Identifying cyber threats maintains the security and operational stability of smart grid systems because they experience escalating attacks that endanger both operating data reliability and system stability and electricity grid performance.
Priya R. Karpaga, Praveen Kumar Balachandran, Umawathy Techanamurthy, Muhammad Ammirrul Atiqi Mohd Zainuri   +3 more
wiley   +1 more source