Results 71 to 80 of about 1,532,152 (301)

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

Real‐time semantic segmentation network for crops and weeds based on multi‐branch structure

IET Computer Vision
Weed recognition is an inevitable problem in smart agriculture, and to realise efficient weed recognition, complex background, insufficient feature information, varying target sizes and overlapping crops and weeds are the main problems to be solved.
Yufan Liu, Muhua Liu, Xuhui Zhao, Junlong Zhu, Lin Wang, Hao Ma, Mingchuan Zhang   +6 more
doaj   +1 more source

Non‐stationary financial time series forecasting based on meta‐learning

Electronics Letters, 2023
In this letter, the authors address the challenge in forecasting non‐stationary financial time series by proposing a meta‐learning based forecasting model equipped with a convolution neural network (CNN) predictor and a long short‐term memory (LSTM) meta‐
Anqi Hong, Minghan Gao, Qiang Gao, Xiao‐Hong Peng   +3 more
doaj   +1 more source

DeepID-Net: Deformable deep convolutional neural networks for object detection [PDF]

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and
Wanli Ouyang, Xiaogang Wang 0001, Xingyu Zeng, Shi Qiu, Ping Luo 0002, Yonglong Tian, Hongsheng Li 0001, Shuo Yang 0003, Zhe Wang 0006, Chen Change Loy, Xiaoou Tang   +10 more
openaire   +4 more sources

Petrographic analysis with deep convolutional neural networks [PDF]

, 2019
Petrographic analysis is based on the microscopic description and classification of rocks and is a crucial technique for sedimentary and diagenetic studies.
Pires de Lima, Rafael
core  

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

, 2019
Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far.
Wu, Jingpeng, Lee, Kisuk, Macrina, Thomas, Turner, Nicholas, Lu, Ran, Seung, H Sebastian   +5 more
core   +1 more source

Quantifying Subsurface Weak in‐Plane Magnetization of Mixed Phase BiFeO3 by Scanning Nitrogen Vacancy Magnetometry

Advanced Functional Materials, EarlyView.
We use scanning nitrogen vacancy magnetometry to directly image the weak in‐plane magnetic moments in mixed phase BiFeO3 at the nanoscale and quantify the local magnetic moments to be 18.8±2.0 μB/nm2 in the rhombohedral‐like phase and 1.5±0.6 μB/nm2 in the well‐known non‐magnetic tetragonal‐like phase.
Lei Wang, Zhewen Xu, Yuwen Xu, Björn Josteinsson, Simon Josephy, Dawei Zhang, Yunlong Sun, Heung‐Sik Park, Jihun Kim, Kanghyun Chu, Kyung Song, Danyang Wang, Chan‐Ho Yang, Andrea Morales, Jan Seidel   +14 more
wiley   +1 more source

Lightweight attention‐guided redundancy‐reuse network for real‐time semantic segmentation

IET Image Processing, 2023
Semantic segmentation is a critical topic in computer vision, and it has numerous practical applications, including mobile devices, autonomous driving, and many other fields.
Xuegang Hu, Shuhan Xu, Liyuan Jing
doaj   +1 more source

Y-Net: A deep Convolutional Neural Network for Polyp Detection

CoRR, 2018
Colorectal polyps are important precursors to colon cancer, the third most common cause of cancer mortality for both men and women. It is a disease where early detection is of crucial importance. Colonoscopy is commonly used for early detection of cancer and precancerous pathology.
Ahmed Kedir Mohammed, Sule Yildirim, Ivar Farup, Marius Pedersen, Øistein Hovde   +4 more
openaire   +4 more sources

Deep deformable registration: Enhancing accuracy by fully convolutional neural net [PDF]

Pattern Recognition Letters, 2017
Deformable registration is ubiquitous in medical image analysis. Many deformable registration methods minimize sum of squared difference (SSD) as the registration cost with respect to deformable model parameters. In this work, we construct a tight upper bound of the SSD registration cost by using a fully convolutional neural network (FCNN) in the ...
Sayan Ghosal, Nilanjan Ray
openaire   +3 more sources