scholarly journals Real-time, wide-field and high-quality single snapshot imaging of optical properties with profile correction using deep learning

2020 ◽  
Vol 11 (10) ◽  
pp. 5701 ◽  
Author(s):  
Enagnon Aguénounon ◽  
Jason T. Smith ◽  
Mahdi Al-Taher ◽  
Michele Diana ◽  
Xavier Intes ◽  
...  
Keyword(s):  
Optical Properties ◽  
Deep Learning ◽  
Real Time ◽  
Wide Field ◽  
High Quality ◽  
Profile Correction ◽  
Snapshot Imaging ◽  
Single Snapshot

scholarly journals Real-time, profile-corrected single snapshot imaging of optical properties

2015 ◽  
Vol 6 (10) ◽  
pp. 4051 ◽  
Author(s):  
Martijn van de Giessen ◽  
Joseph P. Angelo ◽  
Sylvain Gioux
Keyword(s):  
Optical Properties ◽  
Real Time ◽  
Time Profile ◽  
Snapshot Imaging ◽  
Single Snapshot

scholarly journals Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 649
Author(s):  
Yifeng Liu ◽  
Wei Zhang ◽  
Wenhao Du
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Quality Data ◽  
Time Data ◽  
High Quality ◽  
Real Time System ◽  
High Quality Data ◽  
Learning Framework ◽  
Data Accumulation

Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.

scholarly journals Single snapshot imaging of optical properties

2013 ◽  
Vol 4 (12) ◽  
pp. 2938 ◽  
Author(s):  
Jean Vervandier ◽  
Sylvain Gioux
Keyword(s):  
Optical Properties ◽  
Snapshot Imaging ◽  
Single Snapshot

scholarly journals Single snapshot imaging of optical properties using a single-pixel camera: a simulation study

2019 ◽  
Vol 24 (07) ◽  
pp. 1 ◽  
Author(s):  
Enagnon Aguénounon ◽  
Foudil Dadouche ◽  
Wilfried Uhring ◽  
Nicolas Ducros ◽  
Sylvain Gioux
Keyword(s):  
Optical Properties ◽  
Simulation Study ◽  
Snapshot Imaging ◽  
Single Pixel ◽  
Single Snapshot

scholarly journals Single Snapshot Imaging of Optical Properties (SSOP) for Perfusion Assessment during Gastric Conduit Creation for Esophagectomy: An Experimental Study on Pigs

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6079
Author(s):  
Lorenzo Cinelli ◽  
Eric Felli ◽  
Luca Baratelli ◽  
Silvère Ségaud ◽  
Andrea Baiocchini ◽  
...  
Keyword(s):  
Optical Properties ◽  
Tissue Oxygenation ◽  
Current Knowledge ◽  
New Technology ◽  
Gastric Conduit ◽  
Anastomotic Site ◽  
Optical Technology ◽  
Perfusion Assessment ◽  
Snapshot Imaging ◽  
Single Snapshot

Anastomotic leakage (AL) is a serious complication occurring after esophagectomy. The current knowledge suggests that inadequate intraoperative perfusion in the anastomotic site contributes to an increase in the AL rate. Presently, clinical estimation undertaken by surgeons is not accurate and new technology is necessary to improve the intraoperative assessment of tissue oxygenation. In the present study, we demonstrate the application of a novel optical technology, namely Single Snapshot imaging of Optical Properties (SSOP), used to quantify StO2% in an open surgery experimental gastric conduit (GC) model. After the creation of a gastric conduit, local StO2% was measured with a preclinical SSOP system for 60 min in the antrum (ROI-A), corpus (ROI-C), and fundus (ROI-F). The removed region (ROI-R) acted as ischemic control. ROI-R had statistically significant lower StO2% when compared to all other ROIs at T15, T30, T45, and T60 (p < 0.0001). Local capillary lactates (LCLs) and StO2% correlation was statistically significant (R = −0.8439, 95% CI −0.9367 to −0.6407, p < 0.0001). Finally, SSOP could discriminate resected from perfused regions and ROI-A from ROI-F (the future anastomotic site). In conclusion, SSOP could well be a suitable technology to assess intraoperative perfusion of GC, providing consistent StO2% quantification and ROIs discrimination.

scholarly journals REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS

2017 ◽  
Vol IV-2/W3 ◽  
pp. 41-48 ◽  
Author(s):  
A. Milioto ◽  
P. Lottes ◽  
C. Stachniss
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sugar Beet ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Image Data ◽  
High Quality ◽  
Sugar Beets ◽  
Quality Classification

UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.

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