Real‐time area‐tracker records cellular volume changes from video images

1984 ◽  
Vol 55 (11) ◽  
pp. 1788-1790 ◽  
Author(s):  
Bernd Lindemann
Keyword(s):  
Real Time ◽  
Volume Changes ◽  
Video Images ◽  
Cellular Volume

Design of FPGA-Based Missile-Borne Image Acquisition and Storage Recorder

2015 ◽  
Vol 713-715 ◽  
pp. 1448-1451
Author(s):  
Lin Lu ◽  
Yan Feng Zhang ◽  
Xiao Feng Li
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Storage System ◽  
Large Data ◽  
Engineering Practice ◽  
Image Storage ◽  
Video Images ◽  
Stable Performance ◽  
And Storage ◽  
Time Image

The high-altitude missile and other special application occasions have requirements on image storage system, such as small size, high storage speed, low temperature resistance, etc. Commonly used image storage system in the market cannot meet such requirement. In the paper, real-time image storage system solutions on missile based on FPGA should be proposed. The system mainly consists of acquisition module and memory reading module. The whole system adopts FPGA as main control chip for mainly completing real-time decoding and acquisition on one path of PAL format video images, reading and writing of NandFlash chipset, erasure, bad block management and so on. The solution has passed various environmental tests with stable performance, large data storage capacity and easy expansion, which has been used in engineering practice.

scholarly journals A Real Time Mosaic Method for Remote Sensing Video Images from UAV

2013 ◽  
Vol 04 (03) ◽  
pp. 168-172 ◽  
Author(s):  
Yang Yang ◽  
Guangmin Sun ◽  
Dequn Zhao ◽  
Bo Peng
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Video Images

scholarly journals Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model

Gut ◽  
2017 ◽  
Vol 68 (1) ◽  
pp. 94-100 ◽  
Author(s):  
Michael F Byrne ◽  
Nicolas Chapados ◽  
Florian Soudan ◽  
Clemens Oertel ◽  
Milagros Linares Pérez ◽  
...  
Keyword(s):  
Real Time ◽  
Predictive Value ◽  
Narrow Band Imaging ◽  
Colorectal Polyps ◽  
Hyperplastic Polyps ◽  
Video Images ◽  
Video Frames ◽  
Endoscopic Video ◽  
Design And Methods ◽  
Standard Colonoscopy

BackgroundIn general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the ‘resect and discard’ paradigm for diminutive colorectal polyps workable. Computer analysis of video could potentially eliminate the obstacle of interobserver variability in endoscopic polyp interpretation and enable widespread acceptance of ‘resect and discard’.Study design and methodsWe developed an artificial intelligence (AI) model for real-time assessment of endoscopic video images of colorectal polyps. A deep convolutional neural network model was used. Only narrow band imaging video frames were used, split equally between relevant multiclasses. Unaltered videos from routine exams not specifically designed or adapted for AI classification were used to train and validate the model. The model was tested on a separate series of 125 videos of consecutively encountered diminutive polyps that were proven to be adenomas or hyperplastic polyps.ResultsThe AI model works with a confidence mechanism and did not generate sufficient confidence to predict the histology of 19 polyps in the test set, representing 15% of the polyps. For the remaining 106 diminutive polyps, the accuracy of the model was 94% (95% CI 86% to 97%), the sensitivity for identification of adenomas was 98% (95% CI 92% to 100%), specificity was 83% (95% CI 67% to 93%), negative predictive value 97% and positive predictive value 90%.ConclusionsAn AI model trained on endoscopic video can differentiate diminutive adenomas from hyperplastic polyps with high accuracy. Additional study of this programme in a live patient clinical trial setting to address resect and discard is planned.

Real time correlator of video-images

1982 ◽  
Vol 13 (6) ◽  
pp. 345-358 ◽  
Author(s):  
M Azouit
Keyword(s):  
Real Time ◽  
Video Images

scholarly journals A Phlegm Stagnation Monitoring Based on VDS Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhiguo Gao ◽  
Xin Yu
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Moving Objects ◽  
Recognition Rate ◽  
Differential Method ◽  
Video Frame ◽  
Moving Targets ◽  
System A ◽  
Video Images ◽  
Svm Algorithm

In the nonmedical sputum monitoring system, a practical solution for phlegm stagnation care of patients was proposed. Through the camera, the video images of patients’ laryngeal area were obtained in real time. After processing and analysis on these video frame images, the throat movement area was found out. A three-frame differential method was used to detect the throat moving targets. Anomalies were identified according to the information of moving targets and the proposed algorithm. Warning on the abnormal situation can help nursing personnel to deal with sputum blocking problem more effectively. To monitor the patients’ situation in real time, this paper proposed a VDS algorithm, which extracted the speed characteristics of moving objects and combined with the DTW algorithm and SVM algorithm for sequence image classification. Phlegm stagnation symptoms of patients were identified timely for further medical care. In order to evaluate the effectiveness, our method was compared with the DTW, SVM, CTM, and HMM methods. The experimental results showed that this method had a higher recognition rate and was more practical in a nonmedical monitoring system.

scholarly journals Left atrial Frank Starling law assessed by real-time, three-dimensional echocardiographic left atrial volume changes

Heart ◽  
2007 ◽  
Vol 93 (11) ◽  
pp. 1393-1397 ◽  
Author(s):  
A. M Anwar ◽  
M. L Geleijnse ◽  
O. I I Soliman ◽  
A. Nemes ◽  
F. J t. Cate
Keyword(s):  
Real Time ◽  
Left Atrial ◽  
Three Dimensional ◽  
Left Atrial Volume ◽  
Atrial Volume ◽  
Volume Changes

Depth-Based Real-Time Gait Recognition

2020 ◽  
Vol 29 (16) ◽  
pp. 2050266
Author(s):  
Adnan Ramakić ◽  
Diego Sušanj ◽  
Kristijan Lenac ◽  
Zlatko Bundalo
Keyword(s):  
Real Time ◽  
Gait Recognition ◽  
Video Stream ◽  
Outdoor Environment ◽  
Microsoft Kinect ◽  
Recognition Method ◽  
Real Time Processing ◽  
Depth Data ◽  
Video Images ◽  
Subject Identification

Each person describes unique patterns during gait cycles and this information can be extracted from live video stream and used for subject identification. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. In this paper, a method to enhance the appearance-based gait recognition method by also integrating features extracted from depth data is proposed. Two approaches are proposed that integrate simple depth features in a way suitable for real-time processing. Unlike previously presented works which usually use a short range sensors like Microsoft Kinect, here, a long-range stereo camera in outdoor environment is used. The experimental results for the proposed approaches show that recognition rates are improved when compared to existing popular gait recognition methods.

Sign in / Sign up

Export Citation Format

Share Document