A Scalable Architecture for Real-Time Synthetic-Focus Imaging

2003 ◽  
Vol 25 (3) ◽  
pp. 151-161
Author(s):  
William D. Richard
Keyword(s):  
Real Time ◽  
Image Generation ◽  
Image Size ◽  
Scalable Architecture ◽  
Data Set ◽  
Channel System ◽  
Real Time Image ◽  
Available Technology ◽  
Image Computation ◽  
Time Image

A scalable architecture for forming real-time synthetic focus images is described and the design of a 256-channel system using currently-available technology is presented as an example implementation of the architecture. The parallelism of the system scales directly with the number of array elements and the image computation rate for a given image size (in pixels) stays constant as the number of array elements is increased. The system leverages earlier work in the real-time generation of the required time-of-flight surfaces and allows either real-time image generation or iterative adaptive image generation from a single complete data set.

scholarly journals Real Time Attendance Marking System

2020 ◽  
Vol 5 (8) ◽  
pp. 1112-1116
Author(s):  
Riya John ◽  
Akhilesh. s ◽  
Gayathri Geetha Nair ◽  
Jeen Raju ◽  
Krishnendhu. B
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Educational Institution ◽  
Rfid Tags ◽  
Learning Technology ◽  
Business Organizations ◽  
Data Set ◽  
Real Time Image ◽  
Time Image ◽  
Excel File

Attendance management is an important procedure in an educational institution as well as in business organizations. Most of the available methods are time consuming and manipulative. The traditional method of attendance management is carried out in handwritten registers. Other than the manual method, there exist biometric methods like fingerprint and retinal scan, RFID tags, etc. All of these methods have disadvantages, therefore, in order to avoid these difficulties here, we introduce a new method for attendance management using deep learning technology. Using deep learning we can easily train a data-set. Real-time face algorithms are used and recognized faces of students in real-time while attending lectures. This system aims to be less time- consuming in comparison to the existing system of marking attendance.The program runs on anaconda flask server.Here real time image is captured using mobile phone camera. The faces on the image of the persons are then recognized and attendance is marked on an excel file.

A real time image generation system and its applications

1990 ◽  
Vol 36 (3) ◽  
pp. 216-222
Author(s):  
H. Ishida ◽  
N. Iwata ◽  
K. Nemoto
Keyword(s):  
Real Time ◽  
Image Generation ◽  
Generation System ◽  
Real Time Image ◽  
Time Image

scholarly journals Real-time Image Generation for Compressive Light Field Displays

2013 ◽  
Vol 415 ◽  
pp. 012045 ◽  
Author(s):  
G Wetzstein ◽  
D Lanman ◽  
M Hirsch ◽  
R Raskar
Keyword(s):  
Real Time ◽  
Light Field ◽  
Image Generation ◽  
Real Time Image ◽  
Time Image

Inexpensive real-time image generation and control

1977 ◽  
Vol 11 (1) ◽  
pp. 41-46 ◽  
Author(s):  
Bill Etra ◽  
Lou Katz
Keyword(s):  
Real Time ◽  
Image Generation ◽  
Real Time Image ◽  
And Control ◽  
Time Image

Real-time image generation with a pulsed coherent laser radar

1997 ◽  
Author(s):  
Francis J. Corbett ◽  
Michael Groden ◽  
Gordon L. Dryden ◽  
Mark A. Kovacs ◽  
George Pfeiffer
Keyword(s):  
Real Time ◽  
Laser Radar ◽  
Image Generation ◽  
Real Time Image ◽  
Coherent Laser Radar ◽  
Time Image

scholarly journals Kinect-Based Real-Time Acquisition Algorithm of Crop Growth Depth Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shan Hua ◽  
Minjie Xu ◽  
Zhifu Xu ◽  
Hongbao Ye ◽  
Cheng quan Zhou
Keyword(s):  
Real Time ◽  
Image Acquisition ◽  
Depth Map ◽  
Crop Growth ◽  
Image Generation ◽  
The Real ◽  
Real Time Image ◽  
Time Image ◽  
Time Acquisition ◽  
Generation Technology

Kinect 3D sensing real-time acquisition algorithm that can meet the requirements of fast, accurate, and real-time acquisition of image information of crop growth laws has become the trend and necessary means of digital agricultural production management. Based on this, this paper uses Kinect real-time image generation technology to try to monitor and study the depth map of crop growth law in real time, use Kinect to obtain the algorithm of crop growth depth map, and conduct investigation and research. Real-time image acquisition research on crop growth trends provides a basis for in-depth understanding of the application of Kinect real-time image generation technology in research. Kinect image real-time acquisition algorithm is a very important information carrier in agricultural information engineering. The research results show that the real-time Kinect depth image acquisition algorithm can obtain good 3D image data information and can provide valuable data basis for the 3D reconstruction of the later crop growth model, growth status analysis, and real-time monitoring of crop diseases. The data shows that, using Kinect, the real-time feedback speed of crop growth observation can be increased by 45%, the imaging accuracy is improved by 37%, and the related operation steps are simplified by 30%. The survey results show that the crop yield can be increased by about 12%.

Microbolometer sensor model for performance predictions and real-time image generation of infrared scenes and targets

2008 ◽  
Author(s):  
Robert A. Richwine ◽  
Yash R. Puri ◽  
Ashok K. Sood ◽  
Raymond S. Balcerak ◽  
Stuart Horn ◽  
...  
Keyword(s):  
Real Time ◽  
Image Generation ◽  
Sensor Model ◽  
Performance Predictions ◽  
Real Time Image ◽  
Time Image
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