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.

Real-time image style transformation based on deep learning

2018 ◽  
Vol 27 (04) ◽  
pp. 1
Author(s):  
Xianlin Zhang ◽  
Yixin Luan ◽  
Xueming Li
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Real Time Image ◽  
Time Image

scholarly journals Advances in deep learning for real-time image and video reconstruction and processing

2020 ◽  
Vol 17 (6) ◽  
pp. 1883-1884
Author(s):  
Pourya Shamsolmoali ◽  
M. Emre Celebi ◽  
Ruili Wang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Real Time Image ◽  
Video Reconstruction ◽  
Time Image

scholarly journals Real-Time Color Image Classification Based On Deep Learning Network

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Mohammed Hamzah Abed ◽  
Atheer Hadi Issa Al-Rammahi ◽  
Mustafa Jawad Radif
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Real Time ◽  
Color Image ◽  
Input Image ◽  
Proposed Model ◽  
Real Time Image ◽  
Deep Learning Network ◽  
Fully Connected ◽  
Time Image

Real-time image classification is one of the most challenging issues in understanding images and computer vision domain. Deep learning methods, especially Convolutional Neural Network (CNN), has increased and improved the performance of image processing and understanding. The performance of real-time image classification based on deep learning achieves good results because the training style, and features that are used and extracted from the input image. This work proposes an interesting model for real-time image classification architecture based on deep learning with fully connected layers to extract proper features. The classification is based on the hybrid GoogleNet pre-trained model. The datasets that are used in this work are 15 scene and UC Merced Land-Use datasets, used to test the proposed model. The proposed model achieved 92.4 and 98.8 as a higher accuracy.

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 Deep learning approaches for real-time image super-resolution

2020 ◽  
Vol 32 (18) ◽  
pp. 14519-14520
Author(s):  
Pourya Shamsolmoali ◽  
M. Emre Celebi ◽  
Ruili Wang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Super Resolution ◽  
Learning Approaches ◽  
Real Time Image ◽  
Image Super Resolution ◽  
Time Image
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