scholarly journals Framework of Object Detection and Classification High Performance Using Video Analytics in Cloud

2018 ◽  
Vol Volume-3 (Issue-1) ◽  
pp. 1-8
Author(s):  
R. Gnana bharathy ◽  
Keyword(s):  
Object Detection ◽  
High Performance ◽  
Video Analytics

Video Stream Analysis in Clouds: An Object Detection and Classification Framework for High Performance Video Analytics

2019 ◽  
Vol 7 (4) ◽  
pp. 1152-1167 ◽  
Author(s):  
Ashiq Anjum ◽  
Tariq Abdullah ◽  
M. Fahim Tariq ◽  
Yusuf Baltaci ◽  
Nick Antonopoulos
Keyword(s):  
Object Detection ◽  
High Performance ◽  
Video Stream ◽  
Video Analytics ◽  
Classification Framework

scholarly journals Object detection networks and augmented reality for cellular detection in fluorescence microscopy

2020 ◽  
Vol 219 (10) ◽  
Author(s):  
Dominic Waithe ◽  
Jill M. Brown ◽  
Katharina Reglinski ◽  
Isabel Diez-Sevilla ◽  
David Roberts ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Fluorescence Microscopy ◽  
Object Detection ◽  
High Performance ◽  
Back Projection ◽  
Detection Algorithms ◽  
High Classification Accuracy ◽  
Fast Processing ◽  
Automate Detection ◽  
Very High

Object detection networks are high-performance algorithms famously applied to the task of identifying and localizing objects in photography images. We demonstrate their application for the classification and localization of cells in fluorescence microscopy by benchmarking four leading object detection algorithms across multiple challenging 2D microscopy datasets. Furthermore we develop and demonstrate an algorithm that can localize and image cells in 3D, in close to real time, at the microscope using widely available and inexpensive hardware. Furthermore, we exploit the fast processing of these networks and develop a simple and effective augmented reality (AR) system for fluorescence microscopy systems using a display screen and back-projection onto the eyepiece. We show that it is possible to achieve very high classification accuracy using datasets with as few as 26 images present. Using our approach, it is possible for relatively nonskilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of fluorescence microscopy acquisition pipelines.

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