Parallel Computation of Event-Based Visual Features Using Relational Graphs

2021 ◽  
Author(s):  
Daniel R. Mendat ◽  
Jonah P. Sengupta ◽  
Drake K. Foreman ◽  
Andreas G. Andreou
Keyword(s):  
Parallel Computation ◽  
Visual Features ◽  
Relational Graphs ◽  
Event Based

scholarly journals Asynchronous Event-Based Multikernel Algorithm for High-Speed Visual Features Tracking

2015 ◽  
Vol 26 (8) ◽  
pp. 1710-1720 ◽  
Author(s):  
Xavier Lagorce ◽  
Cedric Meyer ◽  
Sio-Hoi Ieng ◽  
David Filliat ◽  
Ryad Benosman
Keyword(s):  
High Speed ◽  
Visual Features ◽  
Event Based

scholarly journals Classifying smoke in laparoscopic videos using SVM

2017 ◽  
Vol 3 (2) ◽  
pp. 191-194 ◽  
Author(s):  
Tamer Abdulbaki Alshirbaji ◽  
Nour Aldeen Jalal ◽  
Lars Mündermann ◽  
Knut Möller
Keyword(s):  
Detection Algorithm ◽  
Classification Performance ◽  
Visual Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Smoke Detection ◽  
Lighting Conditions ◽  
Event Based ◽  
Laparoscopic Videos ◽  
Smoke Removal

AbstractSmoke in laparoscopic videos usually appears due to the use of electrocautery when cutting or coagulating tissues. Therefore, detecting smoke can be used for event-based annotation in laparoscopic surgeries by retrieving the events associated with the electrocauterization. Furthermore, smoke detection can also be used for automatic smoke removal. However, detecting smoke in laparoscopic video is a challenge because of the changeability of smoke patterns, the moving camera and the different lighting conditions. In this paper, we present a video-based smoke detection algorithm to detect smoke of different densities such as fog, low and high density in laparoscopic videos. The proposed method depends on extracting various visual features from the laparoscopic images and providing them to support vector machine (SVM) classifier. Features are based on motion, colour and texture patterns of the smoke. We validated our algorithm using experimental evaluation on four laparoscopic cholecystectomy videos. These four videos were manually annotated by defining every frame as smoke or non-smoke frame. The algorithm was applied to the videos by using different feature combinations for classification. Experimental results show that the combination of all proposed features gives the best classification performance. The overall accuracy (i.e. correctly classified frames) is around 84%, with the sensitivity (i.e. correctly detected smoke frames) and the specificity (i.e. correctly detected non-smoke frames) are 89% and 80%, respectively.

Live demonstration: Neuromorphic event-based multi-kernel algorithm for high speed visual features tracking

2014 ◽  
Author(s):  
Xavier Lagorce ◽  
Cedric Meyer ◽  
Sio-Hoi Ieng ◽  
David Filliat ◽  
Ryad Benosman
Keyword(s):  
High Speed ◽  
Visual Features ◽  
Live Demonstration ◽  
Event Based

Information Dispersal and Parallel Computation

1993 ◽  
Author(s):  
Yuh-Dauh Lyuu
Keyword(s):  
Parallel Computation ◽  
Information Dispersal

Differential Processing of Ongoing Task Items for Non-Focal Event-Based Prospective Memory

2012 ◽  
Author(s):  
Shayne D. Loft ◽  
Michael S. Humphreys
Keyword(s):  
Prospective Memory ◽  
Ongoing Task ◽  
Differential Processing ◽  
Focal Event ◽  
Event Based

Individual differences in strength of category-based relations vs. event-based relations

2011 ◽  
Author(s):  
D. Mirman ◽  
K. M. Graziano
Keyword(s):  
Individual Differences ◽  
Event Based

"This Will Be Tough(er)": Influences of Metacognitive Predictions on Cue Monitoring in Event-Based Prospective Memory

2011 ◽  
Author(s):  
Beatrice G. Kuhlmann ◽  
Jan Rummel ◽  
Dayna R. Touron
Keyword(s):  
Prospective Memory ◽  
Event Based

A Comparison of Time-Based and Event-Based Prospective Memory: Effects of Delay

2014 ◽  
Author(s):  
Angela Conte ◽  
Dawn McBride
Keyword(s):  
Prospective Memory ◽  
Memory Effects ◽  
Event Based

Reduced change blindness for task-relevant visual features during a social interaction

2001 ◽  
Author(s):  
Donald A. Varakin ◽  
Sheena Rogers ◽  
Jeffrey T. Andre ◽  
Susan L. Davis
Keyword(s):  
Social Interaction ◽  
Change Blindness ◽  
Visual Features
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