Mitigate compression artifacts for face in video recognition

2020 ◽  
Author(s):  
Xuan Qi ◽  
Chen Liu
Keyword(s):  
Video Recognition ◽  
Compression Artifacts

Segmental multi-way local pooling for video recognition

2013 ◽  
Author(s):  
Ilseo Kim ◽  
Sangmin Oh ◽  
Arash Vahdat ◽  
Kevin Cannons ◽  
A.G. Amitha Perera ◽  
...  
Keyword(s):  
Video Recognition

Use of a visual discrimination model to detect compression artifacts in virtual pathology images

2010 ◽  
Author(s):  
Jeffrey P. Johnson ◽  
Elizabeth A. Krupinski ◽  
Michelle Yan ◽  
Hans Roehrig
Keyword(s):  
Visual Discrimination ◽  
Discrimination Model ◽  
Compression Artifacts

Robustness of copy-move forgery detection under high JPEG compression artifacts

2015 ◽  
Vol 76 (1) ◽  
pp. 1509-1530 ◽  
Author(s):  
Deng-Yuan Huang ◽  
Ching-Ning Huang ◽  
Wu-Chih Hu ◽  
Chih-Hung Chou
Keyword(s):  
Jpeg Compression ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
Compression Artifacts

Direct evaluation of the effect of filtering the chest compression artifacts on the uninterrupted cardiopulmonary resuscitation time

2013 ◽  
Vol 31 (6) ◽  
pp. 910-915 ◽  
Author(s):  
Jesús Ruiz ◽  
Unai Ayala ◽  
Sofía Ruiz de Gauna ◽  
Unai Irusta ◽  
Digna González-Otero ◽  
...  
Keyword(s):  
Cardiopulmonary Resuscitation ◽  
Chest Compression ◽  
Direct Evaluation ◽  
Compression Artifacts

Abstract 17060: Machine Learning and Video Recognition for Automated Detection of Fluid Status in Heart Failure Patients

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Pratik Doshi ◽  
John Tanaka ◽  
Jedrek Wosik ◽  
Natalia M Gil ◽  
Martin Bertran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Point Of Care ◽  
Machine Learning Algorithms ◽  
Right Atrial ◽  
Heart Catheterization ◽  
Heart Failure Patients ◽  
Non Invasive ◽  
Fluid Status ◽  
Video Recognition

Introduction: There is a need for innovative solutions to better screen and diagnose the 7 million patients with chronic heart failure. A key component of assessing these patients is monitoring fluid status by evaluating for the presence and height of jugular venous distension (JVD). We hypothesize that video analysis of a patient’s neck using machine learning algorithms and image recognition can identify the amount of JVD. We propose the use of high fidelity video recordings taken using a mobile device camera to determine the presence or absence of JVD, which we will use to develop a point of care testing tool for early detection of acute exacerbation of heart failure. Methods: In this feasibility study, patients in the Duke cardiac catheterization lab undergoing right heart catheterization were enrolled. RGB and infrared videos were captured of the patient’s neck to detect JVD and correlated with right atrial pressure on the heart catheterization. We designed an adaptive filter based on biological priors that enhances spatially consistent frequency anomalies and detects jugular vein distention, with implementation done on Python. Results: We captured and analyzed footage for six patients using our model. Four of these six patients shared a similar strong signal outliner within the frequency band of 95bpm – 200bpm when using a conservative threshold, indicating the presence of JVD. We did not use statistical analysis given the small nature of our cohort, but in those we detected a positive JVD signal the RA mean was 20.25 mmHg and PCWP mean was 24.3 mmHg. Conclusions: We have demonstrated the ability to evaluate for JVD via infrared video and found a relationship with RHC values. Our project is innovative because it uses video recognition and allows for novel patient interactions using a non-invasive screening technique for heart failure. This tool can become a non-invasive standard to both screen for and help manage heart failure patients.

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