Video Processing Algorithm in Automated Semen Analysis using Optical Flow

2019 ◽  
Author(s):  
Fara Mutia ◽  
Hasballah Zakaria
Keyword(s):  
Optical Flow ◽  
Video Processing ◽  
Semen Analysis ◽  
Processing Algorithm

Massively parallel Lucas Kanade optical flow for real-time video processing applications

2014 ◽  
Vol 11 (4) ◽  
pp. 713-730 ◽  
Author(s):  
Aurélien Plyer ◽  
Guy Le Besnerais ◽  
Frédéric Champagnat
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Video Processing ◽  
Massively Parallel

scholarly journals Measuring Surface Velocity of Water Flow by Dense Optical Flow Method

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2320
Author(s):  
Wu ◽  
Zhao ◽  
Gan ◽  
Ma
Keyword(s):  
Optical Flow ◽  
Water Flow ◽  
Video Processing ◽  
Processing Technology ◽  
Surface Velocity ◽  
Flow Method ◽  
Optical Flow Method ◽  
Dense Optical Flow ◽  
Measuring Surface ◽  
Relative Errors

Recent advances in video processing technology have provided a new approach to measuring the surface velocity of water flow (SVWF). However, most of the previous researches using video processing technology depended on tracers for target tracing, requiring spraying tracers in the measurement process. These methods are not convenient for velocity measurement. In this study, a dense optical flow method (Farneback optical flow method) was used to process the water flow video to get the estimated SVWFs. The estimated SVWFs were verified by the actual SVWFs measured by a portable propeller velocimeter. The regression analyses between the estimated SVWFs and the measured SVWFs were conducted. The coefficient of determinations (R2) of the estimated and the measured SVWFs in different test regions are between 0.81 and 0.85. The average relative errors of the estimated and the measured SVWFs in all test regions are no more than 6.5%. The results indicate that the method had a good accuracy in estimating the SVWF and is a feasible and promising approach to analyzing the surface velocity distribution of water flow.

scholarly journals Photoplethysmography for demarcation of cutaneous squamous cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Mylius Rasmussen ◽  
Thomas Nielsen ◽  
Sofie Hody ◽  
Henrik Hager ◽  
Lars Peter Schousboe
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Video Processing ◽  
Processing Algorithm ◽  
Surgical Removal ◽  
Healthy Skin ◽  
Video Recordings ◽  
Remote Photoplethysmography ◽  
Significant Difference

AbstractA video processing algorithm designed to identify cancer suspicious skin areas is presented here. It is based on video recordings of squamous cell carcinoma in the skin. Squamous cell carcinoma is a common malignancy, normally treated by surgical removal. The surgeon should always balance sufficient tissue removal against unnecessary mutilation, and therefore methods for distinction of cancer boundaries are wanted. Squamous cell carcinoma has angiogenesis and increased blood supply. Remote photoplethysmography is an evolving technique for analysis of signal variations in video recordings in order to extract vital signs such as pulsation. We hypothesize that the remote photoplethysmography signal inside the area of a squamous cell carcinoma is significantly different from the surrounding healthy skin. Based on high speed video recordings of 13 patients with squamous cell carcinoma, we have examined temporal signal differences in cancer areas versus healthy skin areas. A significant difference in temporal signal changes between cancer areas and healthy areas was found. Our video processing algorithm showed promising results encouraging further investigation to clarify how detailed distinctions can be made.

Adaptive Background Image Calculation for Video Sequences Based on Optical Flow

2014 ◽  
Vol 945-949 ◽  
pp. 1820-1824
Author(s):  
Hui Zhu ◽  
Xiao Peng Ji
Keyword(s):  
Optical Flow ◽  
Video Processing ◽  
Moving Objects ◽  
Averaging Method ◽  
Moving Object ◽  
New Method ◽  
Video Sequences ◽  
Background Estimation ◽  
Traffic Video ◽  
Background Image

A new method is proposed to calculate the background in video sequences. The optical flow is estimated to determine the local regions occupied by moving objects. The background image is calculated by an efficient averaging process excluding the moving object regions, which overcomes the foreground-occluding problem in direct averaging method for background estimation. The experiments for traffic video processing prove the method’s effectiveness and robustness.

Sign in / Sign up

Export Citation Format

Share Document