TITLE:
Single-Phase Velocity Determination Based in Video and Sub-Images Processing: An Optical Flow Method Implemented with Support of a Programmed MatLab Structured Script
AUTHORS:
Andreas Nascimento, Edson Da Costa Bortoni, José Luiz Gonçalves, Pedro Antunes Duarte, Mauro Hugo Mathias
KEYWORDS:
Optical Flow, Single-Phase Velocity, Video and Image Processing, Sensing, MatLab Script
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.8 No.6,
June
10,
2015
ABSTRACT: Important in many different sectors of the
industry, the determination of stream velocity has become more and more
important due to measurements precision necessity, in order to determine the
right production rates, determine the volumetric production of undesired fluid,
establish automated controls based on these measurements avoiding
over-flooding or over-production, guaranteeing accurate predictive
maintenance, etc. Difficulties being faced have been the determination of the
velocity of specific fluids embedded in some others, for example, determining
the gas bubbles stream velocity flowing throughout liquid fluid phase. Although
different and already applicable methods have been researched and already
implemented within the industry, a non-intrusive automated way of providing
those stream velocities has its importance, and may have a huge impact in
projects budget. Knowing the importance of its determination, this developed
script uses a methodology of breaking-down real-time videos media into frame images,
analyzing by pixel correlations possible superposition matches for further gas
bubbles stream velocity estimation. In raw sense, the script bases itself in
functions and procedures already available in MatLab, which can be used for
image processing and treatments, allowing the methodology to be implemented.
Its accuracy after the running test was of around 97% (ninety-seven percent);
the raw source code with comments had almost 3000 (three thousand) characters;
and the hardware placed for running the code was an Intel Core Duo 2.13 [Ghz]
and 2 [Gb] RAM memory capable workstation. Even showing good results, it could
be stated that just the end point correlations were actually getting to the
final solution. So that, making use of self-learning functions or neural
network, one could surely enhance the capability of the application to be run
in real-time without getting exhaust by iterative loops.