Open AccessNon parametric methods of disparity computation
Author(s) -
Priya Charles,
Anupama V. Patil
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.6.10062
Subject(s) - computer science , artificial intelligence , computer vision , stereopsis , parametric statistics , computation , computer stereo vision , algorithm , mathematics , statistics
Disparity is inversely proportional to depth. Informationabout depth is a key factor in many real time applicationslikecomputer vision applications, medical diagnosis, model precision etc. Disparity is measured first in order to calculate the depth that suitsthe real world applications. There are two approaches viz., active and passive methods. Due to its cost effectiveness, passive approach is the most popular approach. In spite of this, the measures arelimited by its occlusion, more number of objects and texture areas. So, effective and efficient stereo depth estimation algorithms have taken the toll on the researchers. Theimportant goal of stereo vision algorithms is the disparity map calculation between twoimages clicked the same time. These pictures are taken using two cameras. We have implemented the non-parametric algorithmsfor stereo vision viz., Rank and Census transform in both single processor and multicore processors are implemented andthe results showsits time efficient by 1500 times.