Abstract
Computer stereo vision tries to mimic human vision by grabbing multiple views of the same scene and cognizing it. The stereo correspondence will find out the matching pixels between the two views based on the Lambertian criteria, which results in disparity. The distance of the objects from the camera can be calculated using this disparity. But in the real world scenario, this Lambertian assumption may not work always due to the radiometric variation between the image pairs and the conventional approaches results in erroneous disparity. In this work, for doing the radiometric invariant stereo matching, the simple local binary pattern is used. The correspondence is done by using semi global block matching method, which can handle the depth changes of curved surfaces and slanting surfaces by adding suitable penalty terms. The performance evaluation of the proposed shows lesser error rate in the range of 0.14% - 0.3883% and run time requirement of 0.20 milliseconds only. This radiometric invariant stereo correspondence attains accuracy as that of global method with run time speed as that of local method and is suitable for most of the real time stereo vision applications.
Highlights
Binocular stereo vision a has been playing a major role in computer vision applications such as robotic vision, medical imaging, autonomous vehicles and augmented reality
The real time applications in computer vision faces the trade-off between speed and accuracy, they usually rely on local area based correspondence
This passive range finding system can handle curved surfaces such as a bottle and can be used for the pick and place robots. It can be used as the sensor for the navigation of autonomous robots in unknown environments. This system can be used as robotic vision system for precise assembly in automotive industry
Summary
Binocular stereo vision a has been playing a major role in computer vision applications such as robotic vision, medical imaging, autonomous vehicles and augmented reality. This approach is based on the planar surface assumption, i.e., all the pixels within a window are assumed to be at same distances from the camera They can provide dense disparity map but fail to handle small depth changes such as slanted and curved surfaces. To handle the small and large depth discontinuities of such surfaces, smaller and larger penalty terms have been used to get accurate depth results In this system, the LBP will transform the input stereo pair to radiometric invariant form. This work targets in the formulation of stereo vision system for autonomous robots, which is suitable for various real-time indoor and outdoor applications This passive range finding system can handle curved surfaces such as a bottle and can be used for the pick and place robots. It can be used as the sensor for the navigation of autonomous robots in unknown environments
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