30 September 2011 Monocular depth perception using image processing and machine learning
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Proceedings Volume 8285, International Conference on Graphic and Image Processing (ICGIP 2011); 82850J (2011) https://doi.org/10.1117/12.913252
Event: 2011 International Conference on Graphic and Image Processing, 2011, Cairo, Egypt
Abstract
This paper primarily exploits some of the more obscure, but inherent properties of camera and image to propose a simpler and more efficient way of perceiving depth. The proposed method involves the use of a single stationary camera at an unknown perspective and an unknown height to determine depth of an object on unknown terrain. In achieving so a direct correlation between a pixel in an image and the corresponding location in real space has to be formulated. First, a calibration step is undertaken whereby the equation of the plane visible in the field of view is calculated along with the relative distance between camera and plane by using a set of derived spatial geometrical relations coupled with a few intrinsic properties of the system. The depth of an unknown object is then perceived by first extracting the object under observation using a series of image processing steps followed by exploiting the aforementioned mapping of pixel and real space coordinate. The performance of the algorithm is greatly enhanced by the introduction of reinforced learning making the system independent of hardware and environment. Furthermore the depth calculation function is modified with a supervised learning algorithm giving consistent improvement in results. Thus, the system uses the experience in past and optimizes the current run successively. Using the above procedure a series of experiments and trials are carried out to prove the concept and its efficacy.
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Apoorv Hombali, Vaibhav Gorde, Abhishek Deshpande, "Monocular depth perception using image processing and machine learning", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 82850J (30 September 2011); doi: 10.1117/12.913252; https://doi.org/10.1117/12.913252
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