Automated generation of 3D from 2D videos using Manifold Learning - Pippal, Meghna
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Depth estimation from a single monocular image is a difficult problem.The task is even more challenging as depth cues such as motion, stereo correspondences are not present in single image. Hence machine learning based approach for extracting depth information from single image is proposed. Firstly depth is generated by manifold learning in which LLE algorithm is used, it is a non linear method of dimensionality reduction in which neighbors of input set in higher dimensional space are preserved while being transformed into lower dimensional space. The depth maps obtained are further refined by…mehr

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Produktbeschreibung
Depth estimation from a single monocular image is a difficult problem.The task is even more challenging as depth cues such as motion, stereo correspondences are not present in single image. Hence machine learning based approach for extracting depth information from single image is proposed. Firstly depth is generated by manifold learning in which LLE algorithm is used, it is a non linear method of dimensionality reduction in which neighbors of input set in higher dimensional space are preserved while being transformed into lower dimensional space. The depth maps obtained are further refined by fixed point algorithm, it is supervised learning in which those features are extracted from image which have strong correspondences with labels.
Autorenporträt
Meghna Pippal is a senior software developer, living in Bangalore. Evolution of AI techniques to solve day to day task has always interested her. Apart from her work she is currently working on another project which would help to create automated videos for all the new features being released for any given application.