{ "info": { "author": "Will Welch", "author_email": "github@quietplease.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 2.7", "Topic :: Scientific/Engineering" ], "description": "RASL\n====\n\nAlign linearly correlated images, possibly having gross corruption or occlusions.\n\nDetailed description and installation instructions, along with\nexample code and data, are here: https://github.com/welch/rasl\n\n`rasl` is a python implementation of the batch image alignment technique\ndescribed in:\n\nY. Peng, A. Ganesh, J. Wright, W. Xu, Y. Ma, \"Robust Alignment by\n Sparse and Low-rank Decomposition for Linearly Correlated Images\",\n IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 2011\n\nThe paper describes a technique for aligning images of objects varying\nin illumination and projection, possibly with occlusions (such as\nfacial images at varying angles, some including eyeglasses or\nhair). RASL seeks transformations or deformations that will best\nsuperimpose a batch of images, with pixel accuracy where possible. It\nsolves this problem by decomposing the image matrix into a dense\nlow-rank component (analogous to \"eigenfaces\" in face-recognition\nliterature) combined with a sparse error matrix representing any\nocclusions. 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