Batch image alignment using the technique described in "Robust Alignment by Sparse and Low-rank Decomposition for Linearly Correlated Images"
Project description
Align linearly correlated images, possibly having gross corruption or occlusions.
Detailed description and installation instructions, along with example code and data, are here: https://github.com/welch/rasl
rasl is a python implementation of the batch image alignment technique described in:
Peng, A. Ganesh, J. Wright, W. Xu, Y. Ma, “Robust Alignment by Sparse and Low-rank Decomposition for Linearly Correlated Images”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 2011
The paper describes a technique for aligning images of objects varying in illumination and projection, possibly with occlusions (such as facial images at varying angles, some including eyeglasses or hair). RASL seeks transformations or deformations that will best superimpose a batch of images, with pixel accuracy where possible. It solves this problem by decomposing the image matrix into a dense low-rank component (analogous to “eigenfaces” in face-recognition literature) combined with a sparse error matrix representing any occlusions. The decomposition is accomplished with a robust form of PCA via Principal Components Pursuit.
Dependencies
numpy, scipy, scikit-image
