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README.md

rasl

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Align linearly correlated images, possibly having gross corruption or occlusions.

rasl is a python implementation of the batch image alignment technique described in:

Y. 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.

Precise alignment like this is required by (or at least improves the performance of) many different facial decomposition and recognition algorithms. RASL is thus a useful preprocessing step for a training set of images, rather than a complete facial extraction/decomposition/recognition system.

The paper, the data used in the paper, and a reference MATLAB implementation are available from the paper's authors at http://perception.csl.illinois.edu/matrix-rank/rasl.html

(This python implementation is based on that MATLAB implementation but is otherwise independent of its authors)

Quick Start

> pip install rasl
> rasl.demo --help
... (rasl help output) ...

This isn't going to me much fun without actual image data. To install in-place along with data and examples, so you can run tests and play with the included data sets:

> git clone git@github.com:welch/rasl.git
> cd rasl
> pip install -e .
> py.test -sv
.... (test output) ...

Examples

You'll need to be in the toplevel rasl directory to run these. The examples use image sets from the paper.

> python examples/dummy.py

dummy screenshot

The "Aligned" panel is the reconstructed low-rank component of the image. Notice how the occusions have been removed. The "Error" panel is the part of the image that is left over after the low-rank component is removed.

> python examples/gore.py

gore screenshot

Successive frames of Al Gore giving a speech. Bump up the --grid option to see more frames (there are 140 of them). It is interesting that Gore's closed eyes in frames 8-10 are deemed "occlusions", and the restored frame in the Aligned panel gives him open eyes!

> python examples/digits.py

digits screenshot

RASL is not just for faces. These are handwritten digits from the MNIST database. They are aligned using only rotation and translation (--euclidean), as in the paper. The other transformation types succeed in aligning the digits, but then zoom out to infinity. I've not determined if this is a flaw in the implementation or a natural behavior of RASL as specified.

Built-in command: rasl.demo

The rasl package ships with a builtin shell command, rasl.demo, that is installed when you install the package. It expects a path to a directory of image files, and has many options (the python examples above simply call rasl.demo with appropriate settings). One caveat: if your images are of different sizes (as in the Al_Gore directory) you'll need specify --crop to trim them all to the same size as they load.

Dependencies

numpy, scipy, scikit-image

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Python implementation of RASL image alignment algorithm

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