This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
Latest Version Dependencies status unknown Test status unknown Test coverage unknown
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:

  1. 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

Release History

Release History

0.1.0

This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

0.0.0

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
rasl-0.1.0-py2.py3-none-any.whl (18.5 kB) Copy SHA256 Checksum SHA256 2.7 Wheel Apr 25, 2016
rasl-0.1.0.tar.gz (13.6 kB) Copy SHA256 Checksum SHA256 Source Apr 25, 2016

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS HPE HPE Development Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting