Multiple computational backend support
While scikits.image is currently built on NumPy, leveraging the graphical processing unit (GPU) by using PyOpenCL (http://mathema.tician.de/software/pyopencl) would provide a significant speed increase for certain parallelizable algorithms.
Similarly, other libraries such as CorePy (http://www.corepy.org) and Theano (http://deeplearning.net/software/theano) provide benefits over NumPy under certain circumstances.
To leverage these libraries, a backend system will be implemented whereby algorithms can easily be run on different libraries or devices. For example, we could execute a color conversion on the GPU as:
from scikits.image import backend
rgb2hsv(x, backend=backend('opencl'))
Video Input and Output
The scikit has excellent input/output plugin facilities, but currently does not support video reading, writing or display. Existing open source video reader libraries will be wrapped using Cython (http://cython.org), whereafter the existing ImageCollection class can be modified to display them. The possibility of wrapping a camera acquisition library can be investigated.
Image Processing Algorithms
The number of algorithms provided by the library is currently limited. The addition of certain key algorithms would increase its usefulness dramatically. I propose adding the following algorithms, implemented in Cython:
Feature detection (probabilistic Hough transform, STAR, FAST)
Segmentation (graph cuts and watershed)
Geometrical transformations (distortion removal)
This goal seems ambitious, but for many algorithms existing wrappers exist which may be re-used, for example code from CellProfiler at MIT’s Broad Institute (released under a BSD license).
Documentation
To improve adoption of the project, I’d like to write an introductory tutorial and improve the existing documentation.
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