Seven of 22 superpixel methods (RSS, ERGC, ETPS, CRS, LSC, SH, GMMSP) outperform the SLIC baseline on a composite ranking for deforestation segment generation in the ForestEyes citizen science project.
Mahotas: Open source software for scriptable computer vision
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abstract
Mahotas is a computer vision library for Python. It contains traditional image processing functionality such as filtering and morphological operations as well as more modern computer vision functions for feature computation, including interest point detection and local descriptors. The interface is in Python, a dynamic programming language, which is very appropriate for fast development, but the algorithms are implemented in C++ and are tuned for speed. The library is designed to fit in with the scientific software ecosystem in this language and can leverage the existing infrastructure developed in that language. Mahotas is released under a liberal open source license (MIT License) and is available from (http://github.com/luispedro/mahotas) and from the Python Package Index (http://pypi.python.org/pypi/mahotas).
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Exploring Superpixel Segmentation Methods in the Context of Citizen Science and Deforestation Detection
Seven of 22 superpixel methods (RSS, ERGC, ETPS, CRS, LSC, SH, GMMSP) outperform the SLIC baseline on a composite ranking for deforestation segment generation in the ForestEyes citizen science project.