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  Natter: A Python Natural Image Statistics Toolbox

Sinz, F., Lies, J.-P., Gerwinn, S., & Bethge, M. (2014). Natter: A Python Natural Image Statistics Toolbox. Journal of Statistical Software, 61(5), 1-34.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-7F99-E Version Permalink: http://hdl.handle.net/21.11116/0000-0000-FDDA-E
Genre: Journal Article

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http://www.jstatsoft.org/v61/i05 (Publisher version)
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 Creators:
Sinz, FH1, Author              
Lies, J-P1, Author              
Gerwinn, S, Author              
Bethge, M1, Author              
Affiliations:
1University of Tübingen, ou_persistent22              

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 Abstract: The statistical analysis and modeling of natural images is an important branch of statistics with applications in image signaling, image compression, computer vision, and human perception. Because the space of all possible images is too large to be sampled exhaustively, natural image models must inevitably make assumptions in order to stay tractable. Subsequent model comparison can then filter out those models that best capture the statistical regularities in natural images. Proper model comparison, however, often requires that the models and the preprocessing of the data match down to the implementation details. Here we present the Natter, a statistical software toolbox for natural images models, that can provide such consistency. The Natter includes powerful but tractable baseline model as well as standardized data preprocessing steps. It has an extensive test suite to ensure correctness of its algorithms, it interfaces to the modular toolkit for data processing toolbox MDP, and provides simple ways to log the results of numerical experiments. Most importantly, its modular structure can be extended by new models with minimal coding effort, thereby providing a platform for the development and comparison of probabilistic models for natural image data.

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 Dates: 2014-11
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: SinzLGB2014
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Title: Journal of Statistical Software
Source Genre: Journal
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Pages: - Volume / Issue: 61 (5) Sequence Number: - Start / End Page: 1 - 34 Identifier: -