Natter: A Python Natural Image Statistics Toolbox
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 ine...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2014-11-01
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| Series: | Journal of Statistical Software |
| Online Access: | http://www.jstatsoft.org/index.php/jss/article/view/2193 |
| _version_ | 1831774036708818944 |
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| author | Fabian H. Sinz Jörn-Philipp Lies Sebastian Gerwinn Matthias Bethge |
| author_facet | Fabian H. Sinz Jörn-Philipp Lies Sebastian Gerwinn Matthias Bethge |
| author_sort | Fabian H. Sinz |
| collection | DOAJ |
| description | 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 ?lter 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 e?ort, thereby providing a platform for the development and comparison of probabilistic models for natural image data. |
| first_indexed | 2024-12-22T08:47:04Z |
| format | Article |
| id | doaj.art-4db8139babdd48fc896d0ee63e43d4da |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-22T08:47:04Z |
| publishDate | 2014-11-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-4db8139babdd48fc896d0ee63e43d4da2022-12-21T18:32:04ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602014-11-0161113410.18637/jss.v061.i05797Natter: A Python Natural Image Statistics ToolboxFabian H. SinzJörn-Philipp LiesSebastian GerwinnMatthias BethgeThe 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 ?lter 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 e?ort, thereby providing a platform for the development and comparison of probabilistic models for natural image data.http://www.jstatsoft.org/index.php/jss/article/view/2193 |
| spellingShingle | Fabian H. Sinz Jörn-Philipp Lies Sebastian Gerwinn Matthias Bethge Natter: A Python Natural Image Statistics Toolbox Journal of Statistical Software |
| title | Natter: A Python Natural Image Statistics Toolbox |
| title_full | Natter: A Python Natural Image Statistics Toolbox |
| title_fullStr | Natter: A Python Natural Image Statistics Toolbox |
| title_full_unstemmed | Natter: A Python Natural Image Statistics Toolbox |
| title_short | Natter: A Python Natural Image Statistics Toolbox |
| title_sort | natter a python natural image statistics toolbox |
| url | http://www.jstatsoft.org/index.php/jss/article/view/2193 |
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