Tuwo 1.0

Computer vision is a subfield of computer science and deals with systems that can make sense of image information.
Building systems suited for high-level computer vision tasks such as object recognition and image classification requires the use of robust image statistics in the form of image features and machine learning algorithms to separate discriminative information from noise.
Tuwo is a C++ library that provides code to extract image features and learn decision functions from training data. Tuwo is suitable for high-level computer vision tasks.
The library should be used freely; in particular, extracting code as you deem necessary is encouraged. Tuwo is not intended to be a single monolithic library. To encourage free use, the library is licensed under the very liberal open-source MIT License.

Main features:
Machine Learning:
- k-means clustering
- nn/soft vector quantization and histograms
- mean shift clustering
- randomized decision tree ensembles
- structured support vector machine training, parallel
- conditional random field, MAP-MRF inference
Image Features:
- adaptive color histograms (CIE LAB, HSV, RGB)
- canny edge detection
- histogram of oriented gradients (HoG)
- local binary patterns (LBP)
- local self-similarity (LSS)
- oriented gradient histograms
- semantic texton forests
- region covariance
- textons
High-level Image Processing:
- mean shift segmentation
- tree-based segmentation (FH)
- normalized cut image segmentation
- image laplacians: matting laplacian, intervening contours, gradient
Input/Output:
- PASCAL VOC challenge XML metafile reader/writer