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The release 1.2.0 of the Bob signal-processing and machine learning toolbox is available (www.idiap.ch/software/bob)

Bob provides both efficient implementations of several machine learning algorithms as well as a framework to help researchers to publish reproducible research. It is developed by the Biometrics Group ( http://www.idiap.ch/~marcel/professional/Research_Team.html ) at Idiap in Switzerland.

The release 1.2.0 of the Bob signal-processing and machine learning toolbox is available ( www.idiap.ch/software/bob ).

Bob provides both efficient implementations of several machine learning algorithms as well as a framework to help researchers to publish reproducible research.
It is developed by the Biometrics Group ( http://www.idiap.ch/~marcel/professional/Research_Team.html ) at Idiap in Switzerland.

 

The previous release of Bob was providing:
* image, video and audio IO interfaces such as jpg, avi, wav ( http://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/TutorialsIO.html ),
* database accessors such as FRGC, Labelled Face in the Wild, and many others ( http://www.idiap.ch/software/bob/packages/xbob/nightlies/last/sphinx/html ),
* image processing: Local Binary Patterns (LBPs), Gabor Jets, SIFT ( http://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/TutorialsIP.html ),
* machines and trainers ( http://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/TutorialsTrainer.html ) such as Support Vector Machines (SVMs), k-Means,
Gaussian Mixture Models (GMMs), Bayesian intra/extra (personal) classifier, Inter-Session Variability modeling (ISV), Joint Factor Analysis (JFA),
Probabilistic Linear Discriminant Analysis (PLDA).

The new release of Bob has brought the following features and/or improvements, such as:
* Unified implementation of Local Binary Patterns (LBPs),
* Histograms of Oriented Gradients (HOG) implementation,
* Total variability (i-vector) implementation,
* Conjugate gradient based-implementation for logistic regression,
* Improved multi-layer perceptrons implementation (Back-propagation can now be easily used in combination with any optimizer -- i.e L-BFGS),
* Pseudo-inverse-based method for Linear Discriminant Analysis,
* Covariance-based method for Principal Component Analysis,
* Whitening and within-class covariance normalization techniques,
* Module for object detection and keypoint localization (bob.visioner),
* Module for audio processing such as LFCC and MFCC ( http://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/TutorialsAP.html ),
* Improved extensions (satellite packages), that now support both Python and C++ code, within an easy to use framework,
* Improved documentation and add new tutorials,
* Support for Intel's MKL (in addition to ATLAS),
* Extend supported platforms (Arch Linux).

This release represents a major milestone in Bob with plenty of functionality improvements (>640 commits in total: https://github.com/idiap/bob/compare/v1.1.0...v1.2.0 ) and plenty of bug fixes (https://github.com/idiap/bob/issues?page=1&state=closed ).
• Sources and Documentation: https://github.com/idiap/bob/wiki/Releases
• Binary packages (instructions: https://github.com/idiap/bob/wiki/Packages ):
• Ubuntu: 10.04, 12.04, 12.10 and 13.04
• For Mac OSX: works with 10.6 (Snow Leopard), 10.7 (Lion) and 10.8 (Mountain Lion)

For instructions on how to install pre-packaged version on Ubuntu or OSX, consult our quick installation instructions: https://github.com/idiap/bob/wiki/Packages (N.B. OS X macport has not yet been upgraded. This will be done very soon. cf. https://trac.macports.org/ticket/39831 ).

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