Release Notes: The major new feature in this release is a Python API for training histogram-of-oriented-gradient based object detectors and examples showing how to use this type of detector to perform real-time face detection. Simpler interfaces were provided for learning to solve assignment and multi-target tracking problems.
Release Notes: This release added a tool for training histogram-of-oriented-gradient based object detectors and examples showing how to use this type of detector to perform real-time face detection. It also added multi-threaded training options for the multiclass classifiers as well as numerous other usability improvements.
Release Notes: This release adds bound constrained non-linear optimizers using the BFGS and L-BFGS methods. It also includes a new tool for learning a max-margin Mahalanobis distance metric as well as routines for easily computing Felzenszwalb's 31 channel HOG image representation.
Release Notes: This release focused on improving the speed and usability of dlib's structural support vector machine solver. Two new tutorial style example programs showing how to use the solver from either C++ or Python were included.
Release Notes: This release added a tool for solving large scale support vector regression problems to the library as well as a structural SVM tool for learning BIO or BILOU style sequence tagging models. It also added Python interfaces to a number of dlib's machine learning tools.
Release Notes: This release has primarily focused on improving the flexibility and ease of use of the object detection tools.
Release Notes: In addition to some bugfixes, this release also brings the following notable improvements to the library: a more accurate SURF feature extractor, a faster cutting plane solver, a routine for computing the singular value decomposition of very large matrices, a tool for performing canonical correlation analysis on large datasets, and simple tools for writing parallel for loops.
Release Notes: This release includes a large number of new minor features and usability improvements. It also includes a new machine learning tool for learning to rank objects. This is the dlib::svm_rank_trainer, an implementation of the well known SVM-Rank algorithm. Moreover, the implementation runs in O(n*log(n)) time and is therefore suitable for use with large training datasets.
Release Notes: This release brings a number of new features to the library. Highlights include a probabilistic CKY parser, tools for creating applications using the Bulk Synchronous Parallel computing model, and two new clustering algorithms: Chinese Whispers and Newman's modularity clustering.
Release Notes: In addition to a number of minor usability and feature improvements, this release also gives dlib's object detection tools the ability to model objects with movable parts.