Joint Categorization and Segmentation (JCaS) is a framework for image segmentation that combines bottom-up and top-down cues in a principled manner using Conditional Random Fields (CRFs). For more information, please visit the corresponding Object Categorization and Segmentation research page.
We provide a Matlab implementation of the research contributions made in this field by members in the VisionLab here. For any questions, comments or bugs, please contact msid [at] cis [dot] jhu [dot] edu.
GPCA (Generalized Principal Component Analysis) is an algebraic, non-iterative algorithm for segmenting points lying in an union of subspaces. For more information, please visit the corresponding GPCA research page.
We provide a MATLAB implementation of GPCA with Polynomial Differentiation and spectral clustering for subspace classification.
We provide a MATLAB implementation of Local Subspace Analysis and RANSAC for for subspace classification.
Sparse Modeling Representative Selection (SMRS) is an algorithm based on sparse multiple-measurement-vector recovery theory for selecting a subset of data points as the representatives.
Structured-Sparse Subspace Classification is an algorithm based on block-sparse representation techniques (also known as Block Sparse Subspace Clustering (BSSC)) for classifying multi-subspace data, where the training data in each class lie in a union of subspaces.
Please contact Ehsan Elhamifar ( ehsan [at] cis [dot] jhu [dot] edu ) for questions and comments regarding the code. For more information please visit Ehsan's research page .
The problem of segmenting features tracked in a video with multiple objects can be viewed as the problem of segmenting points lying in an union of subspaces. Many machine learning algorithms can therefore be used to solve this problem (see the Motion Segmentation research page for more information on this topic).
We provide MATLAB implementation of three subspace separation and motion segmentation algorithms:
This is the code for computing the alignment distances between linear dynamical systems as described in reference . The code is implemented in MATLAB but also uses compiled MATLAB executed files (.mex files). Instructions for compiling .mex files from C++ source code is provided in README.odt. Note that the computation of f1 corresponds to the computation of the distance squared as described in reference . The stochastic alignment distances f2 and f3 are described in reference .
In case of questions, comments, bugs or errors, please contact Nicolas D. Jimenez at nicodjimenez (at) gmail (dot) comDownload Code for Dynamical Systems Distances