These codes of general interest are made available "as it is".

Please acknowledge their use by referring to this webpage.

**
Semi-Supervised Kernel Mean Shift Clustering**

Matlab **code**
to perform mean shift clustering in kernel space by using a few
user-specified pairwise constraints. The theory is described in
**
Semi-Supervised Kernel Mean Shift Clustering**.

**
Generalized Projection based M-estimator**

C++ **code**
to find the robust estimate derived without using any user supplied
scale. The theory is described in
**
Generalized Projection Based M-Estimator: Theory and Applications.**.

**
Nonlinear Mean Shift over Riemannian Manifolds**

C++ **code**
to generalize nonlinear mean shift to data points lying on Riemannian manifolds.
The theory is described in
**
Nonlinear Mean Shift over Riemannian Manifolds**.

**
Edge Detection and Image SegmentatiON (EDISON)
System**

C++ **code**,
can be used through a graphical interface or command line.

The system is described in **
Synergism in low level vision**.

The EDISON system contains the image segmentation/edge preserving
filtering algorithm described in the paper
**Mean shift: A robust approach toward feature space analysis**
and the edge detection algorithm described in the paper
**Edge detection with
embedded confidence**.

**
Adaptive mean shift based clustering **

C++ **code** implementing an
(approximate) mean shift procedure with variable bandwith (in high
dimensions).
The algorithm is described in **
Mean shift based clustering in high dimensions: A texture
classification
example**.

**
Color distribution and optical flow based point matcher**

C++ **code**
to find point correspondences by
matching color distributions computed with spatially oriented kernels and
optical flow registration.
The theory is described in **
Point Matching Under Large Image Deformations and Illumination Changes**.

**
Heteroscedastic Regression**

C++ **code**
implementing the estimation of errors-in-variables models under point
dependent noise. It includes examples for linear, ellipse, fundamental
matrix and trifocal tensor estimation. The theory is described in
**
Estimation of nonlinear errors-in-variables models for computer vision
applications. **