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Code Available to Download


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.
For comments, please contact Saket Anand or Sushil Mittal.

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..
For comments, please contact Sushil Mittal or Saket Anand.

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.
For comments, please contact Raghav Subbarao or Sushil Mittal.


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.
For comments, please contact Bogdan Georgescu or Chris M. Christoudias.

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.
For comments, please contact Bogdan Georgescu or Ilan Shimshoni.


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.
For comments, please contact Bogdan Georgescu.

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.
For comments, please contact Bogdan Georgescu.