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The RANdom SAmple Conesenus algortihm is used to estimate parameters in presence of a large number of outliers. We are investigating in efficient techniques to improve the stability of the estimates and to reduce their bias, even in presence of multiple instances of the same model.

 

with Dr. C. Kenney, Dr. M. Bober and Prof. B. S. Manjunath

(2004-now) In this project we are trying to construct a RANSAC framework that will enable us to perform parameter estimation robustly in different scenarios characterized by the presence of large quantities of outliers. We are developing methods that will speed up the convergence of the traditional algorithm, that will allow us to perform the fusion of information coming from different sources and that can cope with the presence of multiple models. We are also interested in characterizing the stability of the solutions found by RANSAC.

 

Related Publications

  1. M. Zuliani, S. Bhagavathy, B. S. Manjunath, C. S. Kenney,
    "Affine-Invariant Curve Matching"
    IEEE International Conference on Image Processing, Singapore, Oct. 2004.
    VRL ID 138: [abstract] [PDF] [BibTex]

    Abstract preview: "In this paper, we propose an affine-invariant method for describing and matching curves. This is important since affine transformations are often used to model perspective distortions. More specifical..." [more]

  2. M. Zuliani, C. S. Kenney, S. Bhagavathy, B. S. Manjunath,
    "Drums and Curve Descriptors"
    British Machine Vision Conference, Kingston University, London, UK, Sep. 2004.
    VRL ID 134: [abstract] [PDF] [BibTex]

    Abstract preview: "In this paper we present a new physically motivated curve descriptor based on the solution of Helmholtz's equation. The descriptor satisfies the six principles set by MPEG-7: it has a good retrieval a..." [more]

Click here for the ICIP 2005 presentation.