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Segmentation of Vectorial Image Features Using Shape Gradients and Information Measures

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Abstract

In this paper, we propose to focus on the segmentation of vectorial features (e.g. vector fields or color intensity) using region-based active contours. We search for a domain that minimizes a criterion based on homogeneity measures of the vectorial features. We choose to evaluate, within each region to be segmented, the average quantity of information carried out by the vectorial features, namely the joint entropy of vector components. We do not make any assumption on the underlying distribution of joint probability density functions of vector components, and so we evaluate the entropy using non parametric probability density functions. A local shape minimizer is then obtained through the evolution of a deformable domain in the direction of the shape gradient.

The first contribution of this paper lies in the methodological approach used to differentiate such a criterion. This approach is mainly based on shape optimization tools. The second one is the extension of this method to vectorial data. We apply this segmentation method on color images for the segmentation of color homogeneous regions. We then focus on the segmentation of synthetic vector fields and show interesting results where motion vector fields may be separated using both their length and their direction. Then, optical flow is estimated in real video sequences and segmented using the proposed technique. This leads to promising results for the segmentation of moving video objects.

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Author information

Authors and Affiliations

  1. Laboratoire I3S, CNRS-UNSA, 2000, route des Lucioles, 06903, Sophia Antipolis, France

    Ariane Herbulot & Michel Barlaud

  2. Laboratoire GREYC-Image, 6 Bd Marechal Juin, 14050, Caen, France

    Stéphanie Jehan-Besson & Stefan Duffner

  3. Laboratoire J.A. Dieudonné, CNRS-UNSA,Parc Valrose, 06108, Nice Cedex 2, France

    Gilles Aubert

  4. Now with France Telecom R&D, France

    Stefan Duffner

Authors
  1. Ariane Herbulot
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  2. Stéphanie Jehan-Besson
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  3. Stefan Duffner
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  4. Michel Barlaud
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  5. Gilles Aubert
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Correspondence to Ariane Herbulot.

Additional information

Ariane Herbulot received the M. Engineering degree in computer science from the Ecole Superieure en Sciences Informatiques (ESSI), Sophia Antipolis,France in 2001, and the M.S. degree in computer vision from the University of Nice-Sophia Antipolis (UNSA) in 2003. She is currently a Ph.D. student in image processing with the I3S laboratory, CNRS-UNSA. Her research interests focus on nonparametric methods for image and video segmentation.

Stéphanie Jehan-Besson received the engineering degree from Ecole Centrale Nantes and a Ph.D. in computer vision from the University of Nice Sophia Antipolis. She is currently associate professor at ENSICAEN, engineering school of Caen. Her research interests include variational methods for image segmentation, geometric PDEs (Partial Differential Equations), video object detection for MPEG-4/7, medical image segmentation, motion estimation and tracking.

Stefan Duffner was born in Schorndorf, Germany in 1978. He received the Bachelor's degree in Computer Science from the University of Applied Sciences Konstanz, Germany in 2002 and the Master's degree in Applied Computer Science from the University of Freiburg, Germany in 2004. He's currently pursuing a Ph.D. degree in Computer Science at the Research Laboratory of France Telecom in Rennes, France.

His research interests include machine learning, neural networks and their application to object detection and recognition in images.

Michel Barlaud received his These d'Etat from the University of Paris XII and Agregation de Physique. He is currently a Professor of Image Processing at the University of Nice-Sophia Antipolis, and the leader of the Image Processing group of I3S. His research topics are: Image and Video coding using Wavelet Transform, Inverse problem using Half Quadratic Regularization and, Region Based Image and Video Segmentation using Shape Gradient and Active Contours. He is a regular reviewer for several journals, a member of the technical committees of several scientific conferences. He leads several national research and development projects with French industries, and participates in several international academic collaborations: European Network of Excellence SCHEMA and SIMILAR (Louvain La Neuve (Belgium), ITI Greece, Imperial College ...) and NSF-CNRS Funding (Universities of Stanford and Boston). He is the author of a large number of publications in the area of image and video processing, and the Editor of the book “Wavelets and Image Communication” Elsevier, 1994.

Gilles Aubert received the These d'Etat es-Sciences Mathematiques from the Univesity of Paris 6, France, in 1986. He is currently professor of mathematics at the University of Nice-Sophia Antipolis and member of the J.A. Dieudonne Laboratory at Nice, France. His research interests are calculus of variations, nonlinear partial differential equations. Fields of applications include image processing and, in particular, restoration, segmentation, decomposition models and optical flow.

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Herbulot, A., Jehan-Besson, S., Duffner, S. et al. Segmentation of Vectorial Image Features Using Shape Gradients and Information Measures. J Math Imaging Vis 25, 365–386 (2006). https://doi.org/10.1007/s10851-006-6898-y

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