Extending the Power Watershed Framework Thanks to Γ-Convergence

In this paper, we provide a formal proof of the power-watershed framework relying on the Γ-convergence framework. The main ingredient for the proof is a concept of scale. The proof and the formalism introduced in this paper have the added benefit to clarify the algorithm, and to allow to extend the applicability of the power watershed algorithm to many other types of energy functions. Several examples of applications are provided, including Total Variation and Spectral Clustering.

[1]  Jayaram K. Udupa,et al.  Fuzzy Connectedness Image Segmentation in Graph Cut Formulation: A Linear-Time Algorithm and a Comparative Analysis , 2012, Journal of Mathematical Imaging and Vision.

[2]  Chung-Kuan Cheng,et al.  Towards efficient hierarchical designs by ratio cut partitioning , 1989, 1989 IEEE International Conference on Computer-Aided Design. Digest of Technical Papers.

[3]  Laurent Najman,et al.  On the Equivalence Between Hierarchical Segmentations and Ultrametric Watersheds , 2010, Journal of Mathematical Imaging and Vision.

[4]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[5]  J. Kruskal On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[6]  Charles T. Zahn,et al.  Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters , 1971, IEEE Transactions on Computers.

[7]  Camille Couprie,et al.  Anisotropic diffusion using power watersheds , 2010, 2010 IEEE International Conference on Image Processing.

[8]  Jean Stawiaski,et al.  Minimum spanning tree adaptive image filtering , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[9]  Hervé Le Men,et al.  Scale-Sets Image Analysis , 2005, International Journal of Computer Vision.

[10]  Michel Couprie,et al.  Some links between extremum spanning forests, watersheds and min-cuts , 2010, Image Vis. Comput..

[11]  Gilbert Strang,et al.  Maximal flow through a domain , 1983, Math. Program..

[12]  Thierry Géraud,et al.  A Comparative Review of Component Tree Computation Algorithms , 2014, IEEE Transactions on Image Processing.

[13]  Gilles Bertrand,et al.  Watershed Cuts: Minimum Spanning Forests and the Drop of Water Principle , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Camille Couprie,et al.  Power Watershed: A Unifying Graph-Based Optimization Framework , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Serge Beucher,et al.  Watershed, Hierarchical Segmentation and Waterfall Algorithm , 1994, ISMM.

[16]  Jaroslav Nesetril,et al.  Otakar Boruvka on minimum spanning tree problem Translation of both the 1926 papers, comments, history , 2001, Discret. Math..

[17]  Patrick L. Combettes,et al.  Proximal Splitting Methods in Signal Processing , 2009, Fixed-Point Algorithms for Inverse Problems in Science and Engineering.

[18]  Sisto Baldo,et al.  Asymptotic development by Γ-convergence , 1993 .

[19]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Makoto Nagao,et al.  Region extraction and shape analysis in aerial photographs , 1979 .

[21]  Jean Paul Frédéric Serra,et al.  Global-local optimizations by hierarchical cuts and climbing energies , 2013, Pattern Recognit..

[22]  Abderrahim Elmoataz,et al.  Nonlocal Discrete Regularization on Weighted Graphs: A Framework for Image and Manifold Processing , 2008, IEEE Transactions on Image Processing.

[23]  Serge Beucher,et al.  The Morphological Approach to Segmentation: The Watershed Transformation , 2018, Mathematical Morphology in Image Processing.

[24]  G. D. Maso,et al.  An Introduction to-convergence , 1993 .

[25]  Y. D. Verdière,et al.  SINGULAR LIMITS OF SCHRÖDINGER OPERATORS AND MARKOV PROCESSES , 2007 .

[26]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[27]  C. Berge Topological Spaces: including a treatment of multi-valued functions , 2010 .

[28]  Yann Gousseau,et al.  The TVL1 Model: A Geometric Point of View , 2009, Multiscale Model. Simul..

[29]  Jérôme Darbon,et al.  Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization , 2006, Journal of Mathematical Imaging and Vision.

[30]  Benjamin Perret,et al.  Playing with Kruskal: Algorithms for Morphological Trees in Edge-Weighted Graphs , 2013, ISMM.

[31]  Jean Cousty,et al.  Incremental Algorithm for Hierarchical Minimum Spanning Forests and Saliency of Watershed Cuts , 2011, ISMM.

[32]  Gang Wang,et al.  Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree , 2014, IEEE Transactions on Image Processing.

[33]  Etienne Decencière,et al.  Image filtering using morphological amoebas , 2007, Image Vis. Comput..