Component-Hypertrees for Image Segmentation

This article introduces the notion of component-hypertree, which models the component-trees of an image at various connectivity levels, and the relations of the nodes/connected components between these levels. This data structure is then used to extend a recently proposed interactive segmentation method based on component-trees. In this multiscale connectivity context, the use of a component-hypertree appears to be less costly than the use of several component-trees. Application examples illustrate the relevance of this approach.

[1]  Philippe Salembier.  Connected Operators based on Tree Pruning Strategies , 2013 .

[2]  Michael H. F. Wilkinson,et al.  Vector-Attribute Filters , 2005, ISMM.

[3]  Jean Paul Frédéric Serra,et al.  Connectivity on Complete Lattices , 1998, Journal of Mathematical Imaging and Vision.

[4]  Michael H. F. Wilkinson,et al.  Shape Preserving Filament Enhancement Filtering , 2001, MICCAI.

[5]  Laurent Wendling,et al.  Combining Shape Descriptors and Component-tree for Recognition of Ancient Graphical Drop Caps , 2009, VISAPP.

[6]  Azriel Rosenfeld,et al.  Digital topology: Introduction and survey , 1989, Comput. Vis. Graph. Image Process..

[7]  Ulisses Braga-Neto,et al.  Object-based image analysis using multiscale connectivity , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Nicolas Passat,et al.  Selection of Relevant Nodes from Component-Trees in Linear Time , 2011, DGCI.

[9]  Pierre Soille,et al.  Constrained connectivity for hierarchical image partitioning and simplification , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Michael H. F. Wilkinson,et al.  Automatic Attribute Threshold Selection for Blood Vessel Enhancement , 2010, 2010 20th International Conference on Pattern Recognition.

[11]  Michael H. F. Wilkinson,et al.  Connected Shape-Size Pattern Spectra for Rotation and Scale-Invariant Classification of Gray-Scale Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Nicolas Passat,et al.  Segmentation using vector-attribute filters: methodology and application to dermatological imaging , 2007, ISMM.

[13]  Hugues Talbot,et al.  Mathematical Morphology: from theory to applications , 2013 .

[14]  E. R. Urbach,et al.  Shape-only granulometries and gray-scale shape filters , 2002 .

[15]  Michael H. F. Wilkinson,et al.  Mathematical Morphology and Its Application to Signal and Image Processing, 9th International Symposium, ISMM 2009, Groningen, The Netherlands, August 24-27, 2009 Proceedings , 2009, ISMM.

[16]  Michael H. F. Wilkinson,et al.  Mask-Based Second-Generation Connectivity and Attribute Filters , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Michel Couprie,et al.  Building the Component Tree in Quasi-Linear Time , 2006, IEEE Transactions on Image Processing.

[18]  Emmanuel Bertin,et al.  Effective Component Tree Computation with Application to Pattern Recognition in Astronomical Imaging , 2007, 2007 IEEE International Conference on Image Processing.

[19]  Ronald Jones,et al.  Connected Filtering and Segmentation Using Component Trees , 1999, Comput. Vis. Image Underst..

[20]  Wiro J. Niessen,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001 , 2001, Lecture Notes in Computer Science.

[21]  Ulisses Braga-Neto,et al.  A multiscale approach to connectivity , 2003, Comput. Vis. Image Underst..

[22]  Nicolas Passat,et al.  Segmentation of Complex Images Based on Component-Trees: Methodological Tools , 2009, ISMM.