Tree-Pruning: A New Algorithm and Its Comparative Analysis with the Watershed Transform for Automatic Image Segmentation

Image segmentation using tree pruning (TP) and watershed (WS) has been presented in the framework of the image forest transform (IFT) - a method to reduce image processing problems related to connectivity into an optimum-path forest problem in a graph. Given that both algorithms use the IFT with similar parameters, they usually produce similar segmentation results. However, they rely on different properties of the IFT which make TP more robust than WS for automatic segmentation tasks. We propose and demonstrate an important improvement in the TP algorithm, clarify the differences between TP and WS, and provide their comparative analysis from the theoretical and practical points of view. The experiments involve automatic segmentation of license plates in a database with 990 images

[1]  Jorge Stolfi,et al.  The image foresting transform: theory, algorithms, and applications , 2004 .

[2]  Liang-Gee Chen,et al.  Predictive watershed: a fast watershed algorithm for video segmentation , 2003, IEEE Trans. Circuits Syst. Video Technol..

[3]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[4]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Alexandre X. Falcão,et al.  Interactive volume segmentation with differential image foresting transforms , 2004, IEEE Transactions on Medical Imaging.

[6]  Jiaxin Wang,et al.  An efficient method of license plate location , 2005, Pattern Recognit. Lett..

[7]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Alexandre X. Falcão,et al.  IFT-Watershed from gray-scale marker , 2002, Proceedings. XV Brazilian Symposium on Computer Graphics and Image Processing.

[9]  Jayaram K. Udupa,et al.  User-Steered Image Segmentation Paradigms: Live Wire and Live Lane , 1998, Graph. Model. Image Process..

[10]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[11]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Alexandre X. Falcão,et al.  Image segmentation by tree pruning , 2004, Proceedings. 17th Brazilian Symposium on Computer Graphics and Image Processing.

[13]  Alexandre X. Falcão,et al.  The Ordered Queue and the Optimality of the Watershed Approaches , 2000, ISMM.

[14]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[15]  Jayaram K. Udupa,et al.  An ultra-fast user-steered image segmentation paradigm: live wire on the fly , 2000, IEEE Transactions on Medical Imaging.