Spline-based medial axis transform representation of binary images

Abstract Medial axes are well-known descriptors used for representing, manipulating, and compressing binary images. In this paper, we present a full pipeline for computing a stable and accurate piece-wise B-spline representation of Medial Axis Transforms (MATs) of binary images. A comprehensive evaluation on a benchmark shows that our method, called Spline-based Medial Axis Transform (SMAT), achieves very high compression ratios while keeping quality high. Compared with the regular MAT representation, the SMAT yields a much higher compression ratio at the cost of a slightly lower image quality. We illustrate our approach on a multi-scale SMAT representation, generating super-resolution images, and free-form binary image deformation.

[1]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[2]  Punam K. Saha,et al.  A survey on skeletonization algorithms and their applications , 2016, Pattern Recognit. Lett..

[3]  J A Sethian,et al.  A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Alan C. Bovik,et al.  Generalized predictive binary shape coding using polygon approximation , 2000, Signal Process. Image Commun..

[5]  Hans-Peter Seidel,et al.  Skeleton‐based Variational Mesh Deformations , 2007, Comput. Graph. Forum.

[6]  Noel Brady,et al.  Context-based arithmetic encoding of 2D shape sequences , 1997, Proceedings of International Conference on Image Processing.

[7]  Paul A. Yushkevich,et al.  Continuous medial representations for geometric object modeling in 2D and 3D , 2003, Image Vis. Comput..

[8]  Francisco José Madrid-Cuevas,et al.  The computation of polygonal approximations for 2D contours based on a concavity tree , 2014, J. Vis. Commun. Image Represent..

[9]  Peter Shirley,et al.  Fundamentals of computer graphics , 2018 .

[10]  Alexandru Telea,et al.  Focus-and-Context Skeleton-based Image Simplification using Saliency Maps , 2021, VISIGRAPP.

[11]  Gábor Székely,et al.  Multiscale Medial Loci and Their Properties , 2003, International Journal of Computer Vision.

[12]  Günter Rote,et al.  Computing the Minimum Hausdorff Distance Between Two Point Sets on a Line Under Translation , 1991, Inf. Process. Lett..

[13]  Kun Zhou,et al.  2D shape deformation using nonlinear least squares optimization , 2006, The Visual Computer.

[14]  Alexandru Telea,et al.  Quantitative Evaluation of Dense Skeletons for Image Compression , 2020, Inf..

[15]  Alexandru Telea,et al.  A Dense Medial Descriptor for Image Analysis , 2013, VISAPP.

[16]  Peter Gerken,et al.  Object-based analysis-synthesis coding of image sequences at very low bit rates , 1994, IEEE Trans. Circuits Syst. Video Technol..

[17]  P. Jaccard THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .

[18]  Toshiaki Watanabe,et al.  A binary shape coding method using modified MMR , 1997, Proceedings of International Conference on Image Processing.

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

[20]  Kaleem Siddiqi,et al.  Hamilton-Jacobi Skeletons , 2002, International Journal of Computer Vision.

[21]  Alfred M. Bruckstein,et al.  Skeletonization via Distance Maps and Level Sets , 1995, Comput. Vis. Image Underst..

[22]  Tamal K. Dey,et al.  Approximate medial axis as a voronoi subcomplex , 2002, SMA '02.

[23]  Ramón M. Rodríguez-Dagnino,et al.  Efficiency of chain codes to represent binary objects , 2007, Pattern Recognit..

[24]  Alexandru Telea,et al.  An Augmented Fast Marching Method for Computing Skeletons and Centerlines , 2002, VisSym.

[25]  Marie-Paule Cani,et al.  Adaptive implicit modeling using subdivision curves and surfaces as skeletons , 2002, SMA '02.

[26]  Guojun Lu,et al.  Review of shape representation and description techniques , 2004, Pattern Recognit..

[27]  Antonio A. F. Oliveira,et al.  2D Shape Deformation Based on Positional Constraints and Layer Manipulation , 2011, 2011 Brazilian Symposium on Games and Digital Entertainment.

[28]  Dominique Attali,et al.  Modeling noise for a better simplification of skeletons , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[29]  W. Walthen-Dunn A Transformation for Extracting New De scriptors of Shape ' , in , 2017 .

[30]  Andrea Tagliasacchi,et al.  3D Skeletons: A State‐of‐the‐Art Report , 2016, Comput. Graph. Forum.

[31]  Alexandru Telea Feature Preserving Smoothing of Shapes Using Saliency Skeletons , 2012, Visualization in Medicine and Life Sciences II.

[32]  David Malah,et al.  Skeleton-based morphological coding of binary images , 1998, IEEE Trans. Image Process..

[33]  Ming C. Lin,et al.  Efficient computation of a simplified medial axis , 2003, ACM Symposium on Solid Modeling and Applications.

[34]  José M. N. Leitão,et al.  Adaptive B-splines and boundary estimation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[35]  Olaf Kübler,et al.  Hierarchic Voronoi skeletons , 1995, Pattern Recognit..

[36]  Dominique Attali,et al.  Computing and Simplifying 2D and 3D Continuous Skeletons , 1997, Comput. Vis. Image Underst..

[37]  Kaleem Siddiqi,et al.  Medial Representations: Mathematics, Algorithms and Applications , 2008 .

[38]  Noel Brady MPEG-4 standardized methods for the compression of arbitrarily shaped video objects , 1999, IEEE Trans. Circuits Syst. Video Technol..

[39]  Domen Mongus,et al.  Efficient chain code compression with interpolative coding , 2018, Inf. Sci..

[40]  Paul A. Yushkevich,et al.  Deformable M-Reps for 3D Medical Image Segmentation , 2003, International Journal of Computer Vision.

[41]  John Dingliana,et al.  As-rigid-as-possible image registration for hand-drawn cartoon animations , 2009, NPAR '09.

[42]  Francisco José Madrid-Cuevas,et al.  Fast computation of optimal polygonal approximations of digital planar closed curves , 2016, Graph. Model..

[43]  Wim H. Hesselink,et al.  A General Algorithm for Computing Distance Transforms in Linear Time , 2000, ISMM.

[44]  Robert Strzodka,et al.  Generalized distance transforms and skeletons in graphics hardware , 2004, VISSYM'04.

[45]  Bert Jüttler,et al.  G1 Hermite interpolation by Minkowski Pythagorean hodograph cubics , 2006, Comput. Aided Geom. Des..

[46]  Jean-Daniel Boissonnat,et al.  Stability and Computation of Medial Axes - a State-of-the-Art Report , 2009, Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration.

[47]  Hong Yan,et al.  An adaptive split-and-merge method for binary image contour data compression , 2001, Pattern Recognit. Lett..

[48]  Herbert Freeman,et al.  On the Encoding of Arbitrary Geometric Configurations , 1961, IRE Trans. Electron. Comput..

[49]  Bert Jüttler,et al.  Computing a compact spline representation of the medial axis transform of a 2D shape , 2013, Graph. Model..

[50]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[51]  Ching Y. Suen,et al.  Thinning Methodologies - A Comprehensive Survey , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[52]  Wim H. Hesselink,et al.  Euclidean Skeletons of Digital Image and Volume Data in Linear Time by the Integer Medial Axis Transform , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[53]  Tom Lyche,et al.  Discrete B-splines and subdivision techniques in computer-aided geometric design and computer graphics , 1980 .

[54]  Les A. Piegl,et al.  The NURBS book (2nd ed.) , 1997 .

[55]  Tiow Seng Tan,et al.  Parallel Banding Algorithm to compute exact distance transform with the GPU , 2010, I3D '10.

[56]  HARRY BLUM,et al.  Shape description using weighted symmetric axis features , 1978, Pattern Recognit..

[57]  Aggelos K. Katsaggelos,et al.  An efficient rate-distortion optimal shape coding approach utilizing a skeleton-based decomposition , 2003, IEEE Trans. Image Process..