Similarity Assessment for the Analysis of 3D Artefacts

Archaeological artefacts are often classified in homogeneous groups, with respect to their origin, use, age, etc., in terms of their physical traits, i.e., colour, material, design pattern, form, shape, size, style, surface texture, technology, thickness, and weight. In particular, when dealing with archaeological exhibits, a single trait is generally not enough for the classification of the artefact because most of the objects are affected by degradation or only partially preserved. In this contribution we propose a shape analysis and comparison pipeline, which combines geometry and texture to identify classes of homogeneous artefacts. The geometric description is based on a statistical technique to select properties that are mutually independent; the photometric information is handled according to a topological perspective, and complemented by the analysis of colour distribution. The outcome is a mixed description of each 3D artefact, which is used to derive a similarity measure between objects. The potential of our method is high since we can include any property representable as real- or vector-valued functions. Experimental results are exhibited to show the efficacy of the method in retrieval and classification tasks.

[1]  Guido M. Cortelazzo,et al.  Combining color and shape descriptors for 3D model retrieval , 2013, Signal Process. Image Commun..

[2]  Alexander M. Bronstein,et al.  Diffusion framework for geometric and photometric data fusion in non-rigid shape analysis , 2011, ArXiv.

[3]  Federico Tombari,et al.  A combined texture-shape descriptor for enhanced 3D feature matching , 2011, 2011 18th IEEE International Conference on Image Processing.

[4]  Silvia Biasotti,et al.  Grouping real functions defined on 3D surfaces , 2013, Comput. Graph..

[5]  Alexander M. Bronstein,et al.  Efficient Computation of Isometry-Invariant Distances Between Surfaces , 2006, SIAM J. Sci. Comput..

[6]  Vincent Barra,et al.  3D shape retrieval and classification using multiple kernel learning on extended Reeb graphs , 2014, The Visual Computer.

[7]  Daniela Giorgi,et al.  PHOG: Photometric and geometric functions for textured shape retrieval , 2013, SGP '13.

[8]  Bernard Chazelle,et al.  Shape distributions , 2002, TOGS.

[9]  Ron Kimmel,et al.  Images as Embedded Maps and Minimal Surfaces: Movies, Color, Texture, and Volumetric Medical Images , 2000, International Journal of Computer Vision.

[10]  Gary Singh CultLab3D: Digitizing Cultural Heritage , 2014, IEEE Computer Graphics and Applications.

[11]  Louis Chevallier,et al.  SHREC'13 Track: Retrieval on Textured 3D Models , 2013, 3DOR@Eurographics.

[12]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[13]  David Cohen-Steiner,et al.  Stability of Persistence Diagrams , 2007, Discret. Comput. Geom..

[14]  David Arnold,et al.  Graphically Speaking Editor : André Stork Computer Graphics and Cultural Heritage From One-Way Inspiration to Symbiosis , Part 1 , 2014 .

[15]  Andrea Giachetti,et al.  Retrieval and Classification on Textured 3D Models , 2014, 3DOR@Eurographics.

[16]  Radu Horaud,et al.  Keypoints and Local Descriptors of Scalar Functions on 2D Manifolds , 2012, International Journal of Computer Vision.

[17]  M. T. Suzuki,et al.  A Web-based retrieval system for 3D polygonal models , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[18]  Alexander M. Bronstein,et al.  Affine-Invariant Photometric Heat Kernel Signatures , 2012, 3DOR@Eurographics.

[19]  Arnold W. M. Smeulders,et al.  PicToSeek: combining color and shape invariant features for image retrieval , 2000, IEEE Trans. Image Process..

[20]  Jan-Michael Frahm,et al.  3D model matching with Viewpoint-Invariant Patches (VIP) , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  B. Falcidieno,et al.  Invited Lecture: A Shape Abstraction Paradigm for Modeling Geometry and Semantics , 1998 .

[22]  Yasuo Kuniyoshi,et al.  Partial matching of real textured 3D objects using color cubic higher-order local auto-correlation features , 2010, The Visual Computer.

[23]  Yong-Jin Liu,et al.  3D model retrieval based on color + geometry signatures , 2011, The Visual Computer.

[24]  Zhiyong Huang,et al.  Combining Shape and Color for Retrieval of 3D Models , 2009, 2009 Fifth International Joint Conference on INC, IMS and IDC.

[25]  Adrian Hilton,et al.  Correspondence labelling for wide-timeframe free-form surface matching , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[26]  Nicola Orio,et al.  Retrieval of Colored 3D Models , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[27]  Szymon Rusinkiewicz,et al.  Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors , 2003, Symposium on Geometry Processing.

[28]  Alexander M. Bronstein,et al.  Photometric Heat Kernel Signatures , 2011, SSVM.

[29]  Herbert Edelsbrunner,et al.  Computational Topology - an Introduction , 2009 .

[30]  Andrea Cerri,et al.  The Persistence Space in Multidimensional Persistent Homology , 2013, DGCI.

[31]  Haibin Ling,et al.  Deformation invariant image matching , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[32]  Marcello Pelillo,et al.  Dominant Sets and Pairwise Clustering , 2007 .

[33]  Vincent Barra,et al.  Learning Kernels on Extended Reeb Graphs for 3D Shape Classification and Retrieval , 2013, 3DOR@Eurographics.