Automatic petrographic feature extraction from pottery of archaeological interest

The concept of fabric, defined by the description and classification method introduced by Whitbread (1995), has been usually used to perform petrographic studies of thin sections of ancient ceramics. This work analyzes pottery of archaeological interest by making use of image processing algorithms. First a preliminary petrographic analysis has been quantitatively performed by point counter stage. Afterward our attention has been focused on the automatic identification of structural and textural components of the potteries through optical microscopy. Image analysis techniques have been then used to automatically classify the image component into three classes: inclusions, voids and groundmass. Preliminary results, confirm the effectiveness of the proposed approach: petrographic data collection becomes faster with respect to traditional method providing also quantitative information useful for fabric recognition.

[1]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[2]  Giovanni Gallo,et al.  Augmented Perception of the Past. The Case of Hellenistic Syracuse , 2012, J. Multim..

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

[4]  Andrea Di Giulio,et al.  Reliability of textural analysis of ancient plasters and mortars through automated image analysis , 2004 .

[5]  Ian K. Whitbread,et al.  Greek Transport Amphorae: A Petrological and Archaeological Study , 1995 .

[6]  J. Navarro-Pedreño Numerical Methods for Least Squares Problems , 1996 .

[7]  Michael Denis Higgins,et al.  Quantitative Textural Measurements in Igneous and Metamorphic Petrology , 2006 .

[8]  Peter J. Huber,et al.  Robust Statistical Procedures: Second Edition , 1996 .

[9]  Rafael C. González,et al.  Digital image processing, 3rd Edition , 2008 .

[10]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[11]  Sebastiano Battiato,et al.  A Robust Image Alignment Algorithm for Video Stabilization Purposes , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Livio Tenze,et al.  Virtual restoration of vintage photographic prints affected by foxing and water blotches , 2005, J. Electronic Imaging.

[13]  P. J. Huber Robust Statistical Procedures , 1977 .

[14]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Sebastiano Battiato,et al.  Digital video stabilization through curve warping techniques , 2008, IEEE Transactions on Consumer Electronics.

[16]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Sebastiano Battiato,et al.  A Robust Block-Based Image/Video Registration Approach for Mobile Imaging Devices , 2010, IEEE Transactions on Multimedia.