Sony George

Pigment Mapping of the Scream (1893) Based on Hyperspectral Imaging

Hyperspectral imaging is a promising non-invasive method for applications in conservation of painting. With its ability to capture both spatial and spectral information which relates to physical characteristics of materials, the identification of pigments and its spatial distribution across the painting is now possible. In this work, The Scream (1893) by Edvard Munch is acquired using a hyperspectral scanner and the pigment mapping of its constituent pigments are carried out. Two spectral image classification methods, i.e. Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM), and a fully constrained spectral unmixing algorithm combined with linear mixing model are employed for the pigment mapping of the painting.

A database for spectral image quality

We introduce a new image database dedicated to multi-/hyperspectral image quality assessment. A total of nine scenes representing pseudo-at surfaces of different materials (textile, wood, skin. . . ) were captured by means of a 160 band hyperspectral system with a spectral range between 410 and 1000nm. Five spectral distortions were designed, applied to the spectral images and subsequently compared in a psychometric experiment, in order to provide a basis for applications such as the evaluation of spectral image difference measures. The database can be downloaded freely from http://www.colourlab.no/cid.

Spectral Image Analysis and Visualisation of the Khirbet Qeiyafa Ostracon

The article reports the research conducted to enhance the readability of Khirbet Qeiyafa ostracon using the spectral image analysis combined with image processing. The spectral imaging carried out in the visible and IR range facilitated the detection, improvement in readability of characters, and better interpretation of the history. Analysis of the spectral data using principal component analysis and independent component analysis showed better ink visibility and gave further support to archaeologists and historians. The proposed techniques resulted in an improvement compared to earlier interpretations.

Robust Anomaly Detection Using Reflectance Transformation Imaging for Surface Quality Inspection

We propose a novel methodology for the detection and analysis of visual anomalies on challenging surfaces (metallic). The method is based on a local assessment of the reflectance across the inspected surface, using Reflectance Transformation Imaging data: a set of luminance images captured by a fixed camera while varying light spatial positions. The reflectance, in each pixel, is modelled by means of a projection of the measured luminances onto a basis of geometric functions, in this case, the Discrete Modal Decomposition (DMD) basis. However, a robust detection and analysis of surface visual anomalies requires that the method must not be affected neither by the geometry (sensor and surface orientation) nor by the texture pattern orientation of the inspected surface. We therefore introduce a rotation-invariant representation on the DMD, from which we devise saliency maps representing the local differences on reflectances. The methodology is tested on different engineering metallic samples exhibiting several types of defects. Compared to other saliency assessments, the results of our methodology demonstrate the best performance regarding anomaly detection, localisation and analysis.

Ink classification and visualisation of historical manuscripts: Application of hyperspectral imaging

Overwritten characters and mixed inks can make documents hard to read. In this paper, we present and discuss research aimed at enhancement of a historical manuscript by separating text written using two different inks in two directions. We have used hyperspectral imaging to acquire images of the manuscript in the visible and near-infrared spectral region. The obtained data represents the spectral reflectance at every point of the manuscript surface. Visualisation of the manuscript under different illuminant has been simulated and demonstrated the issues associated with conventional digitisation techniques using 3-channel imaging. Spectral classification has been performed using two classification algorithms; spectral angle mapper and spectral information divergence. We have thus demonstrated the usefulness of spectral imaging to separate the inks.