L. Petru

Bio: L. Petru is an academic researcher from Academy of Sciences of the Czech Republic. The author has contributed to research in topics: Image segmentation & Color image. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Microscopic cross-sections of old artworks

Abstract: Materials research of microscopic cross-sections of paintings color layers is a very useful tool. It gives the restorers better insight into the technique, reveals how the artwork was created and what kind of materials was used. The proposed approach solves the problem of the proper spatial alignment of visible light and ultraviolet light images of cross-sections for further analysis by means of multimodal geometrical image registration. Then, the application offers primary segmentation of color layers. Second part of our work deals with automatic image retrieval, as a part of designing a database of laboratory research reports for restoration purposes. The aim is to facilitate search for reports about artworks with similar characteristic, based on the cross-section images.

Application of Information Redundancy Measure To Image Segmentation

Abstract: In this paper, the problem of image segmentation quality is considered. The main idea is to find a quality criterion, which could have an extremum. The problem is viewed as selecting the best segmentation from a set of images generated by segmentation algorithm at different parameter values. We propose to use information redundancy measure as a criterion for optimizing segmentation quality. The method for constructing the redundancy measure provides criterion with extremal properties. To show efficiency of the proposed criterion, computing experiment is carried out. The proposed criterion is combined with SLIC and EDISON segmentation algorithms. Computing experiment shows that the segmented image corresponding to a minimum of redundancy measure produces acceptable information distance when compared with the original image. In most cases, the lowest information distance between this segmented image and ground-truth segmentations is obtained. An example of applying the redundancy measure to segmentation of images of painting material cross-sections is considered.

The image processing system for art specimens: Nephele

Abstract: In our paper we introduce comprehensive solution for processing and archiving information about artwork specimens used in the course of art restoration - Nephele. The information processing based on image data is used in the procedure of identification of pigment and binder present in the artwork, which is very important issue for restorers. Proposed approach geometrically aligns images of microscopic cross-sections of artwork color layers - image registration method based on mutual information, and then creates preliminary color layer segmentation - modified k-means clustering. The archiving part of the Nephele enables creating database entries for painting materials research database, their storage, and creating text-based queries. In addition to these traditional database functions, advanced report retrieval is supported; based on the similarity of image data, comparing either the ultraviolet and visual spectra images (using co-occurence matrices and color similarity functions), or the electron microscopy images (using features computed from the wavelet decomposition of the data).

Analysis of painting materials on multimodal microscopic level

Abstract: Our paper introduces a system based on digital image processing algorithms designed to facilitate analysis of painting materials during artwork conservation. Microscopic images of minute samples - cross sections - from the artworks are scanned using visible and ultraviolet spectra and under scanning electron microscope. Firstly, the scans are registered to remove geometrical differences. The multimodal nature of the problem led to the application of mutual information. The image quality is maximized by means of blind deconvolution methods. Cross-sections are then segmented to individual layers and distinctive seeds. For the image retrieval part, which facilitates further analyzes and conclusions, the layers are represented by means of wavelet analysis and secondorder statistics. The library of such features can be connected to the time of creation and differences between vectors of the same materials but from different paintings can help during a painter authentication.