Multispectral acquisition of large-sized pictorial surfaces

Multispectral acquisition of artworks has recently received considerable attention in the image processing community. Quite understandably, so far this attention has mainly focused on paintings, given their predominant role in museum collections. It is worth pointing out that the instrumentation and procedures used for acquiring regular paintings are not suited for the multispectral acquisition of large-sized painted surfaces such as frescoed halls and great paintings. Given the relevance of such artifacts, and their widespread presence in churches or historical buildings due to their social function, the problem of finding suitable techniques for their acquisition is certainly worth addressing. This paper focuses on multispectral acquisition of large-sized pictorial surfaces, systematically addressing the practical issues related to the acquisition equipment and procedure. Given the crucial role played by the illumination in this application, special attention is given to this issue. The proposed approach is supported by experimental results

On Clinical Agreement on the Visibility and Extent of Anatomical Layers in Digital Gonio Photographs

Purpose: To quantitatively evaluate the inter-annotator variability of clinicians tracing the contours of anatomical layers of the iridocorneal angle on digital gonio photographs, thus providing a baseline for the validation of automated analysis algorithms. Methods: Using a software annotation tool on a common set of 20 images, five experienced ophthalmologists highlighted the contours of five anatomical layers of interest: iris root (IR), ciliary body band (CBB), scleral spur (SS), trabecular meshwork (TM), and cornea (C). Inter-annotator variability was assessed by (1) comparing the number of times ophthalmologists delineated each layer in the dataset; (2) quantifying how the consensus area for each layer (i.e., the intersection area of observers\u2019delineations) varied with the consensus threshold; and (3) calculating agreement among annotators using average per-layer precision, sensitivity, and Dice score. Results: The SS showed the largest difference in annotation frequency (31%) and the minimum overall agreement in terms of consensus size ( 3c28% of the labeled pixels). The average annotator\u2019s per-layer statistics showed consistent patterns, with lower agreement on the CBB and SS (average Dice score ranges of 0.61\u20130.7 and 0.73\u20130.78, respectively) and better agreement on the IR, TM, and C (average Dice score ranges of 0.97\u20130.98, 0.84\u20130.9, and 0.93\u20130.96, respectively). Conclusions: There was considerable inter-annotator variation in identifying contours of some anatomical layers in digital gonio photographs. Our pilot indicates that agreement was best on IR, TM, and C but poorer for CBB and SS. Translational Relevance: This study provides a comprehensive description of interannotator agreement on digital gonio photographs segmentation as a baseline for validating deep learning models for automated gonioscopy

Acquisition and processing of multispectral data for texturing 3D models

This thesis deals with three problems concerning the use of a multispectral imaging spectrograph in applications of cultural heritage. The multispectral camera is part of an instrument developed within the project "Shape&Color" (CARIPARO, 2003-2005), coupling the spectrograph with a 3D laser scanner. Although the issues we have addressed arose from the characteristics of this specic instrument, they can be regarded as general problems concerning multispectral imaging, and are therefore of broader interest. The first part relates on the characterization of the spectrograph performance in measuring spectral reflectance under different illumination conditions. Four different illumination setups have been used to acquire a set of colored calibrated tiles. The system performance has been evaluated through a metrologically-inspired procedure, using as descriptors the average error (AE) and the average error standard deviation (AESTD), calculated by means of error propagation formula. The best results have been obtained with a metallic iodide lamp and an incandescence lamp used in a sequence, juxtaposing the spectral reflectance measured with the metallic iodide lamp in the 400-600 nm interval and that obtained with the halogen lamp in the 600-900 nm interval. The second presented issue concerns the problem of separating spectral illumination and spectral reflectance from the acquired color signal (the global radiation signal reflected by a target object). Since the latter can be considered as the product of illumination and spectral reflectance, this is an ill-posed problem. Methods in the literature estimate the two functions apart from a scale factor. The proposed solution attempts at the recovery of this scale factor using a statistical-based approach. The core of the algorithm consists of the estimation of the illumination intensity through a modification of the RANSAC algorithm, using relations derived from the physical constraints of the illumination and the spectral reflectance. The spectral reflectance is subsequently computed from the measured color signal and the estimated illumination function. The algorithm has been tested on four case studies, representing artworks of different pictorial techniques, color characteristics and dimensions. The results are good in terms of mean relative error, while the infinity norm of the relative error sometimes assumes high values. The last problem we have dealt with is that of using the multispectral images acquired with the Shape&Color scanner to texturize uncalibrated 3D data. What makes the problem worth addressing is that the spectral camera is not pinhole, but can be classified as a cylindrical panoramic camera. In this thesis, the general problem of estimating the extrinsic parameters of the camera from a known set of 3D-2D correspondences has been considered. The chosen approach is the classical reprojection error minimization procedure. As the projection operator is nonlinear, the objective function has a very complicated structure. Due to this and to the high dimensionality of the problem, the minimization results are strongly sensitive to the choice of the initial parameter values. This work proposes a way of finding a reliable initial point for the minimization function, so as to lower the risk of being trapped into local minima

A lattice estimation approach for the automatic evaluation of corneal endothelium density

The analysis of microscopy images of corneal endothelium is routinely carried out at eye banks to assess cell density, one of the main indicators of cornea health state and quality. We propose here a new method to derive endothelium cell density that, at variance with most of the available techniques, does not require the identification of cell contours. It exploits the feature that endothelium cells are approximately laid out as a regular tessellation of hexagonal shapes. This technique estimates the inverse transpose of a matrix generating this cellular lattice, from which the density is easily obtained. The algorithm has been implemented in a Matlab prototype and tested on a set of 21 corneal endothelium images. The cell densities obtained matched quite well with the ones manually estimated by eye-bank experts: the percent difference between them was on average -0.1% (6.5% for absolute values). Albeit the performances of this new algorithm on the images of our test set are definitely good, a careful evaluation on a much larger data set is needed before any clinical application of the proposed technique could be envisaged. The collection of an adequate number of endothelium images and of their manual densities is currently in progress

Multispectral Acquisition of Large-Sized Pictorial Surfaces

Multispectral acquisition of artworks has recently received considerable attention in the image processing community. Quite understandably, so far this attention has mainly focused on paintings, given their predominant role in museum collections. It is worth pointing out that the instrumentation and procedures used for acquiring regular paintings are not suited for the multispectral acquisition of large-sized painted surfaces such as frescoed halls and great paintings. Given the relevance of such artifacts, and their widespread presence in churches or historical buildings due to their social function, the problem of finding suitable techniques for their acquisition is certainly worth addressing. This paper focuses on multispectral acquisition of large-sized pictorial surfaces, systematically addressing the practical issues related to the acquisition equipment and procedure. Given the crucial role played by the illumination in this application, special attention is given to this issue. The proposed approach is supported by experimental results

Lightness Recovery for Pictorial Surfaces

An important technique in cultural heritage preservation is multispectral acquisition, where one recovers a detailed spectral record of a painting using carefully calibrated lighting. This is difficult to do with frescoes, because it is hard to recover the spatial variation in light intensity that results from factors like the imaging setup and the curvature of the fresco. We introduce a new formulation of the lightness problem applied to images of pictorial artworks. The problem is different from the conventional lightness problem, because artists often paint the effects of light, so the albedo field contains a component that mimics an illumination field. Our method distinguishes between physical illumination and painted shading through spatial frequency effects and dynamic range considerations. We evaluate our method using multispectral images of paintings, where the physical illumination field is known. Our method produces estimates of the illumination intensity field that compare very well with the known ground truth, and outperforms other state-of-the art lightness recovery algorithms. For frescoes, ground truth is not available, but we show that our method produces consistent results, in the sense that the illumination functions estimated on the image and on (some of) its subimages are very similar on the overlap. We show our method produces qualitatively good color corrections for images of frescoes found on the web

Multispectral Acquisition of Large-Sized Pictorial Surfaces

Multispectral acquisition of artworks has recently received considerable attention in the image processing community. Quite understandably, so far this attention has mainly focused on paintings, given their predominant role in museum collections. It is worth pointing out that the instrumentation and procedures used for acquiring regular paintings are not suited for the multispectral acquisition of large-sized painted surfaces such as frescoed halls and great paintings. Given the relevance of such artifacts, and their widespread presence in churches or historical buildings due to their social function, the problem of finding suitable techniques for their acquisition is certainly worth addressing. This paper focuses on multispectral acquisition of large-sized pictorial surfaces, systematically addressing the practical issues related to the acquisition equipment and procedure. Given the crucial role played by the illumination in this application, special attention is given to this issue. The proposed approach is supported by experimental results