review of geometric and radiometric analyses of...

REVIEW OF GEOMETRIC AND RADIOMETRICANALYSES OF PAINTINGS

Fabio Remondino ([email protected])Alessandro Rizzi ([email protected])

Bruno Kessler Foundation (FBK), Trento, Italy

Luigi Barazzetti ([email protected])Marco Scaioni ([email protected])

Francesco Fassi ([email protected])Raffaella Brumana ([email protected])

Politecnico di Milano, Milan, Italy

Anna Pelagotti ([email protected])

Istituto Nazionale di Ottica, Florence, Italy

(Extended version of a paper presented at the ISPRS Commission V Symposium on‘‘Close Range Image Measurement Techniques’’ hosted by Newcastle University,

22nd to 24th June 2010)

Abstract

This paper presents an overview of a number of diagnostic campaigns carriedout to assess the conservation state of paintings. The specific characteristics requiredin cultural heritage applications have been investigated and are reported, where twomain fields are considered: radiometric analyses using multispectral images and 3Dsurveying for geometric deformation analysis by means of photogrammetry and laserscanning. The activities described were accomplished in interdisciplinary teams,composed of photogrammetrists, art-historians, restorers and experts of non-destructive diagnostic techniques.

Keywords: 3D surveying, close range photogrammetry, cultural heritage,deformation analysis, multispectral imaging

Multispectral and multimodal images, as well as range data, are fundamental in thecultural heritage conservation domain, since they allow the faithful documentation, testing,digital archiving, computer-aided restoration and digital preservation of artworks at differentscales. Multimodal data are data related to the same object or scene but acquired by differentsensors or in different modes, for instance, using different illuminators. Multispectral imagesare a collection of images related to the same object or scenes, acquired in different spectralbands, generally contiguous, of the visible (or near-visible) spectrum. In the case of paintings,multispectral imaging and 3D surveying (Fig. 1) can be used for pigment identification, precise

The Photogrammetric Record 26(136): 439–461 (December 2011)DOI: 10.1111/j.1477-9730.2011.00664.x

� 2011 The Authors. The Photogrammetric Record � 2011 The Remote Sensing and Photogrammetry Society and Blackwell Publishing Ltd.

Blackwell Publishing Ltd. 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street Malden, MA 02148, USA.

colour measurement, characterisation and rendering, highly detailed recording and visualisa-tion (brush strokes can be identified), as well as for the measurement of the shape of the(wooden) support or the paint-layer roughness (Fontana et al., 2005; Lahanier et al., 2005).Scientific and technical examination of paintings is usually carried out in order to provideinformation on their structure and materials. Studies for the identification of paintingcomponents and for the analysis of the state of conservation are historically based on microsampling, chemical–physical tests or non-invasive single-point methods such as X-rayfluorescence (XRF), laser-induced breakdown spectroscopy (LIBS), reflectance and Ramanspectroscopy. Digital imaging is also applied in several regions of the electromagneticspectrum such as X-rays, visible, near infrared (IR), thermal IR and ultraviolet-induced (UV-induced) visible fluorescence images (Liang et al., 2005; Colantoni et al., 2006; Bonifazziet al., 2008; Pelagotti et al., 2008). However, all these listed techniques examine paintings astwo-dimensional objects, basically neglecting any depth information. The planar nature of apainting justifies this approach but, in some cases, the deviation from planarity, either of thesupport or of the surface, is a significant factor that conservators will have to consider. For thisreason, aided by the great advances in digital 3D surveying methods, the stability of thewooden or canvas supports, and their deformation, are currently also being examined as veryinformative and useful data. Guidi et al. (2004) and Robson et al. (2004) reported the use of 3Dsurveying techniques on wooden artwork that underwent conservation treatments with possiblechanges of environmental conditions (such as humidity and temperature). Indeed, quantitativedata of artwork deformations are particularly relevant for the correct planning and evaluation ofthe restoration process. Furthermore, 3D data can provide essential information for an earlydetection of any structural change, which is crucial to prevent irreversible damage. 3Dinformation can also be integrated with 2D data to create a complete package of facts about anartwork (Fontana et al., 2003). The accuracy required by restorers, for a proper paintingsurveying and conservation treatment, is nearer to that typical of close range metrology (0Æ01 to1mm precision), than to that needed in architectural surveying (1 to 10mm). The surveyingtechniques that could fulfil these requirements are photogrammetry and short-range activesensors, although long-range time of flight (TOF) sensors could also be used for macro-deformation analysis, in particular for large artworks. Although photogrammetry was introduceddecades ago as an important support for the documentation and monitoring of paintingdeformations, recently terrestrial laser scanning and 3D imaging are widely recognised andaccepted for recording 3D details or movement surveying (Akca et al., 2007; Blais et al., 2007).

This paper reports a review of several diagnostic campaigns, carried out by several Italianresearch groups, aimed at assessing the conservation state of paintings on various supportbases. The research was accomplished by different multidisciplinary teams composed of

Fig. 1. Typical instruments used for painting surveying and art diagnostics: (a) close range 3D scanning;(b) IR reflectography scanner; and (c) multispectral UV-induced fluorescence imaging system.

Remondino et al. Review of geometric and radiometric analyses of paintings

� 2011 The Authors

440 The Photogrammetric Record � 2011 The Remote Sensing and Photogrammetry Society and Blackwell Publishing Ltd

photogrammetrists, restorers and experts in non-destructive diagnostic techniques for culturalheritage. Two main aspects are considered here: analysis of radiometric content usingmultispectral images and examination of the 3D geometric deformation. Nowadays, bothaspects are among the most investigated and requested investigations for non-invasive artdiagnostics. The analysis of a set of multispectral and multimodal images represents aneffective tool to highlight and understand possible problems for conservation and restoration,since it visualises differences and similarities among materials, and conservation states, acrossthe whole painting surface. In order to exploit the information provided by multispectral data, itneeds to be skilfully processed. As a first step, multispectral/multimodal images need to beproperly superimposed on one another. Therefore, the procedure of image registration that isrequired will be presented. An unsupervised method to classify and recognise materials, whichuses just the multispectral data-set, will also be discussed. The section on geometric deformationanalysis will report on experiences carried out with photogrammetry and laser scanningtechniques in order to evaluate the deformation of some important paintings as well as surveyingthe 3D shape, brush strokes and detachment of the upper paint layer of works of art.

Multispectral Image Analysis

In the cultural heritage domain, multispectral acquisition systems are generally focused onacquiring and processing images in the UV, visible, near IR and X-ray part of theelectromagnetic spectrum. In the past, the primary goal of multispectral systems was to achievehigh colour fidelity, while nowadays the trend is to use multispectral images to identifymaterials. Multispectral imaging is a non-invasive methodology that exploits the fact that eachmaterial reflects, absorbs and emits electromagnetic radiation according to its molecularcomposition and shape. Due to the different frequencies and energy employed, for each modeit is possible to infer specific information about the painting (Fig. 2). For example, fluorescenceimaging highlights the different varnishes and over-paintings, whilst near IR reflectographyallows the hidden drawings lying underneath the first paint layers to be inspected and revealed(thus visualising an artist’s pentimenti). On the other hand, X-rays are able to deliverinformation regarding the internal structure of the support material.

Multispectral images can be acquired with frame cameras (Pelagotti et al., 2008) orscanning devices mounting single or linear array sensors (Ribes et al., 2005; Bacci et al., 2006).The imaging sensors are generally coupled with interferential filters or a diffraction grating(Polder and van der Heijden, 2001).

In the analysis campaigns, a scientific cooled digital CCD camera (3072 · 2048 pixels,9lm pixel size) was used. The camera was coupled with a 50mm lens and 15 interferentialfilters, spaced at 50 nm intervals from 400 to 1100 nm. The system (property of Art-Test,Florence, Italy), associated with dedicated lamps (Fig. 3), allows the acquisition of images in

Fig. 2. The different responses of a painting to electromagnetic radiation.

The Photogrammetric Record


The Photogrammetric Record � 2011 The Remote Sensing and Photogrammetry Society and Blackwell Publishing Ltd 441

three spectral bands: visible reflectance (400 to 750 nm), UV-induced fluorescence (400 to750 nm) and near IR reflectography (800 to 1100 nm).

Thus a series of monochromatic images is generally acquired, one for each filter’stransmission bandwidth. In this way, it is possible, in the selected acquisition range, toapproximate, for each pixel, the spectral signature of any imaged material. Obviously, thesampling step is not ideal and, given the relatively large bandwidth of the generally employedinterferential filters, the signal reconstruction is certainly not perfect. However, image datacontent, especially if coupled with some single-point result, is often able to provide sufficientinformation, bothon the conservation state andon the chemical content of the painting’smaterials.

Since the optical system employed generally produces small misalignments among theacquired images, because of the distinct optical path lengths at each wavelength, in order tomeaningfully process the acquired data further, images need to be precisely registered with oneanother.

Overview of Data Registration Methods

Registration can generally be performed between 2D–2D data (such as images), 2D–3Ddata (for example, an image mapped onto a 3D model) or 3D–3D (such as 3D TOF scans). Inthe case of 2D data (images or maps), the registration is needed for such processes asmosaicking, 3D geometry extraction, spectra extraction and change detection. In order tosuccessfully integrate different types of data, features corresponding to the same areas need tobe identified. Registration is then the determination of a geometrical transformation that alignsfeatures in one data-set with the corresponding features in another data-set. Data registration isnecessary as the information might come from:

(a) Different imaging systems (multimodal/multispectral data): data related to the sameobject or scene but acquired by different sensors. This data subsequently needs to bealigned and superimposed for information fusion, multispectral analysis or otherdiagnostic applications.

Fig. 3. The acquisition of multispectral images using a frame CCD camera coupled with spectral filters and a UVillumination system.




(b) Different viewpoints (multiview data): data of the same object or scene are acquiredfrom different stations (or by rotating the camera) for 3D reconstruction purposes or togenerate high-resolution views or panoramas.

(c) Different acquisition epochs (multi-temporal data): data of the same object or scene areacquired at different times, for example, for change detection or deformation analysis.

Similar to what happens in the medical field, in art diagnostics and visual cultural heritagethe comparison and integration of different sets of information from diverse sources and data-sets is often required. Registration methods are primarily classified as area-based or feature-based methods (Zitova and Flusser, 2003; Remondino et al., 2009; Wyawahare et al., 2009).

Area-based methods mainly refer to square, rectangular or circular windows around aninterest point or even entire images. If small windows are used, a match is established usingcross-correlation methods or least squares matching. Fourier transform methods (Castro andMorandi, 1987; Reddy and Chatterji, 1996) and maximisation of mutual information (MMI)methods (Viola and Wells, 1997; Cappellini et al., 2005) are generally applied to the entireimages. The MMI method is an area-based method where the area corresponds to the entireimage. It is an interesting and powerful registration approach originating from informationtheory and is particularly suitable for the registration of images acquired in different spectralbands. The MMI method was used originally for medical imaging (Fitzpatrick and Sonka,2000) and was later customised for such cultural heritage applications as multispectral analysisof pigments or texture mapping of 3D models (Barni et al., 2005; Pelagotti et al., 2009). In thefollowing it will be presented in some detail, since it proved to be highly effective in this fielddue to its flexibility and capability of registering images with very few features in common.The MMI seeks a measure of the statistical dependency between two data-sets through acriterion that states that two images are correctly aligned when the Mutual Information (MI)assumes its maximum value. Given two images X and Y, related by a geometric transformationTa such that the pixel p of X (whose intensity is x) corresponds to the pixel Ta(p) of Y (whoseintensity is y), the MI of the two images is given by

MIðX ;Y Þ ¼Xx;y

pXY ðx; yÞ � log2pXY ðx; yÞ

pX ðxÞ � pY ðyÞð1Þ

where pXY (x, y) is the joint probability distribution of the two images and pX(x) and pY(y) aretheir marginal probability distributions. Marginal and joint probability distributions can beestimated with the normalisation of the joint histogram ha(x, y) of the two images, which isobtained by ‘‘binning’’ the intensity value pairs x = X(p) and y = Y(Ta(p)) for all the pixelsand depending on the transformation used (Viola and Wells, 1997)

pXY ;aðx; yÞ ¼ haðx;yÞPx;y

haðx;yÞ; pX ;aðxÞ ¼

Py

pXY ;aðx; yÞ; pY ;aðyÞ ¼P

xpXY ;aðx; yÞ : ð2Þ

MMI is a very general and powerful method, since no assumptions are made about thenature of this dependence and no constraints are imposed on the image content, thus enablingthe MMI criterion to be particularly effective when a small quantity of information is sharedbetween both images. However, the MMI method is computationally quite slow, particularlywhen using large images such as those of artworks. In order to speed up this process, themethod needs indeed an initial estimate of the unknown transformation parameters, togetherwith a search interval and incremental steps. Although its high reliability makes the methodespecially suitable for multimodal and multispectral images, given the large data load and theabsence of extremely different image content in the data analysis reported here, it was decided




to implement a different method to assess the correct registration parameters, which proved tobe much faster. The chosen method belongs to the group of ‘‘feature-based methods’’.

Feature-based methods combine features such as regions or edges with descriptorinformation, matching them using spatial relations (Vosselman, 1992), relaxation methods(Zhang et al., 2000), wavelets (Stone et al., 1999) or descriptor similarities (Mikolajczyk andSchmid, 2005). Feature-based registration methods are used when the local image intensity isless significant than the local structural information or, in the case of wide-baseline images,where area-based methods based on the correlation of interest points are ineffective due to thelarge perspective effects. The most reliable and powerful feature detector and descriptor is thescale invariant feature transform (SIFT) operator (Lowe, 2004). SIFT extracts image featuresthat are completely unchanged by image scaling and rotation, and it is partially invariant toillumination changes and camera viewpoint (affine transformation). The standard implemen-tation of SIFT has a 128-element descriptor. Another powerful detector and descriptor is theSURF (speeded-up robust feature) operator (Bay et al., 2008), which proved to be faster butable to find a slightly smaller number of correspondences.

Implemented Automated Registration of Multispectral Images

Multispectral images acquired with digital cameras and interferential filters (as in Fig. 3)present small misalignments between them due to the optical system of the camera. Therefore,these monochromatic images need to be perfectly registered for further diagnostic applications.The feature-based method developed was originally designed for automated orientation and 3Dreconstruction from sets of terrestrial images acquired with frame CCD/CMOS cameras(Barazzetti et al., 2010b). The same method was then extended to deal with spherical images aswell (Barazzetti et al., 2010a).

Image correspondences of a generic image block can be automatically matched with acombination of operators for feature detection and robust estimators. Indeed, automatedapproaches must be able to work with incorrect data, which should be detected and removed toobtain precise and reliable results. The selected registration procedure is based on: feature-based operators capable of determining the image correspondences and analysing theradiometric content of the images; and robust estimators that exploit data geometry to discardany mistakes that might remain. It presents a multi-step approach, made up of four phases:feature detection; outlier rejection; point decimation; and image transformation.

Firstly, all images are sorted with respect to their wavelength. The algorithm then matcheseach image i with the adjacent one, i + 1, by using the SIFT or SURF operators, following theprogressive order of the data. To remove incorrect correspondences, a preliminary analysis ofthe descriptors (128 vectors in the implementation) is carried out by using a ‘‘ratio test’’ with aprefixed threshold. This means that the ratio between the Euclidean distances of the first twocandidates must be less than a predefined threshold (for example, 0Æ6 to 0Æ8). Afterwards amore rigorous outlier rejection is performed using robust estimators (RANSAC, MAPSAC orLMedS). Based on the results obtained, the parameters for the correct transformation betweentwo images are computed.

As a planar homography (or projective transformation) allows the analysis of almost flatobjects (like most paintings), the procedure for outlier rejection exploits the constraints givenby this 2D fi 2D transformation. However, this method can work even in the case of strong3D deformations of the painting, as described later.

A point on the plane is identified as a column vector x = (x, y)T. This pair of values istermed inhomogeneous coordinates. The homogenous coordinates of a point can be obtainedby adding an extra coordinate. The point is now defined by a triplet x = (kx, ky, k)T, where k




can have any non-zero value. An arbitrary homogeneous vector x = (x1, x2, x3)T represents the

point x = (x1/x3, x2/x3)T in Euclidian 2D space R2.

A projective transformation is an invertible mapping from points in projective space Pn

(extended Euclidean space) to points in Pn (see also Hartley and Zisserman, 2004). This is alsocalled homography and, for 2D points, is represented by a 3 · 3 matrix:

x0 ¼x0

y0

1

24

35 ¼ h1 h2 h3

h4 h5 h6

h7 h8 h9

24

35 x

y1

2435 ¼ Hx ð3Þ

where H in a non-singular matrix with eight degrees of freedom. The inverse transformationis

x ¼ H�1x0: ð4Þ

The estimation of H from a set of correspondences x¢ M x (at least four) may be carriedout using homogenous coordinates. Equation (3) can be cast in a more convenient form usingthe vector cross product x¢ · Hx = 0 with an explicit form given by

x0 �Hx ¼ deti j kx0 y0 1

h1Tx h2Tx h3Tx

24

35 ¼ 0 ð5Þ

where h1T, h2T and h3T are the rows of the matrix H. This yields two equations (the third oneis not linearly independent):

0T �xT y0xT

xT 0T �x0xT

� � h1

h2

h3

24

35 ¼ 0 ð6Þ

and the final system has the form Ah = 0 (linear in the unknown elements of h). The trivialsolution h = 0 can be avoided using the constraint ||h|| = 1 and the best way to solve thesystem is to perform a singular value decomposition (SVD) on the matrix A. The SVDrewrites matrix A as a product of a diagonal matrix D and two diagonal matrices U and V asfollows:

A ¼ UDVT: ð7Þ

The solution of the system is given by the last column of V. In order to improve theconditioning number of the coefficient matrix, Hartley (1997) proposes a normalisation step.The image coordinates are therefore translated to achieve a zero mean and scaled so that theaverage distance to the origin is �2.

Alternatively, the solution of the projective transformation can be achieved with astandard least squares estimation with inhomogeneous coordinates (Luhmann et al., 2006).This approach yields the unknown parameters together with an estimation of the covariancematrix of the unknowns. Both procedures (with and without homogenous coordinates) areimplemented in the package.

The registration approach works even with strong 3D deformations of the canvas. If theperspective centre of the camera is fixed, the images are still connected by a projectivetransformation. This may be verified by considering the pinhole camera matrix model, which




expresses the alignment between the perspective centre, the image point x and object point Xwith a 3 · 4 matrix P as follows:

x ¼ PX: ð8Þ

Here P can be decomposed into the matrix product of a 3 · 3 calibration matrix K, rotationmatrix R and translation vector t (Hartley and Zisserman, 2004). For an image pair acquiredby rotating the camera around the perspective centre, the respective matrices P and P¢ of thepair become

P ¼ K½I; 0�; P0 ¼ K½R; 0�: ð9Þ

It is simple to verify that there is a direct connection between the image points x¢ M x andthe object point X:

x0 ¼ KRK�1x ¼ Hx: ð10Þ

Brown and Lowe (2003) proposed a solution in the case of a small shift of the cameracentre. However, the tests with different paintings and different image sequences, proved thatthe use of equation (10) is sufficient to correctly align images. All tests were carried out byregistering the images and then overlapping them to create a sort of multispectral tensor. Thevisual inspection, with transparency effects and edges extracted and then superimposed ontothe whole tensor, showed a good consistency between the resampled images. Therefore, theseare suitably corrected for further processing in this application area.

The geometric constraint given by the homography is used to remove incorrect matcheswith a RANSAC solution (Fischler and Bolles, 1981), which is based on the randomextraction of a minimal data-set and, iteratively, on the identification of a data-set that doesnot contain gross errors. Here H is computed using four corresponding points, while theother points are projected according to equation (3) and are compared with the measuredvalues. This operation is repeated N times in order to find a good data-set and detect alloutliers. Finally, the inliers are used to estimate H with a least squares solution. Due to thegenerally high number of extracted features, a reduction and more uniform distribution of allimage points can be applied. This is carried out with a bucketing approach (Zhang, 1995),subdividing the images with a regular grid and selecting only one point for each cell of thegrid.

The pair-wise feature matching and inliers estimation procedures are repeated for alladjacent images in order to compute a homographic transformation for each pair. Then eachimage is resampled in order to derive a new image correctly registered with the selectedreference image. Thus all consecutive image pairs (i, i + 1; i + 1, i + 2; …) are independentlyanalysed to estimate progressive combinations of 2D transformations. For a generic image pairi and i + 1 this relation can be written in homogenous coordinates as

xi ¼ Hiþ1xiþ1: ð11Þ

The advantage of this notation is related to the possibility to combine consecutivehomographic transformations by simply multiplying successive matrices. Therefore, thehomography between a generic image h and another image k is given by

xh ¼ Hhþ1Hhþ2 . . .Hkxk: ð12ÞUsing this formulation, the central image of the sequence is generally assumed as a referencefor a progressive concatenation of all the other images.




Compared with other registration methods (like the MMI), this approach proved to bequicker and not to require any initial approximation of the unknown transformation parameters.The procedure has a limited computational cost and demonstrated a good robustness even inthe case of poor texture or a limited amount of shared information.

Fig. 4 shows the registration results of two multispectral images (UV fluorescence at 450and 550 nm) on the San Girolamo painting by Alessandro Rosi, from a private collection inPisa, Italy. The first feature detection and correspondence establishment provided for 774inliers (Fig. 4(a)) while the reduction procedure resulted in 88 correspondences (Fig. 4(b))with a better spatial distribution. The final composite RGB image, obtained by combining theradiometric content of three multiple images in the visible spectrum, is shown in Fig. 4(c). Theentire sequence of multispectral images was composed of eight pictures (2048 · 3072 pixels).The alignment procedure took approximately 15 minutes with an average mean square error(sigma naught—r0) of the computed homographic transformation of 0Æ6 pixels.

Fig. 5 shows another result for a frescoed area in the Hunting and Fishing tomb of theEtruscan Necropolis in Tarquinia, Italy. The acquired sequence was composed of 20multispectral images. Fig. 5(a) is the first picture of the acquired UV fluorescence sequence(450 nm waveband) while Fig. 5(b) is the last one of the near IR reflectance sequence (1100 nmwaveband). As shown by the figures, the radiometric differences between the two images areremarkable. Fig. 5(c) and Fig. 5(d) illustrate the detail of the area that is highlighted in

(b) (c)

(a)

Fig. 4. The San Girolamo painting. Extracted correspondences before (a) and after (b) the reduction and regu-larisation procedure; (c) final RGB image obtained merging the registered monochromatic images.




(a)

(c)

(b)

(d)

(e) (f)

Fig. 5. Multispectral images of an Etruscan fresco in the Hunting and Fishing tomb of Tarquinia, Italy. First(a) and last (b) images of the multispectral sequence. Overlaid edges extracted from a near IR image onto a visibleone before (c) and after (d) the registration procedure. Composed IR false colour (e) and composed UV fluorescence

image (f).




Fig. 5(b). Fig. 5(c) shows one image of the multispectral sequence (visible spectrum at600 nm) overlaid with the edges extracted from the last image of the near IR range (1000 nm)using the Canny edge detection operator. A clear and significant misalignment between thedata (larger than 7 pixels) is visible. After the registration of the multispectral sequence, a goodcorrespondence is achieved (Fig. 5(d)). Fig. 5(e) is the IR false colour picture of the fresco,obtained by combining near IR and visible images, while Fig. 5(f) is the UV fluorescencepicture, obtained by properly combining seven multispectral UV images. It is clearly visiblehow multispectral data could reveal visual information that is hidden to the naked eye.

Once all the images have been registered, they can be overlapped in order to create a kindof image tensor. This tensor of information can be used to classify the imaged areas andrecognise occurring materials by producing the reflectance and fluorescence spectra for eachsingle pixel (Fig. 6). Different materials, for example, original or those added later, will showdifferent spectra. Data collected from each single pixel can be compared among each other andclassified based on their similarities (for example, using the Spectral Angle Mapper measure)(Pelagotti et al., 2008). Therefore, areas having the same colour under visible light, but havinga different chemical composition, can be differentiated in this way (Fig. 7).

Clustering of Multispectral Images via the Spectral Angle Mapper Algorithm

When the system chosen to acquire the multispectral image set has a limited number offilters, as in the present case, it is far from an ideal sampling system and the reflectance oremission signals cannot be exactly reconstructed. However, if images acquired with differentspectral bands (visible, near IR and UV-induced visible fluorescence) are concurrentlyprocessed, it is possible to successfully cluster areas having a similar behaviour and therefore,most likely, a similar chemical composition. In order to cluster data correctly, an appropriatesimilarity algorithm was selected. Spectral Angle Mapper (SAM) is a method for directly

Visible Reflectance

15000

10000

5000

0

15000

10000

5000

0

15000

10000

5000

0

15000

10000

5000

0

15000

10000

5000

0

15000

10000

5000

0800 900 1000 1100

800 900 1000 1100

400 500 600 700

400 500 600 700 400 500 600 700

400 500 600 700

Visible Reflectance

Near Infrared Reflectance

Near Infrared Reflectance

UV Induced Visible Band Fluorescence

UV Induced Visible Band Fluorescence

Fig. 6. Comparison between reflectance and fluorescence spectra of two different red pigment areas (upper andlower graphs). Reflectance spectra in the visible range are very alike, and so is their appearance to the naked eye.However, some differences, even if small, are noticeable in the spectra. The chosen algorithm for similarity

measurement helps to group homologous pixels and distinguish different painting materials.




comparing a spectrum to a known spectrum. This method treats both spectra as vectors andcalculates the spectral angle between them: two image points (represented by their ownspectrum) are judged to be similar when the angle between them is smaller than a predefinedthreshold. If the reflectance spectra of two different pigments are stored in two vectors x and y,the angle between them h is computed according to

hðx; yÞ ¼ arccosx; yh i

xk k yk k

� �; 0 � h � p

2: ð13Þ

Since SAM uses only the vector direction and not the vector length N, where N is the numberof the spectral bands considered, the algorithm is insensitive to illumination changes; however,the information concerning the difference in vector length A could be used to distinguishlighter and darker colours:

Aðx;yÞ ¼ xk k � yk k ¼

ffiffiffiffiffiffiffiffiffiffiffiffiXN

i¼1x2i

vuut �

ffiffiffiffiffiffiffiffiffiffiffiffiXN

i¼1y2i

vuut : ð14Þ

3D Surveying and Deformation Monitoring using Photogrammetry

Photogrammetry may be used to survey and determine the possible deformations ofpaintings both on canvas and on wood. Photogrammetric measurements can provide:

(a) deformation analysis of single points, defined by artificial targets or natural features,which can be identified and measured at different epochs;

(b) a 3D dense and detailed digital surface model (DSM), using automated imagematching procedures; and

(c) high-resolution ortho-images.

(a) (b) (c)

Fig. 7. Detail of the Etruscan fresco seen in the visible domain (a) and after the analysis of the multispectral data,which revealed an original red colour (b) and a later one, probably added during an old restoration (c).




Point-based deformation methods are often not applicable, as targets cannot generally beplaced on ancient painting surfaces. D’Amelio and Lo Brutto (2009) report some results of asimulation on a targeted canvas. Detailed DSMs are more appropriate to determine the globalshape and deformation of the entire object in a single epoch (comparing the derived surfacemodel with a theoretical plane) or in multiple sessions (comparing the surface models acquiredat different epochs and deriving the relative movements). Although the texture information ofpaintings is often complex for automated image matching algorithms (due to specularity, darkareas and so on) resulting in noisy DSMs, most of the time ortho-images can be derived fortexture mapping purposes or as reference for setting up an informative system including otherdiagnostic data.

3D Surveying and Deformation Monitoring using Active Sensors

3D imaging and active sensors, in particular interferometric techniques such as micro-profilometers based on conoscopic homography (Fontana et al., 2005), short-range laserscanners (Blais et al., 2007) and structured light systems (Akca et al., 2007), provide accuratehigh-resolution 3D digital records of an object and can be used for documentation and digitalarchiving, conservation, virtual restoration, deformation monitoring, replication, display andvisualisation. Indeed, the advantages of 3D imaging systems, particularly in the heritage field,are nowadays recognised and widely accepted. Although they are very expensive systems,active sensors are able to survey even textureless areas in detail, and deliver dense and accurate3D data useful for:

(a) 3D reconstruction of surface features at a very high geometric resolution (Blais et al.,2005);

(b) instant evaluation of the entire shape of the painting; and(c) multi-temporal analysis and movement measurement of the support’s shape.

For large paintings (several metres), TOF sensors could be employed to determine theoverall shape of the artwork and check its deviation from theoretical planarity. Triangulation-based range sensors or conoscopic micro-profilometers are, instead, more suited to studying thesmall detailed features of the painted surface, analyse craquelure patterns, highlight anddocument colour raisings, detachments or engraving as well as deliver high-resolution 3Dmodels.

Detailed 3D Surveying and Deformation Analysis

Different studies were carried out on some wooden paintings by Giorgio Vasari (1511–74) located in the church of Bosco Marengo (Alessandria, Italy). For two works of art(Martirio di San Pietro and Giudizio Universale), the goal of restorers and conservators was toderive the current shape of the entire painted wooden structures to immediately see, at themacro scale, if significant deformations were present. A Leica ScanStation 2 TOF laser scannerwas used to survey both artworks at a 3mm geometric resolution and retrieve the possibledeformation from the ideal flat plane. The surveying results, shown in Fig. 8, identified auniform bending towards the borders of the first artwork of up to 2Æ3 cm but no significantdeformation (less than 1 cm) in the second one.

For a third painting (Adorazione dei Magi), a more detailed survey was required. It wascarried out using a ShapeGrabber laser scanner (Table I).

The entire painting was firstly surveyed with a SG1002 head that provides a geometricresolution of 0Æ3mm. Eighty-four scans were acquired and then aligned in a unique point cloud




of approximately 200 million points (Fig. 9). The deformation of the wooden structure of thepainting was also derived, comparing the 3D surface model produced with the ideal plane(Fig. 10(b)). For a photo-realistic visualisation, eight images captured with a Nikon D3X (24Æ5megapixels) were mosaicked and orthorectified to produce a final texture of approximately 141megapixels. Such huge metric images are very useful for restoration purposes and can also beeasily visualised or shared remotely using interactive viewing technologies on the web (forexample, Zoomify and Microsoft HD View).

Following the requirements of restorers and conservators, some specific areas of thepainting were digitised at 0Æ1mm resolution by using the ShapeGrabber head SG102. This

(a) (b)

Fig. 8. (a) The Martirio di S. Pietro painting (approximately 4Æ5m · 2Æ5m) and its actual state of macro defor-mation showing a uniform bending towards the borders up to 2Æ3 cm. (b) The Giudizio Universale painting(approximately 6m · 3m) with no significant deformation. Some gaps in the acquired 3D data, due to the dark

colours of the painting (no laser responses), are also visible.

Table I. Specifications for the ShapeGrabber laser scanner (head SG1002 andSG102) employed for the surveying of the Adorazione dei Magi painting.

SG1002 SG102

Working range (Z) 300 to 900mm 120 to 170mmLateral resolution (XY) 300lm 100lmRange resolution (Z) 30 lm 5 lmNoise (1r) 150lm 25 lm




Fig. 9. The Adorazione dei Magi painting (approximately 2Æ6m · 2m), surveyed with 84 scans at 0Æ3mm geo-metric lateral resolution. The final textured 3D model contains approximately 55 million polygons.

(a) (b)

(c) (d)

Fig. 10. (a) The aligned 84 scans (approximately 200 million points) of the Adorazione dei Magi painting, shownwith the reflectance information of the scanner; (b) the corresponding deformation map of the painting: red areasrepresent bended areas of approximately 1 cm above the medium plane (the legend goes from ±12mm); (c) a visibleimage of paint detachments; and (d) the 3D surveying of the same area rendered with gazing light to clearly show

detachments.




enabled the accurate documentation of some brush strokes, some significant paint detachments(Fig. 10(d)) and highlighting the roughness of the painting.

3D Surveying and Deformation Monitoring using Integrated Techniques

As shown in many surveying and documentation projects (El-Hakim et al., 2008; Guidiet al., 2009; Remondino et al., 2010; Fassi et al., 2011), nowadays the best 3D modellingresults are generally achieved by integrating different surveying techniques. The same concepthas been often applied to paintings for complete studies and analyses (Fontana et al., 2003;Bacci et al., 2006; Mohen et al., 2006; Pires et al., 2007).

A representative example of the integration between different techniques for paintingsurveying, deformation analyses and diagnostic studies was performed on the artwork ofAndrea Mantegna (1431–1506) known as Pala Trivulzio (approximately 2Æ6m · 1Æ8m,Fig. 11(a)), currently hosted by Castello Sforzesco in Milan, Italy. The artwork is a canvaspainting, which has an unstable structure (compared with wooden paintings), with possiblemovements due to environmental conditions and changes (temperature, humidity, vibrations,etc.).

For restoration purposes, the painting was removed from its support. The state of health ofthe painting was investigated using visible, UV, near IR and X-ray images as well as scanspectrophotometry (Pesci and Toniolo, 2008). However, the geometric shape and deformationsof the canvas were surveyed using photogrammetry and TOF laser scanning.

Photogrammetric analysis was employed to survey at four different epochs the macro off-plane deformation of the canvas during its restoration period. The choice of photogrammetrywas driven by the high accuracy achievable in the DSM generation and the possibility of

(a) (b)

Fig. 11. (a) The Pala Trivulzio painting; and (b) the layout of the photogrammetric block (40 images) adopted forthe generation of the DSM of the canvas.




producing high-resolution ortho-images to be used as a reference for the visualisation of all thediagnostic data. At the beginning of the restoration and surveying period, the painting waslocated in a room just above the underground railway line of Milan, leading to frequentvibrations that prevented the use of highly precise range sensors (such as phase-shift ortriangulation-based scanners). Therefore, laser scanning was employed in one epoch only tocheck and compare the photogrammetric DSMs.

Photogrammetric surveys were carried out before restoration (March 2005), duringrestoration (May and July 2006) and at the end of the restoration (March 2007). For eachsurveying epoch a photogrammetric block, made up of 40 images divided into five strips witheight frames per strip, was acquired (Fig. 11(b)). A calibrated Rollei DB44 digital camera witha Phaseone CCD sensor (4080 · 4076 pixels, 9 lm pixel size) and a 40mm lens was used. Theimage acquisition distance was roughly 1 m with a consequent pixel resolution (footprint) ofabout 0Æ2mm. A baseline of about 35 cm allowed about 60% fore-and-aft overlap (along strip)and 20% lateral overlap (across strip).

To establish a unique reference system between the photogrammetric and 3D laserscanning measurements, 99 targets were placed on the canvas frame, while further naturalpoints were identified within the texture of the painting. All targets were measured with a

Fig. 12. Double station acquisition scheme.

metri0·00–0·01

0·01–0·02

0·02–0·03

0·03–0·04

0·04–0·05

Fig. 13. Raster visualisation of the photogrammetric DSMs produced in the four epochs (from left to right: March2005, May 2006, July 2006 and March 2007). The derived DSMs clearly show the movement of the canvas.




reflectorless total station (Leica TPS 1200), using a double station acquisition set-up with agraduated 2m long horizontal rod to improve the scale definition (Fig. 12). The measurementscheme is a triangle with a base AB of 5Æ5m and a base to painting distance of approximately7m. As shown in Fig. 12, the measurement was performed from station A to the horizontal rodand then to the targets on the painting. The same operation was then repeated from station B,moving the horizontal rod to station A. This solution achieved a final average accuracy in the

(a) (b)

(d)(c)

metri

Profile 1

Profile 2

Profile 3

metri

0·01–0·02

0·02–0·03

0·03–0·04

0·04–0·05

86·10

86·05

86·00

85·95

85·90

86·10

86·05

86·00

85·95

85·90

86·10

86·05

86·00

85·95

85·90

86·10

86·05

86·00

85·95

85·90

86·10

86·05

86·00

85·95

85·90

86·10

86·05

86·00

85·95

85·90

0·0 0·5 1·0 1·5 2·0 2·5

0·0 0·5 1·0 1·5 2·0 2·5

0·0 0·5 1·0 1·5 2·0 2·5 0·0 0·5 1·0 1·5 2·0 2·5

0·0 0·5 1·0 1·5 2·0 2·5

0·0 0·5 1·0 1·5 2·0 2·5

0–0·01 0–0·01

0·01–0·02

0·02–0·03

0·03–0·04

0·04–0·05

Profile 1

Profile 2

Profile 3

86·10

86·05

86·00

85·95

85·90

86·10

86·05

86·00

85·95

85·90

86·10

86·05

86·00

85·95

85·90

0·0 0·5 1·0 1·5 2·0 2·5

0·0 0·5 1·0 1·5 2·0 2·5

0 0 0 5 1 0 1 5 2 0 2 5 0 0 0 5 1 0 1 5 2 0 2 5

0·0 0·5 1·0 1·5 2·0 2·5

0·0 0·5 1·0 1·5 2·0 2·5

Fig. 14. Raster comparison of the DSM extracted from laser acquisition (a) and the photogrammetric one (b).Some profiles of cross sections on the laser scanning (c) and photogrammetric DSM (d).




computed 3D coordinates of ±0Æ2mm in the X and Y directions (plane of the canvas) and±0Æ4mm along the Z axis.

The images were oriented by means of a least squares bundle adjustment. The finalaccuracy of the computed object coordinates was ±0Æ35mm in the canvas plane (XY), and±0Æ6mm orthogonally to the canvas (Z direction). Subsequently a dense point cloud wasextracted and a TIN surface model produced for the generation of a high-resolution ortho-image (pixel size 0Æ2mm on the canvas) for each epoch.

The comparison between the derived surface models revealed significant movements ofthe canvas before and after it was removed from its frame (Fig. 13). Amazingly, the surface

Fig. 15. Two examples of a high-resolution ortho-image of the Pala Trivulzio painting used inside GIS envi-ronments for archiving, comparing and understanding results obtained from different investigation techniques.




geometry from the last survey (after restoration) showed that the canvas was no longer flat, asexpected, but it had assumed a bent and undulating shape, similar to the situation before therestoration.

The laser scanning surveying was performed using a 5mm geometric sampling step with aLeica HDS3000 and four stations, in order to have redundant data to eliminate any possiblerandom movement of the canvas during the acquisition. The successive geometric comparison(cross sections and entire surface) between the photogrammetric and laser scanning surfacemodels, did not reveal any significant difference (Fig. 14).

Finally, all data (diagnostic, photogrammetry and 3D laser scanning) were collected andused in a GIS environment (Fig. 15) in order to: link other geo-referenced information to thecanvas (in both raster/vector formats); visually explore canvas details; and assess the off-planedeformation of the canvas shape by superimposing the DSMs generated at different epochs.

The results obtained from the integrated survey helped conservators to visually andgeometrically document the painting. It also led them to analyse unconsidered dynamics of thestatic behaviour of the Pala Trivulzio and modify the initial restoration project.

Conclusions

Non-invasive documentation and assessment techniques are nowadays fundamental for artobjects. In this paper an overview of 3D surveying and multispectral/multimodal imagingmethods, applied to paintings’ monitoring and studying, has been presented. It has beendemonstrated how multispectral images can be quickly and accurately registered, and thenfurther processed by clustering and classification, for non-disruptive material detection.Moreover, it has been shown how image- and range-based 3D modelling can be useful forarchiving data and deriving maps of a painting’s deviation from planarity. Divergences thatmay be due to an unwanted warping of the support, or damage to the surface, provide essentialinformation to correctly plan interventions and prevent further deterioration. The best resultsare obtained by comparing and understanding different surveying methods. While 3D imagingand photogrammetric systems are widely available, standards, best practices and comparativedata are still almost non-existent. In particular, the specifications stated by the 3D imagingsystem manufacturers still generate a lot of confusion among end-users. Therefore, if theheritage and museum community is to benefit from adopting these common technologies andtechniques for their daily work and for intervention policies, clearer standards andspecifications are really needed.

High-quality and high-precision products that can be derived using photogrammetric and3D laser scanning surveying allow the acquisition of a geometric reference for the collectionand representation of other diagnostic data as well. Thus, each painting can be described by a2D map and its associated DSM, which are eminently suitable to be managed in desktop andweb GIS packages. Thus by adding other raster and vector data, a comparison and integrationof spatially related information could be carried out, with the possibility to extract many morefindings than the ones achievable by each investigating technique alone.

Acknowledgements

The authors are thankful to the Sovrintendenza per i Beni Storici, Artistici edEtnoantropologici of the Piedmont region, Kermes and Art-Test sas. Acknowledgements alsogo to Cristiana Achille, Federico Prandi, Luigi Fregonese and Lucia Toniolo (Politecnico diMilano) for their contribution to the ‘‘Pala Trivulzio’’ project.




references

Akca, D., Gruen, A., Breukmann, B. and Lahanier, C., 2007. High definition 3D-scanning of art objects andpaintings. Optical 3D Measurement Techniques VIII, Zurich, Switzerland. 50–58.

Bacci, M., Casini, A., Picollo, M., Radicati, B. and Stefani, L., 2006. Integrated non-invasive technologiesfor the diagnosis and conservation of the cultural heritage. Journal of Neutron Research, 14(1): 11–16.

Barazzetti, L., Fangi, G., Remondino, F. and Scaioni, M., 2010a. Automation in multi-image sphericalphotogrammetry for 3D architectural reconstructions. 11th International Symposium on Virtual Reality,Archaeology and Cultural Heritage, Paris, France. 7 pages.

Barazzetti, L., Scaioni, M. and Remondino, F., 2010b. Orientation and 3D modelling from markerless ter-restrial images: combining accuracy with automation. Photogrammetric Record, 25(132): 356–381.

Barni, M., Pelagotti, A. and Piva, A., 2005. Image processing for the analysis and conservation of paintings:opportunities and challenges. IEEE Signal Processing Magazine, 22(5): 141–144.

Bay, H., Ess, A., Tuytelaars, T. and van Gool, L., 2008. Speeded-up robust features (SURF). Computer Visionand Image Understanding, 110(3): 346–359.

Bonifazzi, C., Carcagni, P., Fontana, R., Greco, M., Mastroianni, M., Materazzi, M., Pampaloni, E.,Pezzati, L. and Bencini, D., 2008. A scanning device for VIS–NIR multispectral imaging of paintings.Journal of Optics A: Pure and Applied Optics, 10(6): 064011. 9 pages.

Blais, F., Taylor, J., Cournoyer, L., Picard, M., Borgeat, L., Dicaire, L. G., Godin, G., Beraldin,J.-A., Rioux, M., Lahanier, C. and Aitken, G., 2005. Ultra-high resolution imaging at 50 lm using aportable XYZ-RGB color laser scanner. Proceedings of the Workshop ‘‘Recording, Modeling and Visuali-zation of Cultural Heritage’’, Centro Stefano Franscini, Monte Verita, Ascona, Switzerland. 15 pages.

Blais, F., Taylor, J., Cournoyer, L., Picard, M., Borgeat, L., Godin, G., Beraldin, J.-A., Rioux, M. andLahanier, C., 2007. Ultra high-resolution 3D laser color imaging of paintings: the Mona Lisa by Leonardo daVinci. Proceedings of 7th International Conference on Lasers in the Conservation of Artworks, Madrid. 435–440.

Brown, M. and Lowe, D. G., 2003. Recognizing panoramas. International Conference on Computer Vision, 2:1218–1225.

Cappellini, V., Del Mastio, A., De Rosa, A., Piva, A., Pelagotti, A. and El Yamani, H., 2005. Anautomatic registration algorithm for cultural heritage images. IEEE International Conference on ImageProcessing, II: 566–569.

Castro, E. and Morandi, C., 1987. Registration of translated and rotated images using finite Fourier transforms.IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(5): 700–703.

Colantoni, P., Pillay, R., Lahanier, C. and Pitzalis, D., 2006. Analysis of multispectral images of paintings.Proceedings of 14th European Signal Processing Conference, Florence. 5 pages.

D’Amelio, S. and Lo Brutto, M., 2009. Close range photogrammetry for measurements of paintings surfacedeformations. International Archives of the Photogrammetry, Remote Sensing and Spatial InformationSciences, 38(5-W1). 6 pages (on CD-ROM).

El-Hakim, S., Beraldin, J.-A., Remondino, F., Picard, M., Cournoyer, L. and Baltsavias, M., 2008.Using terrestrial laser scanning and digital images for the 3D modelling of the Erechteion, Acropolis of Athens.Proceedings of Digital Media and its Application in Cultural Heritage, Amman, Jordan. 3–16.

Fassi, F., Achille, C. and Fregonese, L., 2011. Surveying and modelling the main spire of Milan Cathedralusing multiple data sources. Photogrammetric Record, 26(136): 462–487.

Fischler, M. A. and Bolles, R. C., 1981. Random sample consensus: a paradigm for model fitting withapplications to image analysis and automated cartography. Communications of the ACM, 24(6): 381–395.

Fitzpatrick, J. M. and Sonka, M. (Eds.), 2000. Handbook of Medical Imaging, Volume 2. Medical ImageProcessing and Analysis. SPIE Press, Bellingham, Washington, DC. 1250 pages.

Fontana, R., Gambino, M. C., Greco, M., Marras, L., Materazzi, M., Pampaloni, E., Pezzati, L. andPoggi, P., 2003. Integrating 2D and 3D data for diagnostics of panel paintings. Optical Metrology for Arts andMultimedia, SPIE, 5146: 88–98.

Fontana, R., Gambino, M. C., Greco, M., Marras, L., Pampaloni, E. M., Pelagotti, A., Pezzati, L. andPoggi, P., 2005. 2D imaging and 3D sensing data acquisition and mutual registration for painting conser-vation. Videometrics VIII, SPIE, 5665: 51–58.

Guidi, G., Atzeni, C., Seracini, M. and Lazzari, S., 2004. Painting survey by 3D optical scanning. The case ofAdoration of the Magi by Leonardo da Vinci. Studies in Conservation, 49(1): 1–12.

Guidi, G., Remondino, F., Russo, M., Menna, F., Rizzi, A. and Ercoli, S., 2009. A multi-resolutionmethodology for the 3D modeling of large and complex archaeological areas. International Journal ofArchitectural Computing, 7(1): 39–55.




Hartley, R. I., 1997. In defense of the eight-point algorithm. IEEE Transactions on Pattern Analysis andMachine Intelligence, 19(6): 580–593.

Hartley, R. I. and Zisserman, A., 2004. Multiple View Geometry in Computer Vision. Second edition.Cambridge University Press, Cambridge. 672 pages.

Lahanier, C., Aitken, G., Pillay, R., Beraldin, J.-A., Blais, F., Borgeat, L., Cournoyer, L., Picard,M., Rioux, M., Taylor, J., Breuckmann, B., Colantoni, P. and de Deyne, C., 2005. Two-dimensionalmulti-spectral digitization and three-dimensional modelling of easel paintings. 14th Triennial Meeting, TheHague. International Council of Museums, Rome. 19 pages.

Liang, H., Saunders, D. and Cupitt, J., 2005. A new multispectral imaging system for examining paintings.Journal of Imaging Science and Technology, 49(6): 551–562.

Lowe, D. G., 2004. Distinctive image features from scale-invariant keypoints. International Journal of ComputerVision, 60(2): 91–110.

Luhmann, T., Robson, S., Kyle, S. and Harley, I., 2006. Close Range Photogrammetry: Principles, Tech-niques and Applications. Whittles, Caithness, Scotland. 510 pages.

Mikolajczyk, K. and Schmid, C., 2005. A performance evaluation of local descriptors. IEEE Transactions onPattern Analysis and Machine Intelligence, 27(10): 1615–1630.

Mohen, J.-P., Menu, M. and Mottin, B., 2006. Mona Lisa: Inside the Painting. Harry N. Abrams, New York.128 pages.

Pelagotti, A., Del Mastio, A., De Rosa, A. and Piva, A., 2008. Multispectral imaging of paintings. IEEESignal Processing Magazine, 25(4): 27–36.

Pelagotti, A., Del Mastio, A., Uccheddu, F. and Remondino, F., 2009. Automated multispectral texturemapping of 3D models. 17th European Signal Processing Conference, Glasgow: 1215–1219.

Pesci, F. and Toniolo, L. (Eds.), 2008. Andrea Mantegna. La Pala di San Zeno. La Pala Trivulzio. Conoscenza,conservazione, monitoraggio. Marsilio Editore, Venice, Italy. 280 pages.

Pires, H., Marques, P., Henriques, F. and Oliveira, R., 2007. Integrating laser scanning, multispectralimagery and GIS in C&R documentation practices: a first approach using two XVIth century wood paintingsfrom Convento de Cristo in Tomar. Proceedings of XXI CIPA Symposium, Athens, Greece. 6 pages.

Polder, G. and van der Heijden, G. W. A. M., 2001. Calibration and characterization of spectral imagingsystems. SPIE, 4548: 10–17.

Reddy, B. S. and Chatterji, B. N., 1996. An FFT-based technique for translation, rotation, and scale-invariantimage registration. IEEE Transactions on Image Processing, 5(8): 1266–1271.

Remondino, F., Pelagotti, A., Del Mastio, A. and Uccheddu, F., 2009. Novel data registration techniquesfor art diagnostics and 3D heritage visualization. Optical 3D Measurement Techniques IX, 1: 1–10.

Remondino, F., Rizzi, A., Girardi, S., Petti, F. M. and Avanzini, M., 2010. 3D ichnology—recoveringdigital 3D models of dinosaur footprints. Photogrammetric Record, 25(131): 266–282.

Ribes, A., Schmitt, F., Pillay, R. and Lahanier, C., 2005. Calibration and spectral reconstruction for CRI-SATEL: an art painting multispectral acquisition system. Journal of Imaging Science and Technology, 49(6):563–573.

Robson, S., Bucklow, S., Woodhouse, N. and Papadaki, H., 2004. Periodic photogrammetric monitoring andsurface reconstruction of a historical wood panel painting for restoration purposes. International Archives ofthe Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(5): 395–400.

Stone, H. S., le Moigne, J. and McGuire, M., 1999. The translation sensitivity of wavelet-based registration.IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(10): 1074–1081.

Viola, P. and Wells, W. M., 1997. Alignment by maximization of mutual information. International Journal ofComputer Vision, 24(2): 137–154.

Vosselman, G., 1992. Relational matching. Lecture Notes in Computer Science, 628. Springer, Berlin, Germany.190 pages.

Wyawahare, M. V., Patil, P. M. and Abhyankar, H. K., 2009. Image registration techniques: an overview.International Journal of Signal Processing, Image Processing and Pattern Recognition, 2(3): 11–28.

Zhang, Z., 1995. Parameter estimation techniques: a tutorial with application to conic fitting. INRIA ResearchReport 2676, Sophia-Antipolis, France. 34 pages.

Zhang, Z., Zhang, J., Liao, M. and Zhang, L., 2000. Automatic registration of multi-source imagery based onglobal image matching. Photogrammetric Engineering & Remote Sensing, 66(5): 625–629.

Zitova, B. and Flusser, J., 2003. Image registration methods: a survey. Image and Vision Computing, 21(11):977–1000.




Resume

Cet article presente une revue de nombreuses campagnes diagnostiquesrealisees en vue d’evaluer l’etat de conservation de peintures. Les caracteristiquesa prendre en compte pour les etudes patrimoniales sont recensees, et deux grandsdomaines sont consideres: les analyses radiometriques utilisant l’imageriemultibande et les releves 3D par photogrammetrie et balayage laser pour l’etudedes deformations geometriques. Les activites decrites ont ete menees par des equipesinterdisciplinaires composees de photogrammetres, d’historiens de l’art, derestaurateurs et d’experts en techniques diagnostiques non desctructives.

Zusammenfassung

Dieser Beitrag bietet einen Uberblick uber eine Anzahl von diagnostischenKampagnen, um den Konservierungsstatus von Gemalden zu bewerten. Es wurdendie spezifischen Anforderungen fur Anwendungen fur ein Kulturerbe untersucht unddokumentiert. Dabei spielen zwei Analysen eine Hauptrolle: die radiometrischeAnalyse mit Hilfe von Multispektralaufnahmen und eine 3D Vermessung zurDeformationsanalyse durch Photogrammetrie und Laserscanning. Die beschriebenenArbeiten wurden durch ein interdisziplinares Team durchgefuhrt, das sich ausPhotogrammetern, Kunsthistorikern, Restauratoren, und Experten fur zerstorungs-freie Diagnosetechniken zusammensetzte.

Resumen

En el artıculo se reportan algunas de campanas de diagnostico para evaluar elestado de conservacion de pinturas. Se analizan las caracterısticas especıficas quelas aplicaciones de patrimonio cultural requieren, considerandose dos camposprincipales: analisis radiometrico usando imagenes multiespectrales y analisis dedeformaciones geometricas mediante la fotogrametrıa y el escaneado laser. Elanalisis lo ha llevado a termino un equipo interdisciplinar, formado porfotogrametristas, historiadores del arte, restauradores y expertos especializados entecnicas de diagnostico no destructivas.




review of geometric and radiometric analyses of...

Documents