computer aided analysis of the buildings

Journal of Cultural Heritage 1 (2000) 59–67© 1999 Editions scientifiques et medicales Elsevier SAS. All rights reserved

S1296-2074 (99 )00115 -6/FLA

Computer aided analysis of the buildings

Laura Moltedoa*, Giuseppe Mortellitib, Ovidio Salvettic, Domenico Vitulanod

aIstituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Ricerche, IAC-CNR, Viale del Policlinico 137,00161 Rome, Italy

bAccademia Navale, 57100 Livorno, ItalycIstituto di Elaborazione della Informazione, Consiglio Nazionale delle Ricerche, IEI-CNR, Via S. Maria 46,

56126 Pisa, ItalydIstituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Ricerche, IAC-CNR, Viale del Policlinico 137,

00161 Rome, Italy

Received 29 May 1998; accepted 15 July 1999

Abstract – This paper examines how information systems can assist experts to analyse the state of conservation ofbuildings of historic importance. The main focus is on image compression, characterisation and recognition, all of whichare fundamental for defining a database on the state of conservation. In particular, an overview of available methods ispresented for characterising the structure of materials and recognising the various degrees of degradation. A new unifiedapproach to image compression, characterisation and recognition is also proposed. Applications are included forprocessing stone images. © 1999 Editions scientifiques et medicales Elsevier SAS

Keywords: cultural heritage / state of conservation / material degradation / image characterisation / image recognition /image coding

At a first level, computer assistance supplies func-tions for archiving and accessing multimedia data-bases. In this area many European research projectsare still supported by EC programmes, such asACTS, ESPRIT 3-4, IMPACT2, INFO2000, TELE-MATICS2C and RACE1-2.

At higher levels, computing techniques should beprovided in order to guarantee that data are madeindependent from the acquisition techniques, and areas objective as possible. Very often the informationacquired should be properly pre-processed beforebeing included in a database, and new data should beobtained using dedicated computing procedures, forinstance by applying image analysis and synthesistechniques. In many cases, appropriate fusion ofsome data might contribute greatly to deepen aspecific knowledge of the state of conservation.

1. Introduction

Current methods for documenting and managingcultural heritage are based on providing tools fordata archiving, accessing and querying. Much workis being carried out in many countries in definingappropriate databases, as testified by the contribu-tions at recent conferences [1, 2].

A common approach is to consider heterogeneousand often multimedia data, hence not only alphanu-meric data but also images, videos and graphicinformation. The main characteristics a databaseshould implement are appropriateness and effi-ciency; appropriateness to fulfil objectivity and con-formity to a standardised lexicon and efficiency byallowing easy and perceptive user-interfaces.

* Correspondence and reprints: [email protected]

L. Moltedo et al. / J. Cult. Heritage 1 (2000) 59–6760

In this regard, the RAPHAEL EC programme veryrecently launched actions in the field of culturalheritage conservation where research projects, in-cluding a wide spectrum of information technologytechniques, are required.

Bearing in mind, for instance, the documentationof the state of conservation of buildings of historicimportance, a computer aided approach is a stepforward from the traditional ‘naked-eye’ analysis.This activity consists of an accurate observationfollowed by a description of the building that theexpert compiles looking at the monument and usingwords from a standardised lexicon. By direct visualanalysis, information can be obtained, for instance,on significant dimensions, architectonic typology,typology of the main structures, materials, and theeffects of structural, physical-chemical-biologicaland typo-morphological degradation.

This paper focuses on image compression andimage characterisation and recognition, both ofwhich are fundamental for defining a database onthe state of conservation of a building. In particular,methods are presented for characterising the struc-ture of the materials and recognising the variousdegrees of degradation. A new unified approach toimage compression, characterisation and recognitionis proposed in section 5.

We also present some examples and results onimages acquired from lapideous material from theRoman theatre in Aosta in northern Italy.

Section 2 provides a brief overview of computersupport for cultural heritage conservation andrestoration. This is followed by a description of theframework and the general aims of our research.

2. Computer aided conservation

This section presents some interesting results bothin the analysis of painting surfaces along withthe characterisation of materials and theirmorphologies.

Computer assistance in restoring frescoes has beenapplied by developing a documentation systemabout the Sistine Chapel [3]. On the basis of aphotogrammetric survey of images of the vaults,processing procedures were able to reveal Michelan-gelo’s drawing techniques and the degradation wasautomatically mapped with computer plotted draw-ings. A complete photographic documentation of theentire restoration, including the simulation of differ-ently lighted images, was also made.

In the field of painting analysis, a systemic ap-proach is described in Argenti et al. [4]. Information

technology techniques were used during variousphases: acquisition, pre-processing, processing, dis-play and transmission of data. A statistic analysisand extraction of the structural elements of theimage allowed the authors to penetrate the under-paintings and to reveal the artist’s techniques. Insome cases, however, the underlying texture may bean obstacle for a good comprehension of the imageand so the authors developed an algorithm to sepa-rate the scene represented by the painting from thebackground texture. Restoration techniques for re-moving the degradation affecting the image are alsodescribed. The same research group has been in-volved in the chromatic correction of images derivedfrom photographic acquisition [5].

Virtual restoration of paintings is useful when realrestoration is impossible. In this field some researchcarried out on a personal workstation with reason-ably priced software has been able to present poten-tial visitors with such virtual restorations [6].

It is also opportune to mention here the archaeo-metric approach which started in western Europeand in the USA, and has more recently been adoptedin Italy [7]. Archaeometry is a branch of the sciencethat employs methods from physics, chemistry andcomputer science, such as X-rays, X-ray fluorescenceand NIR reflectometry, in order to study works ofart. It deals with non-destructive techniques (NDT)that allow the evaluation of quantities without per-forming the analysis of specimens. Tools for in situNDT research and diagnostics are applied, for in-stance, as an aid for the study of paintings and forthe characterisation of white marbles.

The analysis and recognition of morphologicalcharacteristics of stone images is carried out inFalcidieno et al. [8] by means of characteristicpoints, lines and regions. Characteristic points arerecognised, such as minimum, maximum and sellapoints, characteristic lines identify surface disconti-nuities and characteristic regions are zones wherethe surface has a uniform behaviour, for instancethe same curvature or a uniform slope. On the basisof a photogrammetric survey, cavities and fissuresrepresented in the stone image can be recognisedvisually.

A morphological approach [9] is also used in astructural texture analysis of carbonate rock weath-ered surface [10]. This approach is based on agranulometric and covariance analysis of grey tonefunctions of texturally representative areas ofimages.

Modelling techniques that evaluate the behaviourof materials that are subject to downgrading ordepreciation due to natural and/or artificial agents

L. Moltedo et al. / J. Cult. Heritage 1 (2000) 59–67 61

have been proposed by Moltedo and Salvetti [11]. Inthis approach, texture based image interpretationand generation are implemented.

A study has been carried out to show how repre-sentations of a monument’s geometry together withthe morphology and distribution of damage, thecomponent materials and their physical characteris-tics and environmental factors can be used to facili-tate the understanding of the degradation process ofthe monument itself [12]. In this case the representa-tions where the geometry has been reconstructed bymeans of photogrammetric data have been mappedto information extracted from a ‘naked-eye’ analysis.

A computer assisted procedure is used in order toevaluate the type and the extent of existing damageto historical buildings [13]. The geometry restoredby a photogrammetric survey is mapped with infor-mation such as the absolute and relative extent ofdamage obtained by means of appropriate imageanalysis procedures. Such data are useful for estimat-ing the costs of restoration or preservation.

2.1. Main goals of our research

In order to provide experts with support in com-puter aided analysis of states of conservation, aresearch was begun and continued within the frame-work of two Italian National Research Councilprojects, the strategic project ‘Knowledge throughimages: an application to Cultural Heritage’ (1994–1996) [14] and the special project ‘Safeguard ofCultural Heritage’.

Within the initial framework, the researchmethodologies were verified in a study-case, theRoman theatre of Aosta. This is a very typicalbuilding of the Augustean age whose componentmaterials are mainly travertine and pudding-stone.In the first project, image analysis and synthesisprocedures were investigated and a prototypal data-base, including all the information collected on thetheatre, was also provided [15]. In particular, to-gether with geometric and architectonic data andimages regarding both the building and a representa-tive set of the component ashlars, also alphanumericinformation coming from ‘naked-eye’ analysis wasincluded. This description was organised, in compli-ance with the lexicon of normalisation groups, bymeans of a subdivision into the following typologies:material and texture types (fine grained, mediumgrained, coarse grained), chemical, physical and bio-logical degradation, organised into four fundamentalfamilies (increase in material, lack of material,breakdown in continuity, colour alteration) andstructural degradation (such as cracking).

The second project deals with the definition oftools to support an expert at two levels. The firstlevel aims to define techniques for making the ‘nakedeye’ analysis objective (this analysis is usually fol-lowed to diagnose the state of conservation of abuilding or work of art). The second level simulates‘future’ scenarios of further degradation and conse-quent restoration, which is useful for deciding spe-cific treatment methodologies.

To reach these aims, a prototypal visual comput-ing environment, called C.H.A.A.T. (cultural her-itage assisted analysis tools) has been developed. It isoriented to both the analysis of complex images andthe simulation of pictorial dynamic events. In thissystem, a high-level user-interface allows access to adata archive including geometric, descriptive infor-mation and images of stone [16].

Many of the functionalities described in the sec-tion entitled ‘Our implementations’ are included inC.H.A.A.T.

3. Image compression

With regard to the cultural heritage field, compres-sion techniques are needed for building databasesthat contain a large amount of images. Compressiontechniques are usually split into two classes: losslessand lossy.

The first class includes techniques that achievecompression without decoded image degradation,that is without information loss. Common ap-proaches are based on data entropy – e.g. Huffman[17, 18], Lempel-Ziv [19], arithmetic coding andzero-length, which are applied above all on binaryimages. Nevertheless, none of these techniquesachieves high compression factors (for instance 2:1and 3:1).

When a higher compression factor is required(10:1, 20:1), lossy techniques can be used. Thesetechniques are based on the fact that the human eyeis not precise, i.e. it is not possible to perceive smallvariations in grey levels. In this case, obviously,there is a decoded image degradation, that is, aninformation loss. Using this class of compressiontechnique, three parameters have to be considered:compression factor, decoded image quality (usuallylower quality derives from higher compression fac-tor) and computational effort. The main techniquesare based on the cosine transform (JPEG actually isthe standard) [20], wavelet transform – WT [21](EPIC, packed wavelet, etc.), iterated function sys-tems – IFS or fractal transform [22], linear predic-tion coding – LPC [23], etc. Many hybrid


techniques, achieving very good results, are alsoused.

3.1. Our implementations

In Moltedo and Salvetti [11], a procedure is de-scribed which implements the well-known fractalapproach. In particular, an algorithm has been de-veloped, based on IFS codes, for texture analysisand reconstruction of compressed images.

An alternative method that we developed to com-press building stone images is ARIFS. This is acombination of linear prediction coding, based onauto regressive (AR) model and iterated functionsystems (IFS) in cascade to encode true colour 24-bitimages [14]. The IFS theory provides a convenientway to describe and code LP residuals since it isdesigned to exploit the self-similarities whichabound in such residuals, instead of adaptive quan-tizers and/or decimators. An IFS usually needs ahuge amount of computing time when applied on animage. But this not the case if it is used withinARIFS: indeed, much less work is required on theresiduals yielded by LP. In other words, the LP filtertraps a large amount of visual information and agreater tolerance is allowed by IFS on the residual.

Our approach is attractive due to low computa-tional complexity, simple parallel implementation,adaptivity to different image components and theability to produce high compression, performinggood results in terms of both subjective and objec-tive parameters. Moreover, these results demon-strate that this technique can be considered apowerful tool, which would be useful when con-structing a database of images from buildings ofhistorical importance which have quite a flat fre-quency distribution.

4. Image characterisation and recognition

Two types of image characteristics should, gener-ally speaking, be analysed: morphometric anddensitometric.

The morphometric information includes geomet-ric, topological and metrical properties of the image[9]. An image is described by a set of primitiveentities (points, lines, curves, regions) and theirrelationships.

A description based on densitometric characteris-tics is based on the spatial layout of the pixelcolours, that is its image texture [24, 25].

The fusion of texture analysis and synthesis is oneof the most widely used tools for characterising

image regions. In fact, the first task in texture analy-sis is to extract features which most completelyembody information about the spatial distributionof grey level variations in real images [24]. Tradi-tionally, texture analysis can be carried out follow-ing either numerical or syntactical approaches [25,26]. Among these numerical approaches are fractalgeometry, statistic analysis, co-occurrence matrixand numerical filters. Following syntactic ap-proaches, a region is investigated by means of tex-tural primitives generated by ‘tree grammar’.

In synthetic images, texture is an important sur-face attribute which provides information about thenature of a scene, acting as a fundamental descriptorof pictorial regions [27] and, for instance, as acharacteristic of the surface material usable for in-troducing perturbations of geometric and spectralproperties of the surface itself. In Greenberg andCarey [28] a taxonomy is proposed based on thecomputation of geometric and spectral properties.

The recognition problem can be defined as fol-lows: ‘‘to classify the elements of a given set, for agiven set of classes’’ [29]. There are many ap-proaches to the ‘machine pattern recognition’.

The first one is the ‘template matching’ approach.By means of a set of templates, each one representa-tive of one class, an evaluation function F is appliedto the examined pattern. Its class will be the classwhere F has the maximum value.

A more complex approach is the statistic ap-proach. In this case, a pattern is characterised by aset of N features and in the corresponding N-dimen-sional space the planes separating the classes shouldbe drawn. These planes can be estimated by a subsetof the available patterns to which the class belongs.

Another statistic approach is the parametric one.In this case, the main idea is to maximise theprobability that the considered pattern belongs to agiven class. The class selected will be the one thathas maximum probability. By this approach, a set offeatures has to be considered. Moreover, the proba-bility is computed taking into account that a ‘train-ing set’ is required, that is a subset of patterns towhich the class is known to belong.

Finally, the structural approach considers the fea-tures of subparts of the considered pattern. In otherwords, a pattern can be considered as a compositionof more simple patterns. The starting pattern is, inthis way, represented by these primitives features.

In conclusion, the template matching has shown asimpler implementation and it can be used whenthere are few differences among the patterns belong-ing to a given class. Otherwise, the statistic andstructural approaches have best performances.


4.1. Our implementations

The following subsections include a short descrip-tion of the techniques we implemented for charac-terising regions, with respect to the componentmaterials typology (section 4.1.1) and different

degradation shapes (section 4.1.2). In Moltedo andSalvetti [11], the problem of how to deal withtexture in integrated environments for analysis andsynthesis is examined and a unified model is pro-posed. Here, among the various techniques whichcan be used to both extract and produce textures,we choose those suitable for a unified approach. Inother words, we selected analysis procedures whichuse parameters that can be easily manipulated in thesynthesis phase. This is, thus, the guideline of ourresearch.

Section 4.2 presents the procedures we are usingin order to assist experts to recognise the class towhich a degradation shape belongs.

4.1.1. Characterisation of the component materialtypology

In order to characterise the component materialtypology and, in particular, materials with differentgrains, statistic and geometric approaches have beenmerged [11, 16]. The former approach allows theregions to be characterised in terms of their statisticproperties, such as standard deviation or entropy.For instance, the examination of several imagesdealing with pudding-stones of different grains(from coarse to fine grain) gives the results shown infigures 1 and 2.

Figure 1 shows the behaviour of the standarddeviation function: for coarse grain it is higher thanfor fine grain. In fact, the fine grain image is more‘uniform’ irrespective of the position of the averageof the intensity levels.

Figure 2 shows the behaviour of the entropyfunction: note the decreasing values from coarse tofine grain. In fact, the more ‘irregular’ the image(higher entropy), the higher the quantity of theinformation included. A coarse grain material sur-face is certainly more irregular than a fine grain one.

The geometric approach deals with the computa-tion of the normal vector field [11]. The grey level ofan image pixel is related to the geometry of theobject that the image itself represents. This assump-tion derives from a law – which is valid in the caseof light sources that can be characterised by aninfinity of parallel rays that have an equal sense anddirection – that states that the intensity of eachpixel is directly proportional to the light source andthe geometric normal vectors. The geometric normalis thus of fundamental importance for the imagecharacterisation and reconstruction. Figure 3 showsthree different grain pudding-stones, at the top, andthe visualisation of their normal fields, at thebottom.

Figure 1. Behaviour of the standard deviation function(SD) depending on the grain (G).

Figure 2. Behaviour of the entropy function (E) dependingon the grain (G).

Figure 3. Normal fields visualisation for different grainpudding-stone images: fine grain (left column), mediumgrain (middle column) and coarse grain (right column).


Figure 4. Image segmentation: original image (bottom); segmented regions (upper left) and their edges (upper right).

All the above mentioned procedures included inC.H.A.A.T. start from an image analysis techniqueand produce synthetic images: in the first case, theyare graphs while in the second one they are visuali-sations of vectors on a two-dimensional space.

4.1.2. Characterisation of degradation shapesOne of the main problems in the analysis of

images showing details of material surfaces is theextraction of basic features of degradation shapes.

These features are particularly relevant with re-spect to both recognition and synthesis; for instance,the simulation of a further degradation process.Nevertheless, the study of the degradation could becomplicated because of the presence of irregularstructures in the material itself.

In order to extract significant characteristics of animage relative to degradation shapes, co-occurrencematrix and wavelet transform (WT) approaches canbe used.

The first technique obtains image segmentation,that is, regions with particular densitometric fea-tures are identified and separated. In order to obtaindensitometric data, C.H.A.A.T. also includes theco-occurrence matrix approach [16, 25, 30]. Tex-turally homogeneous regions can be identified andextracted. An example of image segmentation isshown in figure 4: a detail of a pudding-stone ashlaris processed to characterise degradation zones con-sidered as dark regions corresponding to a lack ofmaterial. This figure includes three images. The bot-tom image represents the original image relative tothe pudding-stone ashlar. The upper left one showsa result of the above mentioned procedure: note thethree different colours that distinguish the threedifferent regions. This image is, in turn, the inputimage for a gradient analysis procedure which givesthe upper right image. The latter shows the contoursof the identified regions by giving a morphologicalcharacterisation of the image.


The point of view of the second technique, basedon the wavelets, is the edge detection at differentscale levels. This technique can be used when theavailable images are difficult to treat, i.e. whenedges cannot be detected because of particular fea-tures of the material (such as texture, etc.). In fact,WT is considered a powerful tool for space-fre-

quency multi-resolution characterisation [31]. Agiven image can be decomposed at several scalelevels and the procedure allows different shapes ofdegradation to be coarsely characterised. Moreover,this representation means that a shape of degrada-tion can be separated from other image features, forinstance noise [32]. This procedure can be appliedwhen the ‘useful signal’ and other features are lo-calised in different areas of the frequency domain.When this condition is not satisfied, the given imageshould be ‘pre-cleaned’, using well-known enhance-ment techniques.

In figure 5 the shapes relative to degradationzones can be extracted by means of the wavelettransform (left column part c) from the originalimages (a and b).

Figures 4 and 5 highlight that with both the abovementioned procedures the values of the extractedcontour points are visualised. In fact, the detectedshape contours are enhanced and, in the first case,the inside areas are filled as well, in order to betterdiscriminate degradation regions by means of differ-ent colours.

4.2. Recognition of shapes

The classical problem of pattern recognition canbe represented in our case by the recognition ofshapes which characterise subparts of the image, forinstance degradation zones. In all our procedures,the expert plays a fundamental role in suggesting thecriteria and rules concerning measures to identifythe class to which a region belongs.

Here we describe the first approach, used withinC.H.A.A.T., to recognise to which class a regionbelongs [33]. Starting from a visualisation such asthe one included in figure 4, we allow the user togive information about significant measures. Forinstance, the user is requested to give the minimumarea a region must have in order to be significant. Infact, figure 6 shows green colour filled regions thatare positive to this test. In such a way, cavitiesderived from a degradation process can be distin-guished from cavities due to structural properties ofthe material.

5. A new unified approach to imagecompression, characterisation andrecognition

A new interesting approach based on the combi-nation of the chain code and a context-free gram-mar [34, 35] allows one to achieve good results in

Figure 5. Extraction and coding of degradation zonesrelative to cavities and fissures.

Figure 6. Lack of material analysis: original image (upperleft); segmented image (upper right); region labelling (bot-tom left); significant region selection (bottom right).


terms of compression, characterisation andrecognition.

Starting from an image showing a material subjectto a given kind of degradation, interesting featurescan be extracted by means of the wavelet transform– WT. Now, if we consider degradation kinds char-acterised by a material loss, we only have to dealwith a shape boundary.

To do that, the boundary of the shapes relative todegradation zones is encoded by means of a well-known technique, i.e. the chain code. On the basisof the strings obtained, we can generate a grammarwith which to describe a given kind of materialdegradation.

Note that, with the help of cultural heritage ex-perts, only a subset of all extracted shapes is singledout and then ‘chain coded’. This one (our basis) willcontain the most representative shapes achieving theimage characterisation. In summary, we are able notonly to generate the shapes contained in our basis,but some new ones obtained from them, using some‘additional rules’. The additional rules are simplylocal changes in the shapes of the basis. Thus, allshapes belonging to a given material degradationkind can be obtained from the basis, through smallchanges. Moreover, this approach can also be con-sidered as a compression technique. In fact, thebasic shapes intrinsically contain a lot of a givendegradation feature. In other words, the grammarrepresents both a formal classification and an ‘infor-mation box’. That is to say that we can consider itas a compression of information that can be ‘ex-ploded’ when a features extraction is required.

Using the syntactic approach, i.e. starting from agrammar representing a given kind of materialdegradation (see for example figure 5), we canachieve the recognition of a given shape to a givenmaterial degradation. If we consider, in fact, thegrammar language (the represented shapes) as the‘knowledge base’, we can introduce a set ofmeasures.

Applying the latter both on the language and onthe considered shape, we can tell whether an exam-ined shape might belong to the material degradationconsidered.

In all the approaches mentioned, the role of theexpert is fundamental in suggesting criteria for themeasures.

6. Conclusions

This paper deals with approaches to computer aidfor experts and operators in the field of analysis of

the state of conservation of building of historicalimportance. We have focused on the problems ofcompression, characterisation and recognition ofparts of images directly derived from the stones thatbelong to such buildings.

Some of these functionalities are included in theC.H.A.A.T. system, which was developed to facili-tate users in accessing data archives, in extractingparameters from images for performing interactivelyvarious evaluation processes and in applying effi-cient methods for studying properties or simulatingpictorial events.

Future research will be addressed to the develop-ment of other techniques for such computer aideddiagnosis and to the improvement of user friendlyenvironments to use these techniques, such asC.H.A.A.T.

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