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Journal of Archaeological Science 55 (2015) 197e208

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Journal of Archaeological Science

journal homepage: http : / /www.elsevier .com/locate/ jas

An assessment of methods for the digital enhancement of rockpaintings: the rock art from the precordillera of Arica (Chile) as a casestudy

Enrique Cerrillo-Cuenca a, *, Marcela Sepúlveda b

a Spanish Council for Scientific Research (CSIC), Institute of Archaeology e M�erida, Plaza de Espa~na, 15, 06800 M�erida Badajoz, Spainb University of Tarapac�a, Departament of Anthropology, 18 de sept. 2222, Casilla 6D, Arica, Chile

a r t i c l e i n f o

Article history:Received 19 November 2014Received in revised form11 January 2015Accepted 12 January 2015Available online 30 January 2015

Keywords:Rock art paintingsDigital methodsStatisticsPrincipal components analysisDecorrelation of images

* Corresponding author.E-mail addresses: enrique.cerrillo@csic.es (E. Cerril

cl (M. Sepúlveda).

http://dx.doi.org/10.1016/j.jas.2015.01.0060305-4403/© 2015 Elsevier Ltd. All rights reserved.

a b s t r a c t

The digital tracing of rock art is becoming a standard for archaeologists working in this field of research.The lack of specific software for this task has resulted in archaeologists either using solutions that are notstatistically robust enough or working with overly generic fields of image analysis. This paper will assessthe application of three techniques for digital tracing: Principal Components Analysis, K-means, andDecorrelation Stretch. In addition to these techniques of image analysis, this paper will also explore threeselective techniques that classify or enhance pigmentation. These analyses have been implemented in asoftware package called PyDRA (developed by one of the authors, ECC). This software makes use ofseveral scientific libraries for the digital analysis of an image.

As a case study, we chose three rock art sites located between 3100 and 3500 m above sea level in theprecordillera of Arica, the northern region of Chile. All of the paintings are located inside rock sheltersthat are easily accessible; however, we lack a systematic recording for analysing these sites. Pampa ElMuerto 14 and Mullipungo 1 were recorded through direct tracings between 1980 and 1990. The Lupica 1site was identified only in 2013 and has not been recorded until now. Due to the advancement oftechnology in the years since the 1980s, we have been able to compare the proficiency of different digitaland statistical techniques. Our study uses the most adequate parameters and enables us to trace thepaintings digitally without actually handling the surface of the rock.

© 2015 Elsevier Ltd. All rights reserved.

1. Introduction

In recent years, the number of papers on the digital analysis ofrock art has increased (Mark and Billo, 2006; Brady and Gunn,2012; Cai, 2011; Clogg et al., 2000; Rogerio-Candelera et al., 2011;Mudge et al., 2012; Hollmann and Crause, 2011). The commonground between these papers is that computational photographyenables archaeologists to avoid direct contact with the paintingsand it enhances motifs and traces that could not otherwise bevisible to the naked eye. Another reason for adopting digitalmethods is the increasing precision of photogrammetric techniques(Plets et al., 2012; Domingo et al., 2013; Cerrillo et al., 2014).

lo-Cuenca), msepulveda@uta.

The aim of this paper is to assess the following statistical tech-niques: principal components analysis (PCA) (Vicent-García al.1996; Rogerio et al., 2011) and decorrelation stretch (DS)(Harman, 2008; Le Quellec et al., 2013). These techniques originatefrom the field of remote sensing, where they have developedremarkably, especially since the 1980s, when some of the decor-relation algorithms described in this paper were designed.With theuse of these techniques, a new algorithm has been developed, theselective intensification of saturation from one component (SISC). Twovariations of this algorithm (SIVC and SISVC) seek to selectivelyincrease the saturation of pigments for a more detailed analysis ofrock paintings. Moreover, we propose the use of K-means (Rogerio,2013) as a classification technique. Some of these procedures havealso been considered as belonging to “computer vision” due to theirability to obtain and classify information from digital imagery. Ouraim is to assess the effectiveness of these methods under differentcircumstances with unequal varying number of depictions, super-impositions, colours, and surfaces. The software application, PyDRA

E. Cerrillo-Cuenca, M. Sepúlveda / Journal of Archaeological Science 55 (2015) 197e208198

(Cerrillo-Cuenca et al., 2014), has been designed for applying thedifferent approaches described in this paper. After processing theimage through this software, the result is a digital tracing, wherethe paintings are isolated from the rock.

The paintings from the precordillera of Arica (Sepúlveda, 2011;Sepúlveda et al., 2013) are a very suitable case study for differentreasons: (1) The chromatic variability of the depicted motifs (withcolours such as red, orange, yellow, black, and white); (2) Thereiterative use of the panels, showing different regional styles andascribed to the different periods of the pre-Hispanic regionalsequence -between the Archaic period (8500 cal BP), and the Incaoccupation (approximately 1535 cal AD); and (3) the recorded sitesoffer an important contribution to rock art studies in the Arica re-gion, where nearly 90 sites with painted and engraved depictionshave been identified. The application of the methodology describedhere is allowing exhaustive analysis and a comparative approach ata regional level.

2. Methods: the tracing of rock art paintings

The appliedmethodology consists of four stages: 1) ortho-imagecreation, 2) the enhancement of pigments by statistically process-ing the image, 3) the non-supervised classification of resultantimages by specific algorithms, and 4) the assignment of classes tonominal categories and the creation of tracings (binary images).

2.1. Ortho-image creation

All of the images were taken with a Nikon D90 DSLR camerawith a sensor size of 23.6 � 15.8 mm, offering a maximum reso-lution of 4288 � 2848 pixels. The images were taken using theaperture priority: setting the f-number to a value higher than five toincrease the depth of field and to avoid blurred areas in the volumeof the surface. The images were stored in the native RAW format,which ensures the creation of files with a greater depth of colour(16 bits), although the camera is able to record the files only in12 bits. The advantages of this format in archaeological practicehave been summarized elsewhere (Wheatley, 2011; Verhoeven,2010), but it is important to recognize the possibility of correctingthe exposure and recording the paintings in a wider chromaticrange– essential practices for recording rock art. RAW images wereconverted to a TIFF file format through dcraw software [http://www.cybercom.net/~dcoffin/dcraw/], which preserves the orig-inal depth of colour, and which can control such features as thedemosaicing of Bayer matrix and white-balancing.

A necessary step for producing digital tracings is the ortho-rectification of original imagery through photogrammetric resti-tution, which has been developed in Photoscan Pro software.Through this procedure, the radial distortion produced by the lensgets corrected, but the conversion of the images to a metric scale isalso achieved (Linder, 2009). A detailed discussion about thismethod and its precision has been published in Archaeology (LoBrutto and Meli, 2012; De Reu et al., 2013).

When recording rock art, two types of approaches can beconsidered: 1) the ortho-rectification of digitally processed images(often tracings) over a 3D model (Rogerio-Candelera, 2010;Domingo et al., 2013; Cerrillo-Cuenca et al., 2014) and 2) the pro-cessing of a single ortho-image obtained from several digital pho-tographs. Domingo et al. (2013) have presented a well-arguedcontribution on this subject. The latter option was used here forproducing direct tracings and explaining the algorithms, althoughwe are still considering and exploring other methods for inte-grating the geometric information from 3D models and the radio-metric information from photographs. It is important to point outthat all the processes described in this paper can be performed on

non-rectified digital photographs as well as on ortho-photographs.In our study, the resolution was 0.25 cm per pixel: a resolutiondetailed enough for recognising the smallest painted motifs. Thecolour-depth of the resultant ortho-images was kept to 16-bit. Wehave achieved the optimal enhancement of images by masking theareas that contain information unrelated to the panels (scale bars,targets, background landscape, etc.).

2.2. Statistical enhancement of the image

PyDRA software was used for processing the ortho-images fromthe previous stage. The current version of this software allows theapplication of several methods that only allow the user to performPCA and false colour combinations (Cerrillo-Cuenca et al., 2014).The software was implemented using scientific open-source li-braries with reasonably well-detailed documentation about theformulae and algorithms being applied. The libraries are also effi-cient in terms of computational resources. The scientific librariesbeing used are as follows: Numpy (Van der Walt et al., 2011) andScipy (Jones et al., 2001) for analysing matrices; Scikit-learn(Pedregosa et al., 2011) for classification; and OpenCV (Bradski,2000) for advanced image processing. The programming of suchan environment allows us to 1) standardise the processes applied toimages, 2) manage matrices containing 16-bit structured data, and3) compare the results from different methods in real time.

The images are imported into PyDRA through the trans-formation of an image file (an ortho-image in this case) into avector composed of three numerical matrices, one for each band ofthe image, and by using the same number of pixels as the originalimage.

2.2.1. Principal components analysis (PCA)As has been stated previously (Abdi and Williams, 2010), PCA is

efficient in the treatment of multiband images (RGB colour imagesand multispectral images). The application of PCA to the digitalanalysis of an image is not recent (Solem, 2011), especially in thedomain of remote sensing (Eklundh and Singh, 1993). Rogerio-Candelera et al. (2011) have popularised the application of thismethod in rock art for similar purposes. The basis of PCA in imageanalysis (Jolliffe, 2002) is based on 1) the calculation of thecovariance matrix between the three matrices (RGB) that composethe image vector, 2) the obtaining of eigenvalues and eigenvectors,and 3) the rotation of the original image vector to a new space bymultiplying it by the eigenvectors (Equation (1)). Through PCA, thecorrelation matrix of the bands of the original image can bedetermined, as well as the percentage of variance that is retained inthe newly created matrix. The formula is as follows:

ym ¼ Rtxm (1)

xm is the image vector, constituted by threematrices andm pixels. Ris the rotation matrix, composed by the eigenvectors. The result ofEquation (1) is the vector ym composed of three principal compo-nents, or matrices, (PC1, PC2, PC3) that synthesise the informationfrom the original matrix into a new group of non-correlated vari-ables without losing information (Lasaporana and Masini, 2012).

In fact, this is a linear transformation of the original vector. In itsnew projection, PC2 and, more frequently, PC3 are used to gathermore information about the pigments that are uncorrelated orhidden in the rock surface (Rogerio-Candelera et al., 2011). AsRogerio-Candelera et al. (2011) have noted, the combination of PC2and PC3 can be useful for forming a new and meaningful RGB im-age. In some cases, when there is only a PC with relevant infor-mation about paintings (usually PC3), a grey scale image can be

Fig. 1. A screenshot from PyDRA during the assignation of classes to nominal categories.

Fig. 2. Location map of the sites described in the text.

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Table 1Correlation matrix between the RGB bands from the panel 1 from Pampa El Muerto14.

R G B

R 1 0.954 0.8348G 0.954 1 0.9511B 0.8348 0.9511 1

Table 2Eigenvalues and explained variance from PCA analysis, panel 1 from Pampa ElMuerto 14.

Component Eigenvalue Explained variance (%)

PC1 65,005,518.946 94.154PC2 3,879,532.972 5.619PC3 156,821.887 0.227

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obtained after contrasting the information (Cerrillo-Cuenca et al.,2014).

2.2.2. Decorrelation stretch (DS)The DS technique was first published by Gillespie et al. (1986) as

a method for decorrelating the bands from multiband images witha high degree of homogeneity. DS maximizes the differences be-tween the bands, contrasting the information to a maximum level.Harman (2008) has made use of this technique in his DStretchsoftware, which is very popular among the archaeologists workingon rock art because of the software's efficiency in rock art docu-mentation (Le Quellec et al., 2013). The method implemented byHarman (2008) includes the rotation of the image in a colour spacedifferent than that of the standard RGB. One of the drawbacks ofthis software is the lack of information about the algorithm beingapplied because there are substantial variations in remote sensingliterature about the DS technique (Gillespie et al., 1986; Gillespie,1992; Dutra et al., 1988; Campbell, 1996; Alley, 1996), and mis-understandings about its application have been documented(Campbell, 1996).

The decorrelation among the RGB bands is achieved throughPCA, although the eigenvalues and eigenvectors are obtained from

Fig. 3. Pampa El Muerto 14, images generated from the processes described in this paper. aFinal tracing obtained after the classification (40 iterations, 80 classes) and assignation of cunedited output image.

the correlation matrix (Gillespie, 1992) and not from covariancematrix. This procedure is based on Campbell (1996), who offers aparticularly well-reasoned description of the method. The algo-rithm consists of the following steps:

) Ortho-image from Panel 1, b) Application of SIVC algorithm (x ¼ 1) to the image A, c)lasses to categories (2). c1 is displayed after cleaning the noise, and c2 represents the

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1) Calculation of ym (Equation (1)), eigenvectors can be obtainedfrom the correlation matrix and, alternatively, from the covari-ance matrix

2) Generation of a stretching vector: a diagonal matrix composedby the inverse of the eigenvectors (Campbell, 1996), as follows(Dutra et al., 1988):

S ¼

2666666664

1ffiffiffiffiffiv1

p 0 0

01ffiffiffiffiffiv2

p 0

0 01ffiffiffiffiffiv3

p

3777777775

(2)

wm ¼ Sym (3)

where S is a diagonal matrix, and vn represents each of the eigen-values (Equation (2)). Optionally, S can be multiplied by an integervalue that serves to achieve a higher contrast in the image (Alley,1996). Finally, the resultant matrix is applied to ym (Equation (3)).During this step, the matrix is re-centred, stretching its values to amaximum.

Fig. 4. Details of the panel after the application of different techniques for enhancing the pigb) PC3, c) DS, d) SISC (x ¼ 1), e) SIVC (x ¼ 1), f) SISVC (x ¼ 1).

3) Application of the rotation matrix, which allows us to return theimage to its original space:

zm ¼ Rwm (4)

z is a new vector composed of three matrices (RGB) where the

m

information is decorrelated. The whole procedure (equations(1)e(4)) can be summarised in a single formula:

zm ¼ RSRtxm (5)

4) Finally, the matrices can be treated through different pro-cedures. Dutra et al. (1988) suggest adding an offset to thewholevector zm, whereas Alley (1996) applies a standard deviationvalue to visually increase the contrast. Although both optionsare available in PyDRA, these approaches can also be substitutedby a simple linear stretching of each PC to an eight-bit scale.

2.2.3. Selective intensification: SISC, SIVC, and SISVCThese new methods are designed to heighten the pigments by

selectively increasing the saturation or the intensity of paintedareas in an RGB image. The procedure also accounts for the resultsof PCA being computed over the covariance matrix (Equation (1)).This method has been named “selective intensification of

ments at Pampa El Muerto 14, details of superimpositions. a) Unprocessed ortho-image,

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saturation from a single component” (SISC). The SISC is composedof the following steps:

1) Conversion of the xm vector to an HSV conical colour space. HSV(Agoston, 2005; Smith, 1978), gives new chromatic space whereeach value of the vector represents the hue (H [0,360]), thesaturation (S [0,1]), and an intensity value (V [0,1]). Such aprocess does not involve either a loss of resolution or a loss ofthe original colour proprieties of an image.

2) Rescaling the desired component from PCA to the same scaleas the saturation band, the interval [0,1]. This can beeasily achieved by setting the lower value to 0, which isachieved by subtracting the minimal value from the wholematrix, and dividing it by the maximum value. This step alsoimplies a linear stretching of the component. The equationused is:

pm ¼ cm �minðcmÞmaxðcmÞ (6)

where cm represents a matrix resultant from PCA, generally PC3.

Fig. 5. The results of setting different values for x in SIVC.

3) Fusion of pm with the matrix corresponding to saturation. Thefollowing algorithm is proposed:

kðxÞ1¼< x

¼ 0:01*10x (7)

ym ¼ pmkðxÞ

!þ Sm (8)

where k(x) is an exponential function (Equation (7)) that will beused for weighting a component of PCA in the resulting image. x canbe a positive number greater than or equal to 1 which must be setby visualizing the results. A lower value (close to 1) can excessivelysaturate the image, whereas a high number will give a greaterweight to the original image and will avoid the enhancement ofpigments. A value between 1 and 5 often renders adequate results.Sm (Equation (8)) is a matrix resulting from the HSV transformationthat holds the saturation values for m pixels. The result is a matrixzm that is rescaled again in the interval [0,1] (Equation (6) can beapplied) for replacing the saturation matrix in the HSV colourspace.

The detailed area corresponds to the same from Fig. 4.

Fig. 6. Lupica 1, images generated from the processes described in this paper. a) Ortho-image, b) Application of DS algorithm, c) Final tracing obtained after the classification(40 iterations, 80 classes) and assignation of classes to categories (3). c1 is displayedafter cleaning the noise, and c2 represents the unedited output image.

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4) Finally, the HSV vector is converted back to a RGB colour spacefor a conventional representation.

In Equation (8), Sm can be substituted by the intensity value (Vm),which will yield a “Selective Intensification of Value from a singleComponent” (SIVC), or by applying the transformation in bothmatrices at the same time (SISVC).

2.3. Image classification

The application of K-means in rock art tracing was suggested byRogerio-Candelera (2013) as a way of obtaining binary images(tracings) from photographs. K-means is a statistical method thatpartitions a set of n observations (cells or pixels in our case) from ad dimensional space into k classes, so that the total distance be-tween the cells and the corresponding centroid of a class is reduced.The algorithm finds the centroid of each set in the partition throughan iterative process, and then re-partitions the input data accordingto the closest centroid. We utilized a closely related algorithm to K-means, the Lloyd (1982) algorithm, for calculating the position ofcentroids in the space through Voronoi diagrams. This algorithm(Pedregosa et al., 2011) accepts a minimum of two different seriesof data (matrices) as input. After its calculation, the algorithmproduces a single dataset where each value belongs to a classnumber.

In our study, we used the three channels from the output imageand classified them into 80 classes after a process of 40 iterations.The tracing can be obtained after assigning the k classes to a minornumber of previously established categories, each of these cate-gories containing one or more classes. These categories, which cancorrespond to tones of pigments as well as differences in the rocksurface, were named “background”, “non-classifiable e dispens-able”, “red pigments”, etc. This process is developed in the PyDRAgraphical interface, where the software highlights a class in theimage and the user can assign it to a category. Fig. 1 shows ascreenshot of PyDRA during this process.

Despite its usefulness in the classification of the original RGBimages, the best results can be obtained if the image has beenprocessed by any of the aforementioned algorithms. For example,the application of PCA and K-means in the data mining reduces theinitial dimensionality of data and achieves better classification(Ding and He, 2004). Through this sequential procedure, an optimalseparation among classes can be rendered, avoiding ambiguitiesduring the classification of certain groups of pixels.

3. Case studies

To illustrate this paper, three sites were chosen, one for eachsector established in the framework of the on-going project FON-DECYT 1130808 in upper Azapa Valley, in Ticnamar River drainage:Pampa El Muerto-Copaquilla, Tojotojone-Bel�en, and Mullipungo-Ticnamar. The aim of this project is to evaluate the patterns ofsettlement and mobility for the archaic hunteregathers from thehighlands of the (3000e3800 m above sea level). In the Andeanfoothills (Fig. 2) the pre-mountain chain presents a hilly reliefcarved by deep defiles interchanged with wide Andean massifs.Nearly 90 sites are located at the bottom and at the skirts of thedefiles, as well as at the monadnocks. Paintings and engravings arepresent, the first of which is the most recurrent in the highlands.Paintings in different colours and hues depict scenes of hunting,and livestock (Sepúlveda, 2011; Dudognon and Sepúlveda, 2013).Other scenes put the stress in schematic, anthropomorphic figuresportrayed in a frontal position and aligned one beside another. Ingeneral terms, geometric shapes are scarce (Sepúlveda et al., 2013).To date, two stylistic groups have been identified: a naturalistic

tradition attributed to archaic hunteregathers from the beginningsof the Formative period (8500e0 cal BP), and a schematic traditionprobably related to the late development of the region from1100 AD (Sepúlveda et al., 2013). In our study, the panels we haveanalysed mainly present figures and scenes from the naturalistictradition.

3.1. Pampa El Muerto 14

This site is part of a wide set of rock shelters located in thenearby area of Copaquilla at 3150 m above sea level (Santoro andDauelsberg, 1985; Mu~noz and Briones, 1996). It is located in aslightly raised position above a defile where 18 shelters with rockart were identified. Due to tectonic movement and subsidence, thedefiles from this sector are the only ones that flow in aWest-to-Eastdirection toward the confluence of the Seco and Tignamar rivers.

The site is located at the upper part of a narrowing defile, whichleads to a slightly restricted view of the shelter within the sur-rounding landscape. Pampa El Muerto 14 consists of two panels,one of which depicts only small and faded figure. In this shelter, thepaintings are adapted to an irregular surface formed by protrudingrocks and holes. Red paintings correspond to the naturalistic

Table 3Correlation matrix between the RGB bands from Lupica 1.

R G B

R 1 0.9982 0.9979G 0.9982 1 0.9921B 0.9979 0.9921 1

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tradition, showing camelids of different sizes, sometimes withslight superimpositions.

3.2. Lupica 1

This rock shelter is located between the Bel�en and Lupica mu-nicipalities at a height of 3100 m above sea level. It is located in theupper part of a strait defilewith steep skirts. The shelter is relativelysmall was used by local shepherds until few years ago. At least fourpanels were identified that occupy the whole inner surface of the

Table 4Eigenvalues and explained variance from PCA analysis, Lupica 1.

Component Eigenvalue Explained variance

PC1 2.904 96.811PC2 0.088 2.951PC3 0.007 0.238

Fig. 7. Details of an area with different pigments after the application of different techniquesSISVC (x ¼ 1).

shelter. The paintings were made in yellow and red tones. Yellowfigures are superimposed over red camelids of a similar style.

3.3. Mullipungo 1

This site is a large rock shelter from the Mullipungo sector,located half-way between Ticnamar and Timalchaca localities at3500 m above sea level. This site is located in a place formed bygreat outcroppings that have been eroded by water streams. Thearea combines archaeological occupations in shelters as well asopen-air sites. Almost 20 more sites are located in the surroundingareas, Mullipungo 1 being themost important of them. The sitewasrecorded in the 1980s by Schiappacasse and Niemeyer (1996), whomade some test pits inside the shelter. These authors recorded atleast six panels corresponding to a naturalistic tradition. Amongthem was an important set of original geometric motifs and a fewschematic quadrupeds. Red, black and white tones are used in thepaintings.

4. Results and discussion

4.1. Pampa El Muerto 14

In this case, we generated an ortho-image (Fig. 3a) whose valueswere stored in 16 bits. The correlationmatrix of the three bands can

at Lupica 1. a) Unprocessed ortho-image, b) PC3, c) DS, d) SISC (x ¼ 1), e) SIVC (x ¼ 1), f)

Fig. 8. Mullipungo 1, panel 2, images generated from the processes described in this paper. a) Ortoimage, b) Application of DS algorithm, c) Final tracing obtained after theclassification (40 iterations, 80 classes) and assignation of classes to categories (3). c1 is displayed after cleaning the noise, and c2 represents the unedited output image.

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be consulted on Table 1, where the strong correlation between thegreen band and the red and blue bands can be seen. The correlationdecreases between the green and blue bands. The general resultsshowaweak separation, in chromatic terms, between the pigmentsand the rocky background. In this case, all of the techniquesdescribed above were applied. The greatest separation betweenpigments and the rock was achieved in PC3. SIVC produced theclearest results (Fig. 4). To find the most suitable value, we havetested with different values for the x variable (Equation (6) andFig. 5) and finally set x to 1 to achieve the best level of contrast. Thesuperimposition of motifs can also be seen in the outputs of SISC,SISVC and DS, although not as contrasted as in SIVC. The resultingimage (Fig. 3b) has been classified by the Lloyd algorithm (Fig. 3c).The output classes from K-means were reclassified into two classes:vanished/light red and red pigments (inweb version). Fig. 3c showsthe unedited image (3c2) and the same image after manual editingon conventional image software for deleting the noise produced byan incorrect classification of pixels. The final results can beconsidered as extraordinarily precise because all of the finest de-tails (small sized anthropomorphs, footprints) have been correctlyrepresented in the tracing (Fig. 3c) (Table 2).

4.2. Lupica 1

At this site, DS showed themost precise results when separatingthe paintings from the background (Fig. 6). The correlation between

Table 5Correlation matrix between the RGB bands from the panel 2 from Mullipungo 1.

R G B

R 1 0.9975 0.9924G 0.9975 1 0.9986B 0.9924 0.9986 1

the bands of the image was even greater than in the previous case(Table 3), and all of the correlation values were close to 1, revealinga great similarity between all of the bands. By analysing PC2 andPC3, we can conclude that both components gather all of the in-formation about the paintings. The percentage of explained vari-ance from PC2 is 2.950 and PC3 0.238 (Table 4).

The application of SISC rendered good results (Fig. 7d), butdefinitively, DS returned the neater difference between the back-ground and the two tonalities of the pigments: red and yellow (inweb version) (Fig. 7c). The rest of the methods failed to contrastyellow pigments sufficiently; this can be explained by the fact thatDS uses the information from all of the PCs and not from a singlecomponent. The final tracing (Fig. 6c) retained three classes as anoutput: vanished red pigments, red pigments and yellow pigments(in web version). In the case of Lupica 1 the separation betweenclasses was so precise that little manual editing (Fig. 6)c1 wasneeded.

4.3. Mullipungo 1

This is the only site that was published before (Schiappacasseand Niemeyer, 1996), and it is the one where we can best eval-uate the benefits of using digital procedures against traditionaltracing techniques. We have selected part of panel 2 (Fig. 8) fromSchiappacasse and Niemeyer (1996). In the preliminary results,

Table 6Eigenvalues and explained variance from PCA analysis, panel 2 from Mullipungo 1.

Component Eigenvalue Explained variance

PC1 1433.642 97.226PC2 37.373 2.535PC3 3.529 0.239

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we can note the discovery of new figures that are unnoticeable tothe naked eye due to their poor degree of preservation. PCAshowed an extraordinarily high correlation between the threeRGB bands; in all cases they were higher than 0.99 (Table 5).Table 6 gathers the values from a variance that is similar to theprevious cases.

Fig. 9 shows the application of the algorithms in a poorly pre-served figure that represents a quadruped. In this case, SISChappened to be the most suitable technique (Fig. 9d). SISVC andSIVC excessively saturated the output image in all of the figures,whereas DS did not offer an adequate contrast. The variable x fromSISC was set to 1 to enhance the more faded paintings (Fig. 10),although, for the rest of the panel, a higher value is adequate todistinguish the paintings.

4.4. Discussion

One of the most relevant aspects of using PyDRA is the ease withwhich the tracing is produced. Although the methods describedabove can be reproduced using independent software, there aretwo primary advantages of designing new software: 1) the ability tosystematically apply algorithms to a set of images and 2) the real-time visualization of applying the algorithms to the image. Whenobtaining the enhanced images, we must consider the differentoutputs provided by the method regarding 1) preserving the

Fig. 9. Details of the panel after the application of different techniques for enhancing the portho-image, b) PC3, c) DS, d) SISC (x ¼ 1), e) SIVC (x ¼ 1), f) SISVC (x ¼ 1).

original resolution, 2) preserving the sharpness of the originalphotograph, and 3) evaluating whether to maintain or discard theoriginal tonal values.

Regarding the first point, it must be said that we have lost noneof the resolution when performing all of the processes describedabove. All of the outputs maintained their original resolution,which facilitates the direct comparison between the original imageand the enhanced one. For instance, this strategy can be used topreserve both images as independent layers in one multilayer im-age file (TIFF format).

All of the methods that make use of PCA for decorrelatingimages can produce a minimal loss of sharpness in second orthird components, especially with the appearance of squarepatterns in jpeg compressed files. The influence of this loss ofresolution can be minimized through techniques such as DS orSISC (and its variants) which algebraically combine the infor-mation from the components with the original radiometric in-formation. There are also specific procedures that can reduce thenoise from the components (Campbell, 1996), but these methodssacrifice the variance in the information from the PC, usually PC3(precisely the one that usually gathers the information from thepigments). Methods such as Direct DS (Liu and Moore, 1996)could perhaps be more efficient for solving this issue, but,conversely, they are less capable when contrasting the pigmentsin the output image.

igments at Mullipungo 1, details of an unpublished and fading figure. a) Unprocessed

E. Cerrillo-Cuenca, M. Sepúlveda / Journal of Archaeological Science 55 (2015) 197e208 207

The original radiometric values are always sacrificed followingthe multiplication of the original matrix by the eigenvalues(Equation (1)). One option for dealing with this issue could be tomaintain the tonal information in a SISC model where the tonalinformation remains unaltered and is enhanced by increasing thesaturation of selected areas. Thus, the original tones can be recov-ered at any time by preserving the HSV file. However, as we haveshown, this method is only accurate under certain conditions(Mullipungo 1), such as when there are a limited number of pig-ments or when tonalities are similar. In Mullipungo, SISC workedreasonably well; the other algorithms could be used, especiallySIVC and SISVC.

DS has proven to be very efficient in the case of Lupica 1, wheredifferent pigments were used in the composition. Finally, in thecase of Pampa El Muerto 14, with a limited number of tonalities,SIVC (Fig. 4e) yielded a much clearer contrast between the paint-ings and the rock.

K-means and related classification techniques, such as the Lloydalgorithm, are a versatile way to obtain clear tracings (Rogerio-Candelera, 2013). The calculation time has been significantlyreduced by employing the optimised scientific libraries, especiallyby Scikit-learn (Pedregosa et al., 2011). When applied to decorre-lated images, the outputs can be improved, as has been demon-strated. The high number of iterations (40) and the high number of

Fig. 10. The results of setting different values for x in SISC.

output classes (80) have ensured the correct assignation of classesto “nominal categories” and the creation of clear tracings. After thereclassification process, clear pictures were obtained for the threecase studies and these pictures can be easily cleaned in few mi-nutes. Moreover, through this procedure, we have obtained ho-mogeneous tracings for three different panels and analysed themwith different methods which becomes essential when attemptingto compare multiple sites. In the case of highly contrasted outputs athresholding algorithm can also render optimal tracings with thebenefit of a significant reduction of time.

5. Conclusions

This paper intends to be a contribution to the research of rockart by using digital techniques, all of which come fromopen-source,scientific applications. The use of photogrammetry for producingmetric products is a first and necessary step for producing scalabletracings. All of the algorithms here analysed can be applied onconventional images for identification purposes, but also for pres-ervation matters (for creating deterioration maps, for example).

After obtaining the outputs of algorithms discussed above, wecan conclude that there is no single efficient and definitive tech-nique to enhance paintings in photographic files. When applyingdifferent algorithms to a single rock art panel, the results can be

The detailed area corresponds to the same from Fig. 9.

E. Cerrillo-Cuenca, M. Sepúlveda / Journal of Archaeological Science 55 (2015) 197e208208

extraordinarily different from one part of the panel to another.Many variables (the weathering of the rock, the granularity of therock, the type of rock, local ambient light, the type of exposure, theprocessing of the RAW files) influence the resulting images.Because it is almost impossible to create homogeneous conditionsfor recording each panel, the comparison between several tech-niques at the same site seems to be a best approach to ensuring asuccessful record of digital imagery.

Although PCA does offer good outputs by itself, the best resultsare obtained when considering additional techniques, especiallyregarding the preservation of image sharpness. We have explainedthe DS algorithm, which is the one most commonly used by ar-chaeologists, and proposed new methods for enhancing images.The DS algorithmwas opaque to most of the users, and so, we haveworked here to describe the technique and its usefulness. Theresearch into new algorithms, such as the three presented in thispaper, is expanding the possibilities for applying computationalimagery to rock art, and it has implications for a broader use.

The procedures here described have facilitated the recordingand preservation of rock art sites in desert and mountainous areassuch as the Chilean precordillera. These procedures are offeringnew insights and aiding us in the stylistic analysis of rock art panels.The techniques described in this paper have allowed us to sys-tematically create a record of more than 20 sites.

Acknowledgements

The Institute of ArchaeologyeM�erida (CSIC-Gobex) funded partof this work. We want to acknowledge support of Sebasti�an Cel-estino. This work has been also partially funded by the projectFONDECYT 1130808 and supported by an agreement between theUniversity of Tarapac�a and the Chilean Ministry of Education.

I wish to sincerely thank Jos�e �Angel Martínez del Pozo for all ofthe support given during a very early stage of this work. Hisencouragement and advice is represented in many parts of thispaper (ECC).

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