Paper Lithic Texture

8/7/2019 Paper Lithic Texture

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Prehistoric Use-wear Analysis through Neural Networks.

By Juan A. Barcel & Jordi Pijoan-Lpez

UNIVERSITAT AUTNOMA DE [email protected]

[email protected]

Abstract. The surface of prehistoric lithic tools are not uniform but contain many variations; some ofthem are of visual or tactile nature. Such variations go beyond the peaks and valleys characterizingsurface micro-topography, which is the obvious frame of reference for textures in usual speaking.Beyond those physical, geological characteristics of the raw material, some visual features of an artifacts

surfaces are consequences of the modifications having experimented that object along its history.Consequently, when we analyze macro- or microscopically an objects surface, we should recognize somedifferential features which are the consequence of an action (human or bio-geological) having modifiedthe original appearance of that surface. In this paper we have described and measured use-wear evidenceas an archaeological texture in terms of the particular dispersion of luminance values across the surface.Textures have been analyzed as complex visual patterns composed of entities, or sub-patterns, that havecharacteristic brightness, color, slope, size, etc. Once the texture elements have been identified in theimage, we have computed statistical properties from the extracted texture elements and used these astexture features. A neural network has been programmed to induce the best discriminant function. Resultsshow the reliability of this approach.

Key words: lithics, use-wear, texture, neural networks, machine learning

Introduction

Archaeologists have been erroneously using clustering methods to achieveclassifications. It has been traditionally assumed that everything which is similar, wasproduced, distributed and used in the past in the same way. But we cannot learnexplanatory knowledge nor predict concept assignments on the basis only of perceivedsimilarities.

The goal in a classification problem is to develop an algorithm which will assign anyartistic, historical or prehistoric artifact, represented by a vector x, to one ofc classes(chronology, function, origin, etc). The problem is to find the best mapping from thedescriptive features (input patterns) to the explanation (output). The task of learning theexplanatory concepts consists in separating the input vectors, assigning some of them toa known output, from the input vectors which are assigned to the another one. We needtraining data, that is, known instances of explanatory concepts, to be capable ofpredicting the way each explanatory output is connected to each descriptive input.

This is a fast perfect example of inverse reasoning. That is, the answer is known, butnot the question. What we need in Culture Heritage research is guessing a past eventfrom its vestiges. In archaeology for instance, the main source for inverse problemslies in the fact that archaeologists generally do not know why archaeologicalobservables have the shape, size, texture, composition and spatiotemporal location they
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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have. Instead we have sparse and noisy observations or measurements of physicalproperties, and an incomplete knowledge of relational contexts and possible causalprocesses. From this information, an inverse engineering approach should be used toadequately interpret ancient remains preserved in the present as the materialconsequence of some social actions performed in the past.

An inverse problem can be solved by conjecturing unobservable mechanisms that linkthe input (observation) with the output (explanation). This is exactly what philosophersof science have called induction. It can be defined as the way of concluding that factssimilar to observed facts are true in cases not examined. The underlying assumptionsare:

A. When a thing of certain sort A has been found to be associated with athing of a certain other sortB, and has never been found dissociated froma thing of the sort B, the greater the number of cases in which A andBhave been associated, the greater is the probability that they will beassociated in a fresh case in which one of them is known to be present;

B. Under the same circumstances, a sufficient number of cases ofassociation will make the probability of a fresh association nearly acertainty, and will make it approach certainty without limit.

Computer scientists are intensively exploring this subject and there are manynew mechanisms and technologies for knowledge expansion through iterative andrecursive revision. Artificial Intelligence offers us powerful methods and techniques tobring about this new task. Fuzzy logic, rough sets, genetic algorithms, neural networks,Bayesian models and agent-based systems are among the directions we have to explore.These paradigms, differ from usual methods in that: a) they are (in comparison at least)robust in the presence of noise; b) they are flexible as to the statistical types that can be

combined; c) they can work with feature (attribute) spaces of very high dimensionality;d) they can be based on non-linear and non monotonic assumptions; e) they require lesstraining data, and f) they make fewer prior assumptions about data distributions andmodel parameters.

The huge number of learning algorithms and data mining tools make impossiblethat we can review the entire field in a single paper (see Barcel 2008 for a list ofalgorithms and their applications to culture heritage research).

Use-Wear Analysis as Archaeological texture.

When an archaeologist analyzes prehistoric objects, the first aspect she perceives are thevisual properties of the artefact. Its surfaces present variations in local properties likealbedo, colour, density, coarseness, roughness, regularity, linearity, directionality,direction, frequency, phase, hardness, brightness, bumpiness, specularity, reflectivityand transparency. Texture is the name we give to these variations, which seem to beusually caused as a result of the process that created that surface.

Texture has always been used to describe archaeological materials. May be themost obvious example of texture analysis in archaeology is that of distinguishingsurface irregularities due to the characteristics of the raw material. We can distinguish

between carved stone tools, stripped bones, polished wood, dry hide, painted pottery,etc. in terms of the visual appearance of the raw material they are made of.


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Furthermore, texture patterns are not only intrinsic to the raw material itself.Some visual features of an artifacts surfaces are consequences of the modificationshaving experimented that object along its history. Consequently, when we inspectmacro- or microscopically an objects surface, we try to recognize those differentialfeatures -striations, polished areas, scars, particles, undifferentiated background-, which

can be the consequence of working actions (use) having modified the originalappearance of that surface.

Archaeologists studying stone instruments usually wish to determine whether ornot these objects were used as tools and how. The best way to do this is through theanalysis of microscopic traces of wear (texture) appearing on the surface of the tool(Figure 1).

ORIGINALUNALTEREDSURFACE

ALTEREDSURFACEAFTER

HUMAN WORK

Figure. 1. Identifying texture on the basis of image features

Surface variations in prehistoric stone tools due to human work can be analyzed interms of different causal factors:

Worked Material: (wood, bone, shell, fur, etc.) the effects of its physicalproperties (hardness, wetness, porosity, plasticity, etc.) on the tool activitysurface (Figure 2)

Movement: longitudinal (cut), transversal (scrape), etc. (Figure 3)


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A B C

Fig. 2. Texture differences between replicated prehistoric stone tools used in different ways. A: original raw material texture

before using (andesite stone); B, Result of the alteration in surface A when the tool was used scrapping fur. C, A different raw

material (obsidian) with texture features produced through wood scrapping.

Edges axis Edges axis

LONGITUDINAL (cutting) TRANSVERSAL (scrapping)

Fig. 3. Longitudinal and Transversally generated original surfaces (Photographs by the authors research team).

The inverse problem of getting the way the tool was used in the past from the vestigesof its use-wear texture observed in the present has been traditionally answered in asubjective way. Archaeological specialists, with many years of experience replicatingprehistoric tools in laboratory generalize their personal intuitions and diagnoseprehistoric objects. Although some essays have been published towards a more formalapproach using statistics and traditional quantitative image analysis tools, the task is stillcarried out by few specialists based on their personal experience.

Let us consider how this archaeological problem can be solved using a computationalapproach based on Neural Networks.

An introduction to Neural Networks

Our brain is composed of biological neurons. We can replicate up to a certain point ahuman brain but building a system of artificial neurons. In analogy to the way chemicaltransmitters transport signals across neurons in the human brain, a mathematicalfunction (learning algorithm) controls the transport of numerical values through theconnections of the artificial neural network. If the strength of a signal arriving in a


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neuron exceeds a certain threshold value, the neuron will itself become active andfired, i.e., pass on the signal through its outgoing connection.

The power of neural computation comes from the massive interconnection among theartificial neurons, which share the load of the overall processing task, and from theadaptive nature of the parameters (weights) that interconnect the neurons. In this case,each neuron is connected to many others, and inputs correspond to the incoming signalsfrom other neurons into the synapses of a single biological neuron. Each neuronreceives connections from other neurons and/or itself.

For a computing system to be called Artificial neural network or connectionistsystem, it is necessary to have a labelled directed graph structure where nodes(representing artificial neurons) perform some simple computations. It is the pattern ofinterconnections which is represented mathematically as a weighted, directed graph inwhich the vertices or nodes represent basic computing elements (neurons), the links oredges represent the connections between elements, the weights represent the strengthsof these connections, and the directions establish the flow of information and more

specifically define inputs and outputs of nodes and of the network. Each nodesactivation is based on the activations of the nodes that have connections directed at it,and the weights on those connections. To complete specification of the network, weneed to declare how the nodes process information arriving at the incoming links anddisseminate the information on the outgoing links. The nodes of the network are eitherinput variables, computational elements, or output variables.

Getting the networks to do something is a matter of giving them inputs, and letting theinformation travel through the topology by simulating each artificial neuron. The role ofneural networks is to provide general parameterised non-linear mappings between a setof input variables and a set of output variables. The neural network builds discriminant

functions from its neurons or processing elements. The network topology determinesthe number and shape of the different classifiers. The shapes of the discriminantfunctions change with the topology, so the networks may be considered semiparametricclassifiers.

There are two modes in neural information processing:

using mode

training mode.

During training, the network is trained to associate outputs with input patterns. Once thenetwork has learnt, training stops, and weights are not changed further, unlesssomething new must be learned. Once trained, a networks response becomesinsensitive to minor variations in its input. Then, when the network is used, it identifiesthe input pattern and tries to output the associated output pattern. The power of neuralnetworks comes to life when a pattern that has no output associated with it, is given asan input. In this case, the network gives the output that corresponds to a taught inputpattern that is least different from the given pattern.

In the using mode, the presentation of an input sample should trigger the generation of aspecific output pattern. Each such input (or output) set is referred as a vector. Neural

networks work by feeding in some input variables, and producing some outputvariables. Data fed to the network represent the pattern of activation over the set of


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processing units. They can therefore be used where you have some known information,and would like to infer some unknown information. When a known input pattern isdetected at the input, its associated output becomes the current output. If the inputpattern does not belong in the taught list of input patterns, the firing rule is used todetermine whether to fire or not. This is also called, the Retrieving Phase. Various non-

linear systems have been proposed for retrieving desired or stored patterns. The finalneuron values represent the desired output to be retrieved.

In the training or learning mode, a network modifies selectively its parameters so thatapplication of a set of inputs produces the desired (or at least consistent) set of outputs.The network requires input data and a desired response to each input. Neural networkstake this input-output data apply a learning rule and extract information from the data.Essential to this learning process is the repeated presentation of the input-outputpatterns. Training is accomplished by sequentially applying input vectors, whileadjusting network weights according to a predetermined procedure. The more data arepresented to the network, the better its performance will be. As the network is trained,

the weights of the system are continually adjusted to incrementally reduce the differencebetween the output of the system and the desired response. If the weights change toofast, the conditions previously learned will be rapidly forgotten. If the weights changetoo slowly, it will take a long time to learn complicated input-output relations. The rateof learning is problem dependent and must be judiciously chosen.

During training the network weights gradually converge to values such that each inputvector produces the desired output vector. It is the network which self-adjusts toproduce consistent responses. By adapting its weights, the neural network workstowards an optimal solution based on a measurement of its performance. Usually, theoptimal weights are obtained by optimizing (minimizing or maximizing) certain"energy" functions. Relaxation is the process whereby the unit activations (not theweights) change over time until they evolve to a state in which activations are no longerchanging, and thus the network can be said to have relaxed, i.e., fallen into a state oflittle activity. Relaxation differs from learning in that only activations change; inlearning, the weights change.

For more technical details, and a complete list of references, the reader is addressed to arecent book on the subject (Barcel 2008).

The Analysis of Use-Wear Evidence. An identification-based approach

The easiest way of creating an associative memory for archaeological explanations is byassuming that there is a roughly fixed set or vocabulary of supposed descriptiveregularities shared by a single population of objects, which are also distinctive enough.In this case, input neurons are not proper visual units, because there is no sensoracquiring image data and sending them to the computer system. Instead, it is the humanuser, who feeds the network with an interpreted input, in which each feature containsthe result of a previous inference. In this way, the receptive field properties of low-levelneurons does not encode the salient features of the input image, but the previousknowledge the user has about the features characterizing the archaeological evidence.

This kind of neural network is mostly similar to an Expert System


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M.H. van den Dries (1998) has published a system of that kind. She has tried to classifystone tools according to function using this kind of texture descriptors. The system hasbeen trained to recognize polishes. The other wear categories (edge retouch, edgerounding and striations) have not been included, and the system has not been equippedto relate the wear patterns to motions. The main reason for this limitation is that the

reference data set yielded the largest number of training examples for this wearcategory.

The input neurons represent 31 wear attributes, and the output neurons represent theworked materials: dry hide, fresh hide, hard wood, soft wood, dry bone, soaked bone,dry antler, soaked antler, cereals, meat, pottery, stone, soil, siliceous plants, non-siliceous plants. The training set consisted of 160 examples of experimentally replicatedtools used in the archaeological laboratory to cut and scrap the materials mentioned onthe output neurons list. After training, the neural network correctly identified mostpresented cases (26.4% of misclassification rate with training data).

Because of the failure to learn a fourth part of the experimental examples, those special

cases were individually analyzed. It turned out that the network was more rigorous thannecessary, because many answers were not really wrong, but a matter of degree ofcertainty. Most of the differences resulted from the fact that the answers were not verypersuasive. Consequently, many of them had a score that just misfit the trainingtolerance. Still most answers corresponded with the expected answer and pointed at theright contact material. In fact, all mistakes included the right contact material in theanswer. Moreover, eight of the mistakes consisted of two outputs with equal scores onsimilar materials like cereals and siliceous plants.

Subsequently, WARP was tested with 16 randomly selected testing data. Consideringthe degree of answer overlapping, only four (25%) were really false. Van den Dries

compared the performance of the network on experimental data, and on archaeologicaldata (interpreted by a human analyst). Despite some unfortunate guesses, WARPperformed rather well.

Although M.H. van den Dries results are impressive, we should question the use ofqualitative presence/absence variables to describe texture. It is really difficult to knowwhether a texture pattern is greasy or very brilliant. If we want to go beyond thiskind of identification-based analysis, we should find a way to fed the network withtexture information directly, and not through a subjective identification.

The Analysis of Use-Wear Evidence. A visual-based approach

The PEDRA system is an example of how using macro- and microtextureanalysis for the texture classification of lithic tools according to use-wear (pedra meansstone in catalan language, Barcel and Pijoan 2004; Barcel et al. 2008, Barcel2008, Pijoan 2007). Instead of subjective types of use-wear, a computational systemwas designed based on image segmentation techniques which associated pixels with thesame grey level and define areas with comparable luminance variance. The underlyingidea was that extracted texels or texture elements corresponded to bumps or largeplateaux generated by the use of the tool cutting or scrapping different materials (meat,hide, wood, bone, shell). Different gray-level thresholds were explored to obtain

different image segmentations, but we selected finally 120 grey levels as the thresholdto separate a texel from the tools background.


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Original image Image Segmentation Identified Texels

The idea was to calculate a non-linear discrimination rule for texel parameters, that is,how to distinguish texels generated because of longitudinal movement (cutting), fromtexels generated during transversal movement (scrapping). Or, alternatively, todiscriminate between texels produced working hard materials (wood, shell) from soft

ones.We have replicated in laboratory more than 100 lithic tools using the same kind

of flint. Three microphotographs were taken for each tool from different areas of theworking surface. Texels were individually measured for their area, perimeter, axislength, etc.

Statistical analysis (Pijoan-Lpez 2007) has proved that there is no clear-cut rulethat relates the shape and geometry of the texture elements to the work activityperformed by the stone tool. It is important to remember that what we are describing astexture is just a light effect, that is to say, an indirect evidence of some irregularities onthe active surface of the tool. The shape parameters of regions with different luminance

properties correspond to the interfacial boundary defined by light reflection, andconsequently they do not fit necessarily with the real texture. Observed image texturedepends on factors such as illumination conditions. Certain properties of stone surfaceshave effects on the appearance of use wear. Because grey values depend on shadows,and shadows depend on the position of light sources, if we do not care, the same objectsurface may have very different texels associated. In our experiments, we havecontrolled light sources, and the influence of the image acquisition device to be able tounderstand observed patterns, but additional control is necessary to select the luminanceintervals selected for texel extraction.

A Neural network should allow us to discover whether there is enough evidenceto establish some degree of nonlinear relationships between light reflection variabilityand micro-topographic features on the active surface of the replicated stone tool. Afeed-forward neural network has been built (Figure 4). Input neurons read centraltendency measures (mean and standard deviation) of all texels segmented at eachmicrophotograph, obtaining a dataset of 496 microphotographs, described in terms of:

- Mean of Elongation/ Std. dev. of Elongation- Mean of Circularity/ Std. dev. of Circularity- Mean of Quadrature-Thinness/ Std. dev. of Quadrature-Thinness- Mean of Ratio Compactness-Thinness/ Std. dev. of Ratio Comp.-Thin.- Mean of Compactness/ Std. dev. of Compactness,- Mean of Irregularity/ Std. dev. of Irregularity

- Mean of Rectangularity/ Std. dev. of Rectangularity,- Mean of Ratio Perimeter/Elongation/Std. dev. of Rt. Per./Elong.


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- Mean of Feret diameter/ Std. dev. of Feret diameter- Mean of Minimum rectangularity/ Std. dev. of Minimum rectangularity- Mean of luminance means within a texel/ Std. dev. of lum. means- Mean of luminance std.dev. within a texel/ Std. dev. of lum. st. dev.- Mean of luminance modes within a texel/ Std. dev. of lum. modes

- Mean of luminance min.values within a texel/ Std. dev. of lum. min.va.- Mean of Area of all texels within the image / Std. dev. of Area

Figure 4. General Architecture of the Neural Network implementing the PEDRA Classifier

In a preliminary investigation, we analyzed the relationship between texturevariation at a single microphotograph and the experimented activity (cutting orscrapping bone, shell, meat, dry or fresh hide, dry or fresh wood). Therefore, the

network has seven output units and a hidden layer with 144 neurons. Learningalgorithm was backpropagation. Results are fairy interesting (Table 1). Whencomparing training data with network interpretation:

Output /Desired BONE BUTCHERY DRY HIDE DRY WOOD

FRESH

HIDE

FRESH

WOOD SHELL

BONE 55 0 0 6 1 3 10

BUTCHERY 8 43 4 2 5 1 0

DRY HIDE 13 4 46 6 0 6 3

DRY WOOD 13 1 12 43 1 13 4

FRESH HIDE 3 17 7 3 18 0 0

FRESH WOOD 10 1 5 6 0 28 8

SHELL 20 1 6 5 0 11 44


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Performance BONE BUTCHERY DRY HIDE DRY WOODFRESHHIDE

FRESHWOOD SHELL

MSE 0,1433 0,0626 0,0983 0,0962 0,0405 0,0852 0,0892

NMSE 0,7731 0,5364 0,7271 0,7844 0,8473 0,7795 0,7449

MAE 0,237 0,1455 0,1966 0,20543 0,1137 0,1910 0,1950Min Abs

Error 1,1E-05 8,2E-05 0,0006 0,0006 0,0002 2,7E-05 0,0002

Max Abs

Error 1,0388 0,9980 0,9961 0,9779 0,9352 1,0341 0,9818

r 0,512 0,7116 0,5397 0,477 0,4998 0,4709 0,5119

Percent

Correct 45,0819 64,1791 57,5 60,5633 72 45,1612 63,7681

Table 1. Results from Experiment 1: Discriminating among worked materials.(Original experimental

data set)

It is easy to see that the neural network correctly classifies most replicated tools

according to the worked material. Only bone and fresh wood get a percentage of rightclassifications less than 50%. However, even these errors are understandable. Bone can bemisclassified with shell, shell with bone, fresh wood with dry wood, butchery with fresh hide,Only worked materials with similar hardness can be confounded.

When comparing test data (15% of experimental replications not used in the trainingset) with network interpretation (Table 2):

Table 2. Testing the PEDRA system with additional data.

Obviously, testing results are worst than training data results. The reason of the badresults for fresh hide is the size of the analyzed sample. In any case, errors are again within

similar hardness categories. This fact can be used to explain what the neural network is reallydoing. It seems that it is able to generalize texture parameters characteristic of each work

Output /Desired BONE BUTCHERY DRY HIDE DRY WOOD

FRESH

HIDE

FRESH

WOOD SHELL

BONE 10 0 0 3 0 0 2

BUTCHERY 2 5 1 0 2 0 0

DRY HIDE 4 1 5 1 0 5 1

DRY WOOD 2 0 2 8 0 4 3

FRESH HIDE 1 6 1 0 0 0 0

FRESH WOOD 3 1 0 3 0 4 2

SHELL 6 0 1 1 0 3 13

Performance BONE BUTCHERY DRY HIDE DRY WOODFRESHHIDE

FRESHWOOD SHELL

MSE 0,189 0,0605 0,0819 0,1074 0,04058 0,1057 0,1151

NMSE 0,974 0,5631 0,9590 0,8387 2,192 0,8254 0,725

MAE 0,288 0,133 0,1761 0,2202 0,1109 0,2225 0,2392Min Abs

Error 0,005 2,4E-05 0,0006 0,00289 0,0005 0,0003 0,0003

Max Abs

Error 1,015 0,9161 0,9882 0,9690 0,67488 0,8836 0,86803

r 0,318 0,7056 0,3194 0,422 0,28249 0,4213 0,5303

Percent

Correct 35,71 38,4615 50 50 0 25 61,904


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activity, but not precisely each worked material. The network could not find formally definedgrammars for texture components placement rules, but it has had some success in creating anassociative memory for similar hardness categories.

Results of texture analysis are even better, when analyzing the kinematics of theworking activity (Table 3). The experimental database contained replications of three different

actions: cutting (longitudinal kinematics), scrapping (transversal kinematics), and butchery (akind of kinematics that pretends to be longitudinal but given the soft nature of the workedmaterial meat- is at the end half longitudinal/half transversal). Using the same textureattributes in the input layer, three output units, and a hidden layer with 13 neurons, we obtainthe following results:

Output / Desired KYNEMAT(L) KYNEMAT(T) KYNEMAT(L(T))

KYNEMAT(L) 196 43 0

KYNEMAT(T) 39 125 0

KYNEMAT(L(T)) 20 13 60

Performance KYNEMAT(L) KYNEMAT(T) KYNEMAT(L(T))MSE 0,159825824 0,147879337 0,042461323

NMSE 0,639813032 0,63808968 0,399318228

MAE 0,329403594 0,323901311 0,115223238

Min Abs Error 0,00045138 0,004611709 0,000183032

Max Abs Error 0,989362897 0,963606931 0,969773317

r 0,615687356 0,612099014 0,802275184

Percent Correct 76,8627451 69,06077348 100

Table 3. Results from Experiment 2: Discriminating between kinematic actions: cutting (longitudinal),

scrapping (transversal), butchery (half longitudinal-half transversal)(Original experimental data set)

Butchery activity was correctly identified in all tested cases, and longitudinal andtransversal kinematics was distinguished in a majority of cases! Nevertheless, these good resultscan be the consequence of a bad selection of the output categories. Longitudinal andTransversal kinematics have been replicated over different materials (shell, bone, dry wood,fresh wood, dry hide, fresh hide), but the third kind of kinematics was exclusive of a workedmaterial (meat). It would be possible that the neural network had learnt to distinguish butcheryfrom the other categories, but not the proper activity. To solve this problem we built a new

network to distinguish only longitudinal from the transversal action, and deleted from thedatabase all the butchery experiments (table 4). The network had the usual 35 central tendencyinputs, 13 units in the hidden layer, and only two outputs. The activation function of thoseoutput neurons was adjusted so that it can be read as a probability measure (the joint activationof both units sum 1). The results for the experimental training set are the following:

Output /

Desired KYNEMAT(T) KYNEMAT(L)

KYNEMAT(T) 143 86

KYNEMAT(L) 26 177

Performance KYNEMAT(T) KYNEMAT(L)


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MSE 0,173922583 0,174325181

NMSE 0,730265892 0,731956322

MAE 0,360646928 0,361481759

Min Abs Error 0,000365206 0,000365206

Max Abs Error 0,962687106 0,962687106

r 0,61404546 0,61404546

Percent Correct 84,61538462 67,30038023

Table 4. Results from Experiment 2: Discriminating between kinematic actions: cutting (longitudinal),

scrapping (transversal (Original experimental data set).

Using 20% of replicated tools not used for training as a test database, we obtain also excellentresults (table 5), showing the ability of the network to learn to discriminate between theworking activities, and hence, to discover the social cause behind the visual appearances oftexture.

Output /

Desired KYNEMAT(T) KYNEMAT(L)

KYNEMAT(T) 40 29

KYNEMAT(L) 12 43

Performance KYNEMAT(T) KYNEMAT(L)

MSE 0,21612187 0,217864788

NMSE 0,887577423 0,894735306

MAE 0,402846942 0,406095708

Min Abs Error 0,0230306 0,0230306

Max Abs Error 0,981177104 0,981177104

r 0,4237645 0,4237645

Percent Correct 76,92307692 59,72222222

Table 2. Testing the PEDRA system with additional data.

Rotational activities (that of burins) were correctly identified in all tested cases, andlongitudinal and transversal kinematics was distinguished in a majority of cases!

Conclusions

The surface of prehistoric lithic tools are not uniform but contain manyvariations; some of them are of visual or tactile nature. Such variations go beyond thepeaks and valleys characterizing surface micro-topography, which is the obvious frameof reference for textures in usual speaking. Such patterns are not only intrinsic to thesolid itself. Beyond those physical, geological characteristics of the raw material, somevisual features of an artifacts surfaces are consequences of the modifications havingexperimented that object along its history. After all, the surface of solids plays asignificant role in any kind of dynamic processes. Consequently, when we analyzemacro- or microscopically an objects surface, we should recognize some differentialfeatures which are the consequence of an action (human or bio-geological) havingmodified the original appearance of that surface.


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In this paper we have described and measured use-wear evidence as anarchaeological texture in terms of the particular dispersion of luminance values acrossthe surface. Textures have been analyzed as complex visual patterns composed ofentities, or sub-patterns, that have characteristic brightness, color, slope, size, etc. Thus,we have decomposed the image of the analyzed surface into regions that differ in the

statistical variability of their constitutive visual features.Once the texture elements have been identified in the image, we have computed

statistical properties from the extracted texture elements and used these as texturefeatures. The method of texture classification involves two main steps. The first step isobtaining prior knowledge of each class to be recognized. Normally this knowledgeencompasses some sets of texture features of one or all of the classes. This knowledgehas been acquired by experimentation replication in laboratory conditions. A neuralnetwork has been programmed to induce the best discriminant function. That is thesecond step.

Results show the reliability of this approach.

Aknowledgments

This research has been made possible thanks to different research projects funded by the Spanish Ministryof Research. Jordi Pijoan-Lpez also aknowledge a research grant from the catalan Government(Generalitat de Catalunya). Andrea Toselli and Assumpci Vila also contributed in different moments ofour research. We wish to aknowledge to our colleagues of the joint research team between UniversitatAutnoma de Barcelona and Instituci Mil i Fontanals (CSIC). This research should be consideredamong the results of the collaboration between both institutions.

References

BARCEL, J.A. PIJOAN-LOPEZ,J 2004, Cutting or Scrapping? Using Neural Networks to Distinguish Kinematics inUse Wear Analysis. In Enter the Past. The E-way into the Four Dimensions of Culture Heritage . Edited by Magistrat derStadt Wien. Oxford, ArcheoPress, BAR Int. Series 1227, pp. 427-431

BARCEL, J.A., PIJOAN-LPEZ, J., TOSELLI,A., VILA, A., 2008, Kinematics in use-wear traces: an attempt ofcharacterization thorugh image digitalization. Prehistoric Technology, 40 years Later: Functional Studies and the Russianlegacy. Edited by L. Longo and N. Skakun. BAR International series 1783, pp. 63-74. ArcheoPress, oxford (UK).

BARCEL, J.A., 2008, Computational intelligence in Archaeology. Information Science reference (The IGI Group). Henshey(NY).

DRIES, M.H. van den, 1998, Archeology and the Application of Artificial Intelligence. Case Studies on Use-wear Analysis ofPrehistoric Flint Tools. Archaeological Studies Leiden University No. 1., Faculty of Archaeology, University of Leiden(Holland).

PIJOAN-LPEZ, J., 2007, Quantificaci de traces ds en instruments ltics mitjanant imatges digitalitzades: Resultatsdexperiments amb Xarxes Neurals I Estadstica. PhD. Dissertation. Universitat Autonoma de Barcelona (Spain).
http://seneca.uab.cat/prehistoria/Barcelo/publication/CuttingScrapping.pdfhttp://seneca.uab.cat/prehistoria/Barcelo/publication/CuttingScrapping.pdfhttp://seneca.uab.cat/prehistoria/Barcelo/publication/CuttingScrapping.pdfhttp://seneca.uab.cat/prehistoria/Barcelo/publication/CuttingScrapping.pdfhttp://seneca.uab.cat/prehistoria/Barcelo/publication/CuttingScrapping.pdfhttp://seneca.uab.cat/prehistoria/Barcelo/publication/CuttingScrapping.pdf

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