Computer Vision Winter Workshop 2008, Janez Pers (ed.)Moravske Toplice, Slovenia, February 4–6Slovenian Pattern Recognition Society, Ljubljana, Slovenia

Registration of Manuscript Images using Rotation Invariant Features

Markus Diem1, Martin Lettner1, and Robert Sablatnig1

1Pattern Recognition and Image Processing Group,Institute of Computer Aided Automation,Vienna University of Technology, Austria

diem@prip.tuwien.ac.at

Abstract One medieval Slavonic manuscript is recorded,investigated and analyzed by philologists in collaborationwith computer scientists. The aim of the project is to de-velop algorithms that support the philologists by automati-cally deriving the description and restoration of the scripts.The parchment partially contains two scripts, where the firstscript was erased. In addition, the script is degraded due topoor storage conditions. In order to enhance the degradedscript, the manuscript pages are imaged in seven bands be-tween 330 and 1000 nm. A registration, aligning the resul-tant images, is necessary so that further image processingalgorithms can combine the information gained by the dif-ferent spectral bands. Therefore, the images are coarselyaligned using rotationally invariant features and an affinetransformation. Afterwards, the similarity of the differentimages is computed by means of the normalized cross cor-relation. Finally, the images are accurately mapped to eachother by the local weighted mean transformation. The algo-rithms used for the registration and preliminary results arepresented in this paper.

1 IntroductionSince ancient times vellum was used to write on, but thelaborious manufacturing made vellum a valuable material.This is why the scripts were erased and the parchment usedagain. The so-called palimpsests1 often comprise vestigesof the original text. During the 19th century scientists usedchemical means to read palimpsests that were sometimesvery destructive, using tincture of gall or later, ammoniumhydrosulfate. Modern methods of reading palimpsests usingmulti-spectral image acquisition are not damaging.

The codex, which is analyzed in cooperation with philol-ogists of the University of Vienna, is called Cod. Sin. slav5N and was found in 1975 at the St. Cathernine’s Monastery(Mount Sinai, Egypt). The codex is written in Glagolitsiawhich is the oldest known Slavic alphabet and consists of162 pages. Each page was captured eleven times in differ-ent spectral bands which results in a total number of 1782images that have to be enhanced.

In this paper the results of a multi-spectral image acqui-sition system and a method for a fully automatic image reg-istration are presented. The aim of multi-spectral imaging is

1Greek: palimpsestos - scraped again

to maximize the contrast between the erased and the secondscript as well as enhancing and making the degraded scriptvisible respectively. Since manual operations such as reposi-tioning the pages or camera changes are performed betweenthe acquisitions, a registration that aligns one spectral imageto the other is necessary. If the images are registered, theycan be combined, using for instance a principal componentanalysis.

In a previous approach, manuscript images were regis-tered using solely the cross correlation. In order to get re-liable results, the template images, which are details of theso-called reference image, needed to comprise at least onecharacter (≈ 130 × 130px). An image to which all otherimages, called sensed images, are aligned to is referred toas reference image. Since the cross correlation needed tobe computed over approximately a quarter of the sensed im-age, two similar characters could be mistaken. But the mainweakness of this approach is the dependency on rotationsbetween the images. Since the manuscript pages are reposi-tioned between both cameras, the algorithm must be able tohandle rotations up to 180◦ between the images.

Thus, a modified scale-invariant feature transform, whichis rotationally invariant, aligns the images coarsely. Wrongpoint matches are eliminated by computing the distance tothe second nearest neighbor and the RANSAC method. Af-terwards the images are aligned by estimating the affinetransformation matrix using the Least Squares Solution.Having aligned the images coarsely, the cross correlationis computed between a 16 × 16px template image and a32 × 32px search window of the sensed image. A localweighted mean mapping function, which is a local poly-nomial transformation, is estimated by means of the corre-sponding control points. Thus, non-rigid local distortionscaused by the changing curvature of the pages are corrected.

The paper is organized as follows. The following sec-tion characterizes related work and gives an overview in therange of image acquisition. Section 3 and Section 4 describethe image acquisition and the image registration in more de-tail. Numerical and visual results of the stated methods aregiven in Section 5. Finally, the last section gives a conclu-sion and an outlook.

Registration of Manuscript Images using Rotation Invariant Features [←]

2 Related WorkThere have been efforts in image analysis of historical doc-uments [1, 2, 15]. In general, differences between imageanalysis of ancient versus modern documents result partic-ularly from the aging process of the documents. Some re-lated studies in image analysis of historical documents arecovered in this section.

Multi- and hyper-spectral imaging has been used in awide range of scientific and industrial fields including spaceexploration like remote sensing for environmental mapping,geological search, medical diagnosis or food quality evalu-ation. Recently, the technique is getting applied in order toinvestigate old manuscripts [1]. Two prominent representa-tives are the Archimedes Palimpsest [2] and Tischendorf’sCodex Sinaiticus [3]. Easton et al. were the first to captureand enhance the erased writing of the famous Archimedespalimpsest by multi-spectral methods [2]. The system theypropose is modeled on the VASARI illumination system de-veloped at the National Gallery of London [13]. In thatproject it turned out that the adoption of spectral imagingproduces higher and better readability of the texts than con-ventional thresholding methods. Balas et al. developed acomputer controllable hyper-spectral imaging apparatus, ca-pable of acquiring spectral images of 5nm bandwidth andwith 3nm tuning step in the spectral range between 380-1000 nm [1]. This device was selected as the instrumentof choice for the Codex Sinaiticus Digitization Project con-ducted by the British Library in London [3].

Spectral images of palimpsests and other ‘latent’ textshave also been enhanced by the Italian company Fotoscien-tifica Re.co.rd. R©2 which provided for instance the picturesfor the EC project Rinascimento virtuale, devoted to the de-cipherment of Greek palimpsest manuscripts [6]. The ECproject IsyReaDeT developed a system for a virtual restora-tion and archiving of damaged manuscripts, using a multi-spectral imaging camera, advanced image enhancement anddocument management software [17].

Two cameras, in contrast to the mentioned imaging sys-tems, are used in this approach. A grayscale camera with anautomatic filter wheel takes seven images in different spec-tral bands. Additionally, color images and UV fluorescenceimages are taken with a second camera. By aligning the im-ages from both cameras to each other up to eleven channelsper pixel are available for further processing steps.

3 Image AcquisitionSince photographic techniques in the visible range haveproven to be insufficient with degraded manuscript pages,spectral imaging is applied [1, 15]. Images in differentwavelengths provide information that is invisible to the hu-man eye [15]. Generally, there are narrow spectral bands atwhich the maximum difference in the reflectance character-istics of each ink exists. The aim of multi-spectral imaging isto provide spectral image cubes, where the third dimensioncontains spectral information for each pixel. The degradedscript is enhanced by combining the spectral information.

2www.fotoscientificarecord.com/

For the acquisition of the manuscripts a HamamatsuC9300-124 camera is used. It records images with a reso-lution of 4000 × 2672px and a spectral response between330 and 1000 nm. A lighting system provides the requiredIR, VIS and UV illumination. In order to speed-up the ac-quisition process software was developed which controls theHamamatsu camera and the automatic filter wheel that isfixed on its object lens. Thus, the user can specify whichoptical filters to use and camera parameters such as expo-sure time. Having specified all parameters, the softwaretakes the spectral images and stores them on the harddisk.Low-pass, band-pass and high-pass filters are used to selectspecific spectral ranges. The near UV (320 nm - 440 nm)excites, in conjunction with specific inorganic and organicsubstances, visible fluorescence light [12]. Up to now thehistorical manuscripts are recorded with UV fluorescenceand UV reflectography. In principle, grabbing the visiblefluorescence of objects is possible with every camera. UVreflectography is used to visualize retouching, damages andchanges through e.g. luminescence. Therefore the visiblerange of light has to be excluded in order to concentrate onthe long wave UV light. This is achieved by applying low-pass filters and using exclusively UV light sources.

Hamamatsu camera

Nikon D2Xs camera

�lter wheel

manuscripts

Figure 1: Illustration of the acquisition system, with the Hama-matsu and the Nikon camera.

Seven filters allow the recording of the document pagesin seven different bands ranging from 330-1000 nm. Addi-tionally, a RGB color image and a UV fluorescence imageof each manuscript page are taken using a Nikon D2Xs cam-era. Due to the automatic image acquisition system the reg-istration of the images is solely needed for the correction ofthe differing distortions caused by the filter changes. There-fore, a simple correlation based approach and a consecutivelocal transformation could be applied. Since the grayscaleimages, taken in varying bands with the Hamamatsu camera

Markus Diem, Martin Lettner, and Robert Sablatnig [←]

system, shall be aligned to the color images taken with theNikon camera, a more extensive registration method needsto be implemented. The image acquisition system is illus-trated in Figure 1. The manuscript pages are first capturedwith the Hamamatsu camera and then moved in order to im-age them with the Nikon camera.

4 Image RegistrationFollowing the acquisition of the manuscripts, the imageshave to be registered. Image registration is a fundamentaltask in image processing used to match two or more picturestaken under different conditions.

As stated above image registration is the process of es-timating the ideal transformation between two different im-ages of the same scene taken at different times and/or differ-ent viewpoints. It geometrically aligns images so that theycan be overlaid. There is a wide variety of different methods(especially in remote sensing and medical imaging applica-tions) like the use of corresponding structures or mappingfunctions [16] which can be adapted for this application. Anoverview of image registration methods is given by Zitovaand Flusser [18].

4.1 Previous WorkAn automatic image registration has been implemented fora previous project. Thereby the control points are localizedusing an Otsu thresholding approach [14]. In order to re-duce low frequency image effects caused by the aging of themanuscripts the images are convolved with a homomorphicfilter. Having localized the control points in one image andenhanced both images the correspondence between the im-ages is computed using a normalized cross correlation. Thecross correlation needs to be computed between a templateimage, which has the size of the currently observed charac-ter (≈ 130 × 130px), and the whole sensed image. Thus,computing the correspondence as described is computation-ally more expensive than computing feature descriptors andmatching the feature vectors. Certainly, the cross correlationcould be computed between the template image and a searchwindow which is an image detail of the sensed image. How-ever, if the translation between the two images is greaterthan the chosen amplification of the search window, the cor-relation fails. Another weakness of the cross correlation isthat it is neither rotationally nor scale invariant. Hence, theobjects registered need to have a similar scale and rotation.

4.2 Coarse RegistrationLowe first introduced the Scale-Invariant Feature Transform(SIFT) in 1999 [10]. In order to get a scale-invariant fea-ture representation Lowe proposes to compute a scale-spacewhich was introduced by Lindeberg [9]. The control pointsare detected by computing the local maxima and minima ofthe Difference-of-Gaussians. Having assigned the orienta-tion to each control point, a local image descriptor is com-puted which is normalized by the orientation of the controlpoint. Afterwards, the control points are matched using theBest-Bin-First search algorithm.

Since the computation of the scale-space is computa-tionally expensive and the size of the objects is similar

in different images, the scale-space is not computed inour approach. Thus, each control point detected has thesame scale. The Difference-of-Gaussians approximates theLaplacian-of-Gaussians and is computed by the differenceof two images which are convolved with a gaussian filterkernel, having a different scale parameter σ. If the inten-sity value of the resultant image is greater or lower than theintensity of its 8 neighbors, a local extremum is detectedand the local descriptor is computed. It turned out, that theDifference-of-Gaussians detects too many local extrema fora registration task. In a 391 × 493px sample image 5000control points are detected. Considering that the transfor-mation is accurate enough if approximately 200 matches areused for the parameter estimation. Hence, the control pointsare localized using the Harris Corner Detector [7]. It detectsless control points with the same scale parameter σ than theDifference-of-Gaussians approach. Control points localizedwith the Harris Corner detector are robust against rotationalchanges. Figure 2 shows a comparison of the keypoint local-ization using the Difference-of-Gaussians method (left) andthe Harris Corner Detector (right). The squares representlocalized control points.

Figure 2: Comparison of the keypoint localization using theDifference-of-Gaussians method (left) and the Harris Corner De-tector (right). The squares represent localized keypoints.

The orientation assigned to each control point is com-puted similar to Lowe’s implementation [11]. First the im-age gradient magnitude m(x, y) and the orientation θ(x, y)are computed for each pixel of the smoothed image L(x, y).

m(x, y) =√

(L(x+ 1, y)− L(x− 1, y))2 + ...

+(L(x, y + 1)− L(x, y − 1))2 (1)

θ(x, y) = tan−1

(L(x, y + 1)− L(x, y − 1)L(x+ 1, y)− L(x− 1, y)

)(2)

An orientation histogram with 36 bins corresponding to360◦ is created. Each sample added to the histogram isweighted by its gradient magnitude and a Gaussian weight.Afterwards, the histogram is smoothed with a Gaussian ker-nel. The maximum of the histogram indicates the dominantdirection of local gradients. Figure 3 shows a test imagewhere the black arrows indicate the dominant orientation

Registration of Manuscript Images using Rotation Invariant Features [←]

for each control point. In contrast to Lowe’s example im-ages all arrows have the same lengths since they all have thesame scale.

Figure 3: Detail of a test image. The black arrows illustrate theorientation for each control point. Since the scale-space is not com-puted, all control points have the same scale.

In order to compute a local descriptor that characterizeseach control point the image gradients m(x, y) and the ori-entations θ(x, y) in a 16×16pxwindow around each controlpoint are considered. The coordinates of the descriptor andthe gradient orientations are rotated relative to the controlpoint orientation so that the features are rotationally invari-ant. Each gradient is weighted by a Gaussian window ofσ = 8 so that the descriptor does not change significantlywith small changes in the position of the window. The con-trol point descriptor consists of eight 4×4 planes where eachplane represents the spatial distribution of the gradients foreight different directions. The location of a gradient in thelocal descriptor depends on the rotated coordinates and theorientation. Each gradient is interpolated to its eight neigh-bors of the control point descriptor.

After the features are computed for both images, theyare matched using the nearest-neighbor algorithm. The Eu-clidean distance between the descriptor of each control pointof the reference and the sensed image is computed. Thecorrespondence of two control points is indicated by theminimal Euclidean distance. Since a control point may ex-ist solely in one of the two images, corresponding controlpoints are rejected if their distance to the nearest-neighboris less than 0.8 times the distance to the second-nearestneighbor. Control points which have more than one corre-spondence are discarded too. Having discarded the controlpoints according to this scheme ≈ 200 corresponding con-trol points are left for an image with 391× 493px.

Since false matches can exist after discarding the pre-viously mentioned control points and one outlier changesthe transformation estimation of the Least Squares Solutiondramatically, the RANSAC method is used to discard all re-maining outliers. RANSAC was introduced by Fischler andBolles [4]. This approach computes the affine transforma-

tion using three randomly selected matching points. Havingtested all remaining control point pairs, the model is rees-timated from the entire set of hypothetical inliers. Thesesteps are repeated until the distances between points and themodel meet a given threshold. This method discards in ourapproach ≈ 8.3% of the matching control points.

Afterwards, an affine transformation matrix is computedusing the Least Squares Solution and all remaining corre-sponding control points.

4.3 Cross CorrelationHaving aligned the two images coarsely using modifiedSIFT Features and a global affine mapping function, a nor-malized cross correlation is computed at the locations of thepreviously found control points. The aim of the cross corre-lation and the subsequent local mapping function is to cor-rect non-rigid distortions. The features detected in the im-ages can be matched by means of the image intensity valuesin their close neighborhood, the feature spatial distribution,or the feature symbolic description [18]. Cross correlationis an area-based method which does not need features of im-ages which have to be registered. The location of the con-trol points are detected exclusively in the reference image inorder to avoid false correspondence. Since the images arecoarsely aligned, the search windows in the sensed imageare set to the same locations as the template images.

The cross correlation calculates the difference of two im-age details by means of a modified Euclidean distance. Thesize of the template image is 16 × 16px. Since the imagesare coarsely aligned by an affine transformation, the searchwindow needs not to be larger than twice the template im-age. Having defined the image details which need to be com-pared, the template image is shifted over the entire detail ofthe sensed image. For each shift the correlation between thetemplate and the search window is computed:

c(m,n) =∑

x

∑y

f(x, y) t(x−m, y − n) (3)

where f(x, y) denotes the gray values of a detail of thesensed image and t(x, y) the template image. Varying mand n shifts the template over the search window. The resul-tant function c(m,n) indicates the strongest correspondenceof the template image and the search window by the abso-lute maximum. Hence the control point of the sensed imageis placed at the coordinates of the absolute maximum.

The cross correlation is variant to changes in the im-age amplitude caused, e.g. by changing lighting condi-tions. Consequently, the correlation coefficient normalizingthe template as well as the search window of the sensed im-age is computed. The dynamic range of the normalized crosscorrelation γ(m,n) moves, independently to changes in theimage amplitude, between −1 and 1.

According to the templates magnitude and the propor-tion of the template and the search window, the computationperforms better in the frequency domain than in the spatialdomain.

4.4 Local TransformationHaving determined the control points, the parameters of themapping function are computed. Images which possess only

Markus Diem, Martin Lettner, and Robert Sablatnig [←]

global distortions (e.g. rotation) may be registered with aglobal mapping function. Likar and Pernus mentioned thatthe global rigid, affine and projective transformations aremost frequently used [8]. As a consequence of non-rigiddistortions such as the changing lenses or curvature of a sin-gle page, the images have to be registered using a curvedtransformation.

A global mapping function is practicable if a low num-ber of parameters are needed to define the transformation(e.g. rigid or affine transformations). Transformations us-ing polynomials of order n are defined by at least n + 1parameters, which results in a complex similarity functionalthat has many local optima. To overcome this problem a lo-cal mapping function is applied. The local weighted meanmethod [5] is a local sensitive interpolation method. It re-quires at least 6 control points which should be spread uni-formly over the entire image. Polynomials are computedby means of the control points. Thus, the transformationof an arbitrary point is computed by the weighted mean ofall passing polynomials. Besides, a weighting function isdefined which guarantees that solely polynomials near anarbitrary point influence its transformation.

5 ResultsHaving discussed the implemented methods, their results arepresented in this section. Both, the modified scale-invariantfeatures and the normalized cross correlation; have beentested on real manuscript images. In addition to tests withreal image data, the invariance against rotation of the coarseregistration was computed. Therefore two image details ofeach spectral band and different pages and six RGB imagedetails captured with the Nikon camera were taken into ac-count (see Figure 4). Each test image was rotated 30 times(0◦, 12◦, 24◦...) which results in a total of 660 tests. Thus,the rotation between the test image pairs is known. After-wards the affine transformation model is estimated using themodified SIFT approach. Hence, the rotational error ϕerr ofthe coarse registration is computed according to:

ϕerr = ‖sin−1(α)− sin−1(−α)‖ (4)

where α is the specified rotation of the test image and α isthe estimated rotation to register the rotated test image withthe initial test image. The resultant maximal error is 0.87◦

and the minimal error is ≈ 0◦ (exactly: 1.44◦ ·10−15). Themean error of all 660 tests is 0.06◦. Additionally the medianwhich is statistically more robust against outliers is men-tioned in order to show the trend of the mean error: 0.02◦.Figure 5 shows the minimum, maximum and mean error foreach sampled angle. The two peaks in all lines are near 90and 270◦. This attributes to the maximal interpolation errorwhich increases before it is zero at 0◦, 90◦, 180◦ and 270◦.Near 0◦ and 180◦ are smaller peaks because the closest sam-ple is ±12◦ whereas it is ±6◦ at 90◦ and 270◦.

Resulting images of the methods are presented in Fig-ure 5 - 9. Figure 6 shows the corresponding control pointsof the SIFT features before wrong correspondences are dis-carded. The final set of control points which is used for theestimation of the affine transformation can be seen in Fig-

Figure 4: Two examples of test images used. Image (a) showsa page detail with degraded characters. Image (b) shows anotherpage captured in a different spectral band with well-preserved char-acters.

ure 7. After the computation of RANSAC, no false corre-spondences are left.

Figure 6: The corresponding control points between twomanuscript images before the consistency check. As can be seen,there are still wrong correspondences. White boxes represent con-trol points without a corresponding one.

Figure 8a shows the template image with a control pointlocated in its center. The search window with the corre-sponding control point of the RGB image is shown in Fig-ure 8b. Computing the normalized cross correlation of thesetwo images results in the third image (see Figure 8c) wherethe strongest peak shows the point with the strongest corre-lation. Both, the template image (16 × 16px) and the seekwindow (32 × 32px) of the sensed image are resized for abetter visualization.

The affine transformation, estimated with the modifiedSIFT approach, and the local weighted mean method arecompared to each other using spectral and RGB images (seeFigure 9). Since the images possess local non-linear distor-tions, only certain parts of the registered images correspondif an affine mapping function is applied. Hence, the fartherthe points are away from the corresponding area, the morethey differ. That is why a local sensitive mapping function isapplied to compute the transformation after the images arecoarsely aligned with an affine transformation. Erroneouspixels still exist in the final image. These attribute to thechanging shadows caused by the changing page curvatureand illumination due to the shifting of the book between thetakings.

Registration of Manuscript Images using Rotation Invariant Features [←]

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0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Angle in °

Abs

olut

e Er

ror i

n °

Max ErrorMean ErrorMin Error

Figure 5: The maxmial, minimal and mean error for each sampled angle. The two peaks attribute to the maximal interpolation error near 90◦

and 270◦.

Figure 8: A control point at the center of the template image (a), which is 16 × 16px. The search window of the sensed image with thecorresponding control point (b). The normalized cross correlation where the strongest peak indicates the location with the strongest correlation(c).

Figure 7: The corresponding control points after discarding pointswith the RANSAC algorithm. The sensed image was rotated about45◦. In contrast to Figure 6, this image contains no wrong cor-respondences. After the consistency check, 87.5% of the corre-sponding control points remain.

Figure 9: Difference image of two registered images using anaffine transformation (a). The errors in the middle of the imageresult from the changing page curvature. Difference image of thesame sample images using the local weighted mean method (b).The pages are registered correctly, remaining errors result fromchanging illuminations.

Markus Diem, Martin Lettner, and Robert Sablatnig [←]

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6 Conclusion and OutlookThis paper introduces a multi-spectral image acquisitionsystem for ancient manuscripts. Multi-spectral imaging al-lows philologists to analyze ancient manuscripts contactless.In Addition, supplementary information is gained, visualiz-ing characters of the degraded script that cannot be seen bythe human eye.

Furthermore a fully automatic registration, aligning twodifferent images with each other, was depicted. The de-scribed approach was compared to a previous registrationmethod. Besides discussing the proposed registration ap-proach, the methods were tested on real images. Addition-ally, numerical results of the coarse registrations accuracywere given in Section 5.

The registration method proposed is planned to be com-pared to some others (e.g. [8]) and evaluated in more detailby applying it to synthetic images. The aim of the currentproject is a combination of the acquired images by means ofa principal component analysis or comparable methods, inorder to enhance the degraded script.

AcknowledgementThis work was supported by the Austrian Science Founda-tion (FWF) under grant P19608-G12.

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