Drawn maps consist of multiple object types. The most important are line objects which represent infrastructure. Attributes of these objects are essential for many tasks but in raster format they provide only low level information. Vectorization must be used to obtain vector data. In this paper general vectorization process consisting of five stages is proposed. For these stages short discussion and basic recommendations are given and some proper methods are presented

Peter Tarabek

Communications - Scientific letters of the University of Zilina

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65C O M M U N I C A T I O N S    4 / 2 0 0 9   1. IntroductionExtraction of graphical information from raster maps is a com-plex problem. Vector data which represent the necessary infor-mation on networks and need less space for their storage are usedin many tasks and are essential for GIS and CAD applications.Drawn maps consist mainly of line objects whose length is muchlarger than the thickness. These objects may represent road infra-structure but the raster data provide only low level information. Vec-torization process needs to be used to transform this data to vectorform. Conservation of connectivity and shape of the line objects aretwo most important conditions for vectorization process.2. Vectorization processThere are three main approaches which can be used in vector-ization process: sparse-pixel vectorization (SPV), matching oppo-site contours (MOC) and skeletonization.SPV [1, 2] is fastest from the mentioned approaches becauseit processes only small portion of pixels in image. The main dis-advantage of SPV methods is that they not preserve connectivityif the parameters are not set up correctly. MOC methods [2, 3]don’t visit all pixels and work either directly on the contours or ona polygonal approximation of it. However hard-to-master thresh-olds and heuristics need to be used for complex drawings [2]. Skeletonization methods compute medial axis called skeleton.There are many papers about different skeletonization techniques[4, 5]. Some of these techniques can directly produce vectors. Themost common are thinning techniques [6, 7, 8] which remove outerpixels layer by layer in iterative process until one pixel thick skele-ton is produced. Thinning conserve connectivity and shape of theobjects and is used in the proposed vectorization process. On theother hand thinning is sensitive to noise and sometimes producesdistortion in junction points.The process of vectorization proposed in this paper consistsof 5 stages (Fig. 1) and it is focused on line objects. Because ofhuge variety of possible representations of individual objects, thereis no specific vectorization process which can deal with all drawnmaps. When dealing with line objects this situation is easier. Theseobjects are represented in a very similar manner so if the rightmethods are applied for each stage this process can be used fora wide range of different maps.2.1 SegmentationBesides line objects, maps usually contain other objects liketext, symbols and regions. In this stage line objects are separatedfrom others. The result is a binary image where all line objectsR E V I E WGENERAL VECTORIZATION OF LINE OBJECTS IN DRAWN MAPSPeter Tarabek *Drawn maps consist of multiple object types. The most important are line objects which represent infrastructure. Attributes of these objectsare essential for many tasks but in raster format they provide only low level information. Vectorization must be used to obtain vector data. Inthis paper general vectorization process consisting of five stages is proposed. For these stages short discussion and basic recommendations aregiven and some proper methods are presented.Keywords: vectorization, skeletonization, skeleton, thinning, raster-to-vector* Peter TarabekDepartment of Transportation Networks, Faculty of Management Science and Informatics, University of Zilina, Slovakia, E-mail: Peter.Tarabek@fri.uniza.skFig. 1 Stages of the proposed general vectorization processhttps://doi.org/10.26552/com.C.2009.4.65-6866  C O M M U N I C A T I O N S    4 / 2 0 0 9should be represented as black pixels and all other objects includ-ing the background as white pixels. This reduces amount of dataand it also speeds up and simplifies future processing.The separation of line objects is accomplished by threshold-ing. A general condition for threshold can be defined as follows:(1)where g(x,y) is the value of pixel with coordinates x, y in a result-ing binary image, f(x,y) is the original value of pixel and T isa threshold value.Several threshold values are usually used to correctly separateobjects in image. There are many local and global threshold tech-niques which can be used. Although a correct automatic set up ofthreshold parameters for all kinds of maps is not possible sometype of automation is achievable [9, 10, 11].2.2 Pre-processingIn pre-processing, a binary image is processed to remove imper-fections and to amplify desired features of line objects. A binaryimage should be improved according to weaknesses of techniquesused in a processing step to prevent future errors. Imperfectionsin a binary image are caused by a bad condition of paper maps,process of scanning, usage of segmentation methods, complexityof map and variety of possible representations. In Fig. 2 thinningwas used to create a skeleton from an inaccurate input. The acquiredskeleton differs from the desired skeleton because thinning is sen-sitive to imperfections in the original image. Accurate pre-processing for thinning should fulfill these tasks:  remove isolated small objects reduce boundary noise (contour intrusions and protrusions) fill small holes in objects connect disconnected objects,, ,0, if ,g x yif f x y Tf x y T11$=___iii*Binary morphology operators opening and closing providevery good results for this kind of tasks. Operations opening andclosing can be combined to remove all the mentioned imperfec-tions. Order of operations and number of repetitions are the mostimportant decisions which need to be made in order to obtainaccurate results [12].2.3 ProcessingIn this step the binary image is prepared for raster-to-vectorconversion. Features like thickness of objects, information abouttheir contours and number of components can be extracted andsome level of feed back is possible by measuring the quality ofresult [13] and analyzing extracted features.Thinning which produces a skeleton is used in the proposedvectorization process. The skeleton is ideal for line objects becauseit is represented by a set of one pixel thick lines which are a naturalrepresentation of line objects such as roads. Thinning should fulfillthese requirements: Skeleton should be one pixel thick Connectivity should be preserved Shape and position of the junction points should be preserved Skeleton should lie in the middle of a shape (medial axis) Skeleton should be immune to noise (especially to boundarynoise) Excessive erosion should be prevented (length of lines and curvesshould be preserved)As noted previously, thinning is sensitive to noise and to otherimperfections like holes in objects. These problems can be solvedby binary morphology operators recommended in the previouschapter. Another often mentioned disadvantage is a low computa-tional speed which can be improved by using the contour approachto thinning proposed in [14].2.4 Raster-to-vector conversionConversion to vector form for one pixel thick skeleton can beperformed in two main steps: nodes and edges recognition. Usuallythe local approach is used. In this approach 33 neighborhood ofeach pixel is inspected. In this case candidates for nodes can berecognized by having a connectivity number (CN)  2 (numberof black to white transitions in 33 neighborhood). Nodes areselected from these candidates based on additional rules. A specialcase of nodes are end points which can be recognized by havingCN  1. In the next step edges are recognized. Edge pixels haveCN  2 so they can be easily traced. This approach producesexcessive nodes and edges and need further processing to yieldaccurate results.Another possibility is to form clusters of candidates for nodes.For each candidate the priority number based on the number of8-neighbors (N8) candidates and the number of 4-neighbors (N4)candidates is computed. For each cluster, the candidate withR E V I E WFig. 2 Desired and acquired skeleton of input image67C O M M U N I C A T I O N S    4 / 2 0 0 9   largest priority is selected for a node. This approach is used in theproposed vectorization process. The difference between the localapproach and cluster approach is shown in Fig.3.2.5 Post-processingAfter the raster-to-vector conversion is done vector data usuallycontain a large number of vertices that can be reduced by somekind of polygonal approximation [15, 16]. Also straight lines andarcs can be recognized in this phase [17]. Vector data can be usedto perform additional processing including pruning, removingincorrectly separated objects, improving quality of junction pointsand recognizing attributes such as length, width and color of theedges.3. ConclusionThe general vectorization process for line objects in drawnmaps was discussed in this paper. For each of its steps a short dis-cussion of the problem was given and suitable methods were rec-ommended. The vectorization process is based on thinning whichconserves connectivity and shape of line objects. The operator isrequired only at the beginning of the process to set up parameters.Processing and raster-to-vector conversion are fully automatic anddo not require any settings.The results of the proposed vectorization process are shownin Figs. 4 and 5. The processing time for the map in Fig. 4 withdimensions 9851114 pixels was less than 2 seconds. For themap with dimensions 600x396 pixels shown in Fig. 5, processingtime was less than 0.1 second.R E V I E WFig. 3 Local approach (left) and cluster approach (right)Fig. 4 Result of proposed vectorization process68  C O M M U N I C A T I O N S    4 / 2 0 0 9References[1] DORI, D., LIU, W.: Sparse Pixel Vectorization: An Algorithm and Its Performance Evaluation, IEEE Transactions on Pattern Analy-sis and Machine Intelligence, Vol. 21, 1999, ISSN 0162-8828, pp. 202–215.[2] TOMBRE, K., AH-SOON, C., DOSCH, P., MASINI, G., TABBONE, S.: Stable and Robust Vectorization: How to Make the RightChoices, Graphics Recognition-Recent Advances, 2000, ISBN 978-3-540-41222-9, pp. 3–18.[3] HORI, O., TANIGAWA, S.: Raster-to-Vector Conversion by Line Fitting Based on Contours and Skeletons, Proceedings of the SecondInternational Conference on Document Analysis and Recognition, 1993, pp. 353–358.[4] ZOU, J. J., HONG, Y.: Vectorization of Cartoon Drawings, Selected papers from the Pan-Sydney workshop on Visualisation -Volume 2, 2000, ISSN 1445-1336, pp. 77–78.[5] SUZUKI, S., UEDA, N.: Robust Vectorization Using Graph-Based Thinning And Reliability-Based Line Approximation, ProceedingsCVPR '91, 1991, ISBN 0-8186-2148-6, pp. 494–500.[6] BERNARD, T. M., MANZANERA, A.: Improved Low Complexity Fully Parallel Thinning Algorithm, In Proceedings of the 10thinternational Conference on Image Analysis and Processing, 1999, ISBN 0-7695-0040-4.[7] ZHANG, T. Y., SUEN, C. Y.: A fast parallel algorithm for thinning digital patterns, Commun. ACM, 27(3), 1984, ISSN 0001-0782,pp. 236–239.[8] LAM, L., LEE, S., SUEN, C. Y.: Thinning Methodologies-A Comprehensive Survey, IEEE Transactions on Pattern Analysis andMachine Intelligence, Vol. 14, No. 9, 1992, ISSN 0162-8828, pp. 869–885.[9] BOBIN, J., STARCK, J.-L., FADILI, J., MOUDDEN, Y., DONOHO, D.L.: Morphological Component Analysis: an adaptativethresholding strategy, IEEE Transactions on Image Processing, Vol. 16, No. 11, 2007, ISSN 1057-7149, pp. 2675–2681.[10] OTSU, N.: A threshold selection method from gray-level histogram, IEEE Transactions on System, Man and Cybernetics, Vol. 9, No.1, 1979, ISSN 0018-9472, pp. 62–66.[11] LIAO, P. S., CHEN, T. S., CHUNG, P. C.: A fast Algorithm for Multilevel Thresholding, Journal of Information Science and Engi-neering 17, 2001, pp. 713–727.[12] TARABEK, P.: Morphology image pre-processing for thinning algorithms, Journal of Information, Control and ManagementSystems, Vol. 5, No.1, 2007, ISSN 1336-1716, pp. 131–138.[13] TARABEK, P.: Performance measurements of thinning algorithms, Journal of Information, Control and Management Systems, Vol.6, No. 2, 2008, ISSN 1336-1716, pp. 125–132.[14] TARABEK, P.: A Contour Approach to Thinning Algorithms, Communications – Scientific Letters of the University of Zilina, Vol.11, 3/2009, ISSN 1335-4205, pp. 43–48.[15] KOLESNIKOV, A., FRANTI, P.: Data Reduction of Large Vector Graphics, Research Report, A-2003-2, Department of ComputerScience, University of Joensuu, Finland, 2003, ISBN 952-458-311-9.[16] SARFRAZ, M.: Optimal Curve Fitting to Digital Data, International Journal of WSCG, Vol. 11, No. 1, 2003, ISSN 1213-6972.[17] DOSCH, P., MASINI, G., TOMBRE, K.: Improving Arc Detection in Graphics Recognition, Proc. 15th Int'l Conf. Pattern Recogni-tion (ICPR '00), Vol. 2, 2000, ISBN 0-7695-0750-6, pp. 243–246.R E V I E WFig. 5 Result of proposed vectorization process

General Vectorization of Line Objects in Drawn Maps

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General Vectorization of Line Objects in Drawn Maps

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