Digital Library of Dr. Szendrei Rudolf

Automatic Raster-Vector Conversion of Topographic maps

	Szendrei, Rudolf - Elek, István - Fekete, István - Márton, Mátyás A Knowledge-Based Approach to Raster-Vector Conversion of Large Scale Topographic Maps Volume of extended abstracts, pp 66. 2010 Conference of PHD Students in Computer Science (CS²), Szeged, Hungary, June 29 - July 2, 2010. Organizing committee: Kálmán Palágyi, Balázs Bánhelyi, Tamás Gergely, Zoltán Kincses Abstract The scanned paper based maps in raster image format are suitable for humans, but geoinformatics prefer to use the properly converted, vectorized maps. The important topographic maps are already vectorized in most countries by a cumbersome, manual procedure. However, the task of raster-vector conversion of paper based maps will not become obsolete within the next few years. Newly issued maps and the updating of old ones will still require this activity. In the IRIS project the authors have elaborated the theoretical background of a raster-vector conversion system, and they have developed the prototype of some components of the system. The aim of the development is to automatize the raster-vector conversion as much as possible. This goal puts an emphasis on the knowledge based approach. This article will focus on the automatic recognition and conversion of the three main types of map symbols, to improve the efficiency of the recognition system. Point-like symbols are small icons each representing a real object (e.g. a monument). The recognition algorithm tries to identify these symbols based on given symbol patterns. Each connected pixel set under a given size limit will be matched against the data base of patterns. Surface-like symbols cover a region with a solid color, or with a pattern (e.g. lake or scrub). The procedure first determines the smallest repetitive part (kernel) of the texture which can be identified by the algorithm used for point-like symbols. In order to identify linear symbols (e.g. roads, railroad) both line style and topology must be recognized. To determine topology a graph is created using the end- and fork-points of the road-like graphics. Currently, the automatic recognition of some kinds of map symbols (e.g. texts) is beyond the scope. Thus, the vectorized coverage generated automatically does not contain all of the elements occurring on the original raster map. Furthermore, the algorithms used for recognition provide the possibility for human expert's intervention in the case of false detection. An important point in the expertise of human interpreters is, for example, the knowledge of the order of map layers they have been printed in. The inclusion of this knowledge would make the conversion much more intelligent. Keywords parallelization, parallel processing, image processing, image filters Abstract (PDF)
	Szendrei, Rudolf Automatic Parallelization of Raster Image Filters Proc. AIPR-09, pp 30-33. 2010 International Conference on Artificial Intelligence and Pattern Recognition (AIPR-10), Orlando, Florida, USA, July 12-14, 2010. ISBN: 978-1-60651-015-5, Publisher: ISRST Editors: Zoran Majkic, Dan Tamir, Guoyin Wang Abstract The processing of high resolution raster images involves high computing load. Most of the programmers and image specialists are not familiar with parallel programming. Nowadays almost all desktop computers have multiple core processors, so it is straightforward to use parallel programming to make efficient image processing algorithms. We will show a method that is able to automatically parallelize a sequential image filter. Furthermore, we will show another method that decreases the memory consumption of raster image filters. The later one uses a virtual image, so the input image can be safely overwritten with the result image. By using these methods, beginner programmers could even use the benefits of the multicore processor based computers, turning their filters into faster and multithread-safe filters. We have carried out a basic testing of kernel based raster image filters, and we have experienced a linear, n times speed-up factor, where n is the number of CPU cores. Keywords parallelization, parallel processing, image processing, image filters Full Text (PDF) , Webpage - www.promoteresearch.org - AIPR-10 (URL)
	Szendrei, Rudolf - Elek, Istvan - Fekete, Istvan Automatic Symbol Recognition for Topographic Maps. Geomatika - Scientific Journal of RTU., pp 45-52. Riga Series 11, Vol. 6, 2009 Abstract This paper introduces a method that is able to convert the symbols of vectorized maps into polygon attributes. It reduces the required storage space of the database by removing the corresponding polygons from the vectorized data model. This procedure is presented with optimized, linear runtime pattern matching on the raster image source of the map, where the symbols are handled as special textures. This method will be improved by using a raw vector model and the kernel symbols. The kernel of the symbol is the smallest part of the image which is tiling the image of the symbol. The optimization of the pattern matching is based on the edge filtered map and symbol images, where the points of interest are determined by thresholding the filtered images. These points are evaluated at pattern matching as possible symbol locations. The efficient method provides a rotation independent symbol recognition using the edge filtered map and symbol images, where edges are converted to vectors, then the angle of the possible rotation is calculated from the corresponding vector datas. Keywords symbol recognition, pattern matching, raster-vector conversion, topographic map Full Text (DOC) , Webpage - Geomatika Riga (URL)
	Szendrei, Rudolf - Elek, Istvan - Fekete, Istvan Texture based recognition of topographic map symbols Proc. AIPR-09, pp 7-10. 2009 International Conference on Artificial Intelligence and Pattern Recognition (AIPR-09), Orlando, Florida, USA, July 13-16, 2009. ISBN: 978-1-60651-007-0, Publisher: ISRST Editors: Dimitrios A. Karras, Zoran Majkic, Etienne E. Kerre, Chunping Li Abstract This paper introduces a method that is able to assign the symbols of vectorized maps into polygon attributes. It reduces the required storage space of the database by removing the corresponding polygons from the vectorized data model. This procedure is presented with optimized pattern matching on the raster image source of the map, where the symbols are handled as special textures. This method will be improved by using a raw vector model and the kernel symbols. Keywords symbol recognition, pattern matching, raster-vector conversion, topographic map Full Text (PDF) , Full Text (PDF (co-author website)) , Presentation (PDF) , Webpage - www.promoteresearch.org - AIPR-09 (URL)

Scheduling of Parallel Processes

	Iványi, Antal - Szendrei, Rudolf Scheduling of Parallel Processes. Book of Abstracts, 46, 6th Joint Conference on Mathematics and Computer Science (MaCS'06), Pécs, Hungary, 2006. Abstract A binary matrix A = [A_ij]_mxn is called good, if it contains at most one 1's in each column; the matrix is called schedulable, [5,6,8] if it can be transformed into a good matrix repeating the following operation: we remove any zero element a_ij = 0, shift the elements a_i,j+1...a_i,n to left and add a new element a_i,n = 0 to the i-th row of the matrix; the matrix is called safe, if for any k (k = 1,...,n) holds that the first k column of the matrix contain at most k 1's. Let Z = [z_ij]_mxn be a matrix containing independent random variables having the join distribution P(z_i,j = 1) = p and P(z_i,j = 0) = 1 - p. Investigating the features of the good and safe matrices in the case m >= 1 we give lower and upper bound of the asymptotic probability [1,2,3,4,7] of the event that a concrete realisation of the matrix Z is schedulable.
	Szendrei, Rudolf Párhuzamos folyamatok ütemezése. TDK 2006 - ELTE IK, TDK 2006 - ELTE IK, Budapest, Hungary, 2006. Abstract A binary matrix A = [A_ij]_mxn is called good, if it contains at most one 1's in each column; the matrix is called schedulable, [5,6,8] if it can be transformed into a good matrix repeating the following operation: we remove any zero element a_ij = 0, shift the elements a_i,j+1...a_i,n to left and add a new element a_i,n = 0 to the i-th row of the matrix; the matrix is called safe, if for any k (k = 1,...,n) holds that the first k column of the matrix contain at most k 1's. Let Z = [z_ij]_mxn be a matrix containing independent random variables having the join distribution P(z_i,j = 1) = p and P(z_i,j = 0) = 1 - p. Investigating the features of the good and safe matrices in the case m >= 1 we give lower and upper bound of the asymptotic probability [1,2,3,4,7] of the event that a concrete realisation of the matrix Z is schedulable. Full Text (PDF) , PowerPoint Presentation (PPT)

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