Vector Processing Contours

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2 Vector Processing Contours Andrey Kirsanov Department of Automation and Control Processes MAMI Moscow State Tecnical University Moscow, Russia A.Vavilin and K-H. Jo Department of Electrical Engineering University of Ulsan Ulsan, Korea {andy Abstract process of object recognition in satellite images of ig resolution is a complex task associated wit a large work on time and complexity of te operator. Tis paper describes an innovative approac for solving tis problem. Based on monocromatic ig-resolution satellite images (in te process of using data from te QuickBird satellite wit a maximum resolution of 0.6 meters per pixel) geo data bitmap and vectorized output are received (sape files). Te principle of object recognition in a satellite image is based on te allocation of edges in te gradient transition using a tresold filter. Obtained data is ten transformed to a vector output using straigt line detection and connected components analysis. Te proposed metod allows to process satellite images of large size wit ig performance. Performance of te proposed metod could be improved by using GPU-based computations. Elevation Data - for constructing 3D models of te Eart's surface Imagery Data - to create a virtual textures Map Data - Identification of geodetic data Culture Data - to build 3D models of objects Culture Data Map Data Keywords-filter; vector data; 3D maps; GIS; I. INTRODUCTION One of te callenging problems in aero and space geodesy is te building contour identification for te transfer of tese contours on a map and furter transfer cards in 3D-image. Reconstruction of tree-dimensional models of buildings and objects on te map is a necessary solution to some problems, suc as: GIS, a database for fligt simulator, analysis and solution of critical situations prevailing in te city, te study te eart's surface layout of urban areas and cities development. Vectorization of satellite images is a laborious and time-consuming task wic requires te presence of te uman operator. Te amount of work deps directly on te time of te operator. Also, in addition to te process of recognition of buildings from satellite images needed vectorized tird-party sites, suc as: trees, roads, walls, fences, power lines, railroad tracks. As a result, must recognize not only te geometric sapes and colors, too (for example recognition of trees). However, te main task is set in te drawing outlines of suc objects as buildings, te difficulty lies in te complexity of te geometric sape of te object and te number of buildings. To create a complete database containing not only te tree-dimensional objects are necessary input data to te grid eigts of land, geo binding, virtual texture. In tis paper we ave listed te data were not considered because at tis time tere are many metods of obtaining tis information, and metods of treatment. Following date is necessary for building a 3D scene: Imagery Data Elevation Data Figure 1. Te necessary data to create 3D maps Te main task in te 3D reconstruction is te vectorization of raster images for Culture Data. Currently, tere are a number of ready solutions provided by GIS companies, suc as: ESRI, Presagis, MultiGen, etc. Most of te software does not use post-processing of satellite images and use only te clustering of data by color [1], [2]. However, tis metod does not allow andling bitmaps wit sufficient accuracy and ig performance. Wen clustering te color does not identify te objects. Tis makes it impossible to identify te type of vector objects: buildings, roads, fences, trees. As a result of tis problem to be addressed to te uman operator. Te solution to tis problem is te preparation of satellite imagery to 3D reconstruction by Tresold Filter.

3 II. SOBEL FILTER AND THRESHOLD FILTER A. Te Algoritm In tis work is considered te problem of 3D map reconstruction from raster image. Te system is designed wit te following algoritms: Processing of satellite images using a tresold filter Figure 4. Sobel Filter Reading after te filter and searc for images of straigt lines Clustering vector data on te types of objects (area, line, point) Creating of tree-dimensional objects from te resulting vector data Downloading te results objects in te database of 3D map Figure 2. Te flowcart of te automatic building detection Te proposed metod is based on grayscale image processing. However, it could be exted to use color information as an additional segmentation criterion. Te first part is allocated bitmap contour image. Algoritms suc programs are known. Traditionally, effective, in particular filters Sobel (Sobel) and Scarra (H. Scarr) [1]. Te disadvantage of tese filters is a significant computational time and noise resulting image. Terefore, in tis paper, we propose a tresold filter, revealing te contours of grayscale images tickness of not more tan one pixel. Filter algoritm as follows. Let eac pixel of te image is encoded number from 0 to 1. Te black color corresponds to 0, wite - 1. In te first pase te wole image is replaced by te maximum possible contrast to some tresold. Pixels wit a value smaller tan are replaced wit black, large - wite. Next by orizontal scan images and identifies te left and rigt boundaries of solid black areas. Te internal parts of tese regions are replaced wit wite borders remain black. Te same scanning and vertically, ten te received images are bled (multiplies of pixel values of 0 and 1). Figure 5. Tresold Filter A comparison of te Sobel filter and proposed filter. Processing images in Figure 3 Sobel filter gives an image containing alftone and sarp contours (Fig. 3). Figure 4 sows te result of data processing tresold filter. Obviously, for tracing te second result is preferable. In addition to image quality in suc problems plays an important role during recognition. In some cases, suc as te problem of orientation, recognition process must ave ig performance, suc as in tracking fast moving objects. Tis is particularly sensitive to fast moving objects (location planes, missiles and. Etc.). Grap comparing te speed of filters to identify te contours (Fig. 6) indicates tat an increase in image size advantage of te proposed filter, te tresold increases. Te calculations were performed in te Maple 11, used te operators of package ImageTools. Figure 6. Demonstration of performance of te filter Sobel and tresold filter Figure 3. Te necessary data to create 3D maps

4 It is noticed tat te result of te tresold filter deps on te coice of values. Deping on te brigtness and contrast of te image, tis value was varied around te average value of 0.5 and was cosen manually or as te average value N / N, were i - pixel value, N n m - te i1 i number of pixels te image size n m. Anoter approac - building density curve (Figure 7). Te grap on te x-axis - te value of a pixel (from black 0 to wite 100), te y-axis - te number of pixels. Te tresold is taken as te expectation: k / K i1 i i Figure 8. Input Image 1 K k( ) d In fact, K - area of te curve of intensity distribution of pixels. In te latter case as a dedicated circuit is obtained above. To speed up calculations in te calculation of te tresold scan was carried out not on all points of te image, but wit some step, te tresold in tis case as canged sligtly. 0 Figure 9. Te image after processing filter Figure 7. Grap te curve distribution density tresold Te abscissa sows te intensity of pixels of te minimum (black) to maximum (wite) in te percentage content. As a result we get a preview for te subsequent vectorization, an example can be considered for te resulting data: III. AUTOMATIC GENERATION OF VECTOR DATA FROM RASTER After preparing te raster image using a tresold filter, te resulting image is converted into vector data and scanned image of vertical stripes of widt k pixels. In eac line consisting of black pixels marked te beginning and of line. An indication of te beginning m of pixels taken in a begin orizontal layer of eigt to 1 pixel, a sign of te - m of wite pixels in te layer eigt n. In te simplest case, te image is scanned stripe in two pixels, mbegin 1, m 2, n 1. Te obtained coordinates of start and of te vertical segment are te output of te program, according to wic te corresponding image. As an example, te common language postscript. Similarly, te orizontal scan using te same parameters are allocated orizontal segments of images. Example of tracing te vertical and orizontal scan is sown in Figure 8. Improve te quality of te image allows oblique scan. In te simplest case of selected slope in /4, it s especially useful on a rectangular grid of pixels. Eac layer consists of a

5 k pixel widt of te form - Pi k, j, were 0 k k k te scanning angle /4 and te pixel type Pi k, j k of scan angle /4. Te coice of te ortogonal grid defined object - ouses and city buildings in aerial potograps are generally rectangular in sape. IV. EXPERIMENTAL RESULTS Experience wit te proposed converter confirms its speed and quality. Te disadvantage of te converter include difficulty arising from te recognition of small objects round. To enance te quality will increase te frequency of scanning angles, te optimization of parameters k, m, m, n begin. Anoter way to improve te quality - use multitresold filter, wic is constructed pat is not only black, but gray field images. For more accuracy work can be applied multitresold filter, but experience sows tat in tis case, firstly, te coice of tresolds is difficult, and secondly te result of increased noise and te traditional Sobel filter is preferable, despite its low speed. After processing te input image filters was analyzed two outcomes: Vectorization of raster images witout a filter Vectorization of raster images wit te filter Figure 10. Vectorization of raster images witout a filter To convert a raster imagery into vector data used te following system: TABLE I. THE USED SYSTEM Type of system components Model CPU Intel Core i7 960 GPU Nvidia GeForce 470GTX RAM 16 GB Cipset ix58 Figure 11. Vectorization of raster images wit te filter Time for processing images using tis system ave been spent 7 seconds. Te size of te processed raster image was 1060 pixels orizontally and 800 pixels vertically. Bot experiments were carried out using te tool ArcGis (ESRI). Te results were as follows: After tracing te input data image using a tresold filter, it is possible to build a 3D scene wit a ligt raster and vector data: Figure 12. 3D model of te map

6 V. CONCLUSION Tis paper describes a system for converting bitmap imagery into vector data wit te construction of 3D maps. Te system consists of several parts: preparation of satellite images using te filter Edge Detection, conversion of te received bitmap image into vector data, te construction of 3D maps. Tis system sowed ig performance and accuracy in comparison wit oter metods of processing satellite data. Te proposed metod as drawbacks: te restrictions on te identification of 3D models, recognition of only rectilinear objects. Te plan for te future work is to improve te current system taking into account all tese drawbacks. Create a parallel rering data on te GPU using te tecnology Cuda. Tis metod will greatly improve system performance. VI. REFERENCES [1] Jianbo Si and Jitra Malik. Normalized Cuts and Image Segmentation, IEEE Transactions on pattern analysis and macine intelligence, pp , Vol. 22, No. 8, 2000 [2] Erick López-Ornelas. Hig Resolution Images: Segmenting, Extracting Information and GIS Integration, World Academy of Science, Engineering and Tecnology, 2009 [3] Jane, H. Scarr, and S. Korkel. Principles of filter design. In Handbook of Computer Vision and Applications. Academic Press, 1999.

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