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Good afternoon. Thank you for your nice introduction. Before I’ll start my presentation, I want to introduce myself. I’m

an Assistant Professor at Computer Science Department of Karabuk University-Turkey. Currently, I’m working on Post

doctoral research at 3D GIS research Lab at UTM.//////// So, why we need the automatic generation of 3D models?

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In a GIS project, data collection is very time consuming and expensive task; however, it still consumes up to fifty

percent of the available resources. In order to reduce the cost on data collection, data generation from existing archives

and plans has been widely applied. By using scanning method, analogue format data from the archives can be

transformed into digital raster format data.

Automatically extracting geometric models of a building is difficult and the nodes and links have to be created

manually or half-manually (Pu and Zlatanova, 2005)

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In this study, Multidirectional Scanning for Line Extraction, MUSCLE Model was used to automatically

generate 3D model of buildings from raster plans. MUSCLE Model developed by us to vectorize the straight lines

through the raster images. The algorithm of the model generates the line thinning and the simple neighborhood

techniques for vectorization processes. Unlike traditional vectorization process, this model generates straight lines based

on a line thinning algorithm, without performing line following-chain coding and vector reduction stages.

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Architectural plans of the buildings are generally either drawn on

a paper as blueprints or stored in vector format. Blueprints can be

stored in raster format after scanned by a scanner. Whether floor

plan information is in raster or vector format, or even drawn on a

form by a user, MUSCLE Model can be applied in any case. The

key point is that images of the floor plan should be pictured on

the form of the user interface. Then the models are generated

automatically by analyzing and processing these images.

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You see the stages of the MUSCLE Model algorithm.

(Göstererek)After the threshold process, the lines are obtained

by scanning the image. Then, wrongly lines corrections,

generation building model and creating geo-database are

performed.

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By the first stage of algorithm, threshold process applied to the raster data. In the threshold process, threshold value is

to be determined and every pixel that is darker than this level is assigned black, while every lighter pixel is assigned

white. Therefore, the greyscale image was converted into a binary image.

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In the next stage, the raster image is scanned horizontally and

vertically pixel by pixel. Nearly vertical lines are obtained by

scanning the images horizontally, while nearly horizontal lines

are obtained by scanning the images vertically.

What do I mean nearly vertical and nearly horizontal?

(Göstererek)According to Figure A, If a line pass through the

region one and central O, it is nearly vertical. If pass through the

region two and central O, it is nearly horizontal. Figure B and C

indicates sample drawings for nearly vertical and nearly

horizontal lines, respectively.

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(Göstererek)This is a nearly vertical line, and this is a nearly

horizontal line in an floor plan. If we zoom in them, we can see

them like these. In the first step of scanning, the raster dataset is

scanned horizontally to determine the thickness of the lines and

the position of the pixel, which is located in the mid-point of the

lines. (Göstererek)The red pixels! The distribution of the red

pixels indicates that they have continuity for nearly vertical lines

but they have discontinuity for nearly horizontal lines.

In the second step of scanning, the raster dataset is scanned

vertically, and then, the same process is carried out for all

columns. This time, the red pixels have continuity for nearly

horizontal lines, but they have discontinuity for nearly vertical

lines.

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This situation is an advantage. Because, after the horizontal

scanning process, if neighborhood analysis is carried out for red

pixels, nearly vertical lines can be obtained. ////////// Nearly horizontal

lines cannot be obtained, because they have no continuity.

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Like this, after the vertical scanning, nearly horizontal lines can

be obtained.

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After, they are combined and consequently all lines obtained.

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In a case where two or more consecutive lines are nearly horizontal or nearly vertical, this process generates wrongly

vectorized lines. For example, three consecutive nearly horizontal lines were horizontally scanned as displayed in above

figure. (Slaytta göstererek)AB, BC, and CD. Due to discontinuity of the red pixels between intersection points A, B, C,

and D, the neighborhood analysis cannot be performed and vectorized data cannot be generated. As seen below figure,

when the raster image was vertically scanned during the second step, the neighborhood analysis generated wrong

vectorization results because of continuity of the red pixels. The algorithm recognizes point A as the beginning point of

the line, skips points B and C, and ends the line at point D.

The detection of wrongly vectorized data is performed by comparing the red pixels with the vectorized lines. The red

pixels and the vectorized line have to be based on the same linear equation. If they don’t, diagonal scanning procedure

is performed.

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As seen in Figure A and B, wrongly vectorized lines was diagonally scanned. (Göstererek)Under forty-five degree

angle, first, from left to right, and then, from right to left. In diagonal scanning process, if there were two consecutive

red pixels along the direction of scanning, the second red pixel is eliminated (Göster). Thus, vectorized line took a

discontinuous form as shown in Figure C. After applying the neighborhood analysis, this time it is possible to extract

the correct lines as indicated in Figure D. Then, corrected vector data was generated by combining both of the

vectorized lines together. Figure E.

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So, how do we generate the 3D building model? (Göstererek)By

using MUSCLE model, firstly, lines and intersection points are

obtained and generated the vector data of floor plan. Based on

user defined data, such as floor numbers and heights, 3D

Building Model is generated after designing each floor

automatically by assigning different elevation values to floor

plan.

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Next step is Geo-database./// After generating Building model, the geo-database is

created, and building model is transferred to this geo-database to be stores. In database,

building Model contains two different tables: points and lines. (Göstererek)Points table

indicates all the intersection points coordinates, while lines table contains the lines which are

generated by connecting the intersection points.

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Programı çalıştır

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As conclusions, In this study, MUSCLE Model was developed and used to automatically extract 3D Building

Models from architectural plans. The algorithm of the model generates the line thinning and the simple neighborhood

techniques for vectorization processes. Unlike traditional vectorization process, this model generates straight lines, based

on a line thinning algorithm without performing line following, chain coding and vector reduction stages. The results

indicate that the model successfully generate the models of the buildings and creates a 3D geo-database.

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The outcome of this research could be utilized for future 3D GIS applications, such as 3D network analysis and

visualization.

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DETAYLARLA İLGİLİ SORU GELİRSE:

The detection of wrongly vectorized data is performed by comparing the red pixels with the vectorized lines. The red

pixels and the vectorized line have to be based on the same linear equation. For example, if vectorized line is a line

with the beginning point of A and the ending point of B, then, the linear equation for this vectorized line can be formed

as you see in the slide.

When X coordinate of a red pixel is inserted into the linear Equation, and if the difference between the Y value

derived from this equation and the Y coordinate of this pixel is greater than a user defined maximum deviation, the

model defines this line as a wrongly vectorized line.

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As seen in Figure A and B, wrongly vectorized lines was diagonally scanned. (Göstererek)Under forty-five degree

angle, first, from left to right, and then, from right to left. In diagonal scanning process, if there were two consecutive

red pixels along the direction of scanning, the second red pixel is eliminated (Göster). Thus, vectorized line took a

discontinuous form as shown in Figure C. After applying the neighborhood analysis, the lines failed to have the

acceptable number of pixels were not vectorized. The continuous pixels, determined by implementing diagonal scanning

from both directions, were vectorized as indicated in Figure D. Then, corrected vector data was generated by combining

both of the vectorized lines together. Figure E.