[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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148 A. Babiarz, R. Bieda, and K. Jaskot thus the equation 12 becomes which ends proof. 11 1 1 (15) 2.6 Flood Fill Algorithm Binarized image coming from the background subtraction algorithm contains measures like center of mass, bounding box, geometrical moments etc. can be also calculated, and what’s very important, calculating these measures can be done at very small cost. The algorithm used for the labeling purpose (one among others) is called Flood Fill. This algorithm can be described best by the following pseudo code. int currLabel = 0; stack s; for (int y = 1; y < HEIGHT-1; y+=SKIP) for (int x = 1; x < WIDTH-1; x+=SKIP) if (img.pixel(x, y) == 255) { currLabel++; s.push(x, y); while (!s.empty()) { int xx, yy; s.pop(xx, yy); if (img.pixel(xx, yy) == 255) { img.pixel(xx, yy) = currLabel; s.push(xx-1, yy-1); s.push(xx , yy-1); s.push(xx+1, yy-1); s.push(xx-1, yy ); s.push(xx , yy ); s.push(xx+1, yy ); s.push(xx+1, yy+1); s.push(xx , yy+1); s.push(xx+1, yy+1); } } } In the above implementation parameters WIDTH and HEIGHT are dimensions of the image img being processed, parameter SKIP is for simple optimization based on the observation that knowing the minimal size occupied on the raster by the objects to be labeled the algorithm does not need to start searching for them from every pixel, it is enough to start searching every n pixels, where n should be a bit
Vision System for Group of Mobile Robots 149 smaller than minimal width or height of objects of interest. The implementation presented above additionally presumes that input image consists of two values only: 0 for background and 255 for foreground objects. 2.7 Estimation of Object’s Position and Orientation Vision algorithm is capable of precise estimation of position of all robots and ball and orientation of robots only. (Quite naturally, orientation is not calculated for a ball). Robots could be interpreted as agents and the approach of planning could be used [9]. However , real time applications requires fast adaptation to changes. Therefore different approach was used. The method used here is described in more detail in [16]. Calculations are done in four stages. First, for each object, the following integrals are calculated: (16) (17) (18) Where is a region occupied by the object, is its area and and are static moments. Next, the position of each object is found from the formulas describing center of the mass: (19) Then the angle of main axis of inertia is calculated for each robot and finally, the direction of this axis is chosen, as the algorithm for finding the angle of main axis of inertia can’t do that automatically and additional heuristics is needed. These two stages are described in more detail in sections below. 2.8 Angle of Main Axis of Inertia Estimating the orientation of robot on the basis of main axis of inertia is possible thanks to the special shape of the color marker placed on the top of each robot. The shape is presented on Figure 6. (20)
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Studies in Computational Intelligen
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Aleksander Nawrat · Zygmunt Kuś E
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“In God We Trust, All others we o
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VIII Preface valuable information i
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Contents Part I: Design of Object D
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Contents XIII Technology Developmen
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XVI List of Contributors Adam Czorn
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XVIII List of Contributors Henryk M
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XX List of Contributors Józef Wron
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2 Design of Object Detection, Recog
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4 A. Babiarz et al. 1 Introduction
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6 A. Babiarz et al. The description
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8 A. Babiarz et al. a) b) Fig. 5. T
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10 A. Babiarz et al. o o o “micvo
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12 A. Babiarz et al. a) b) Fig. 7.
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14 A. Babiarz et al. • The camera
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16 A. Babiarz et al. the camera ser
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18 A. Babiarz et al. a) b) Fig. 11.
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20 A. Babiarz et al. patrol point i
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24 A. Babiarz et al. The sample cod
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Recognition and Location of Objects
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48 P. Demski et al. Fig. 1. Automat
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52 P. Demski et al. 4 Automatic Tar
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54 P. Demski et al. implementing su
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94 Construction of Image Acquisitio
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Probabilistic Approach to Planning
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Probabilistic Approach to Planning
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206 Practical Applications of Class
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208 M. Mellado and K. Skrzypczyk an
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Prototyping the Autonomous Flight A
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Feature Extraction and HMM-Based Cl
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248 K. Daniec et al. In this articl
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250 K. Daniec et al. Fig. 2. Applic
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252 K. Daniec et al. by the ability
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254 K. Daniec et al. The idea of us
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278 A. Nawrat et al. particular cha
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280 A. Nawrat et al. • 1-phase el
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282 A. Nawrat et al. The phase cond
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284 A. Nawrat et al. Table 1. Compa
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286 A. Nawrat et al. P3 P3 P3 71101
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288 A. Nawrat et al. S, VA 4,510 4,
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290 A. Nawrat et al. Comparison dif
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Technology Development of Military
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344 Conclusions Control algorithms
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[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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