[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

Recommendations

Info

42 R. Bieda et al. a. b. Fig. 6. The visualization of the training set after the transformation: a. PCA b. FDL with the 98% mapping information
Recognition and Location of Objects in the Visual Field of a UAV Vision System 43 set to 98%. As a result, the analysis using the algorithms FLD and PCA the feature dimension space is reduced from 22 to 2. This suggests, that the values (features forming the feature vector) describing objects in particular classes were strongly correlated. As a result, the representation of classes using the feature vector was shown graphically on picture 6. The dimension of the vectors representing, in the transformation (7), where the transformation matrix is described by the following relation (42) (for 2). From the picture 6 analysis it is visible that a better representation, In terms of classifier construction, can be achieved by using the FLD algorithm. The classes of the training set objects are more concentrated around the average values than it is in the case of the PCA algorithm. This is also confirmed by the numerical analysis of the result of the transformation. To estimate this, both the degree of separation ( the distance between the mean vectors) of classes and the degree of aggregation of individual vectors in the given object classes were used. The value of the indicator is the greater when the concentration of individual vectors around the middle class vector is greater (small value of ), and the greater is the distance between the middle vectors of particular object classes (great value of ). In the analyzed case the indicator value confirms the observations from the analysis of the Fig 6. The value of the class indicator after the PCA transformation equals to 25.7, whereas to each class of a training set after the transformation with FLD equals to 20260. However, despite such impressive outcome it is easily noticeable ( pic.6b ) that in the dimension of the feature vector it is not possible to mark the hyperplane (for 2 straight line), which would clearly divide the class from other classes. Therefore, the Assumption that the feature vectors describing the four object classes in the space 22-dimensional are linearly inseparable. This fact explains the impossibility of Winding the hypeplane using the described perceptron algorithm as a linear classifier. 7 The Minimum-Distance Classifier As a result, to every class of the training set after the transformation with the FDL algorithm, a Simple minimum-distance classifier can be used. In this study the classification process is all about the finding the class , to which a given vector of feature (according to the similarity function ) is the most similar. The function, describing the ‘similarity’ of the feature vector to one class, for each separate class, was defined as follows: exp 1 2 (42)
Page 1 and 2:
Studies in Computational Intelligen
Page 3 and 4:
Aleksander Nawrat · Zygmunt Kuś E
Page 5 and 6:
“In God We Trust, All others we o
Page 7 and 8: VIII Preface valuable information i
Page 9 and 10: Contents Part I: Design of Object D
Page 11 and 12: Contents XIII Technology Developmen
Page 13 and 14: XVI List of Contributors Adam Czorn
Page 15 and 16: XVIII List of Contributors Henryk M
Page 17 and 18: XX List of Contributors Józef Wron
Page 19 and 20: 2 Design of Object Detection, Recog
Page 21 and 22: 4 A. Babiarz et al. 1 Introduction
Page 23 and 24: 6 A. Babiarz et al. The description
Page 25 and 26: 8 A. Babiarz et al. a) b) Fig. 5. T
Page 27 and 28: 10 A. Babiarz et al. o o o “micvo
Page 29 and 30: 12 A. Babiarz et al. a) b) Fig. 7.
Page 31 and 32: 14 A. Babiarz et al. • The camera
Page 33 and 34: 16 A. Babiarz et al. the camera ser
Page 35 and 36: 18 A. Babiarz et al. a) b) Fig. 11.
Page 37 and 38: 20 A. Babiarz et al. patrol point i
Page 39 and 40: 22 A. Babiarz et al. particular ang
Page 41 and 42: 24 A. Babiarz et al. The sample cod
Page 43 and 44: Recognition and Location of Objects
Page 57: Recognition and Location of Objects
Page 63 and 64: 48 P. Demski et al. Fig. 1. Automat
Page 65 and 66: 50 P. Demski et al. E-Grip firing m
Page 67 and 68: 52 P. Demski et al. 4 Automatic Tar
Page 69 and 70: 54 P. Demski et al. implementing su
Page 71 and 72: Object Tracking for Rapid Camera Mo
Page 91 and 92: Object Tracking in a Picture during
Page 107 and 108: 94 Construction of Image Acquisitio
Page 109 and 110:
96 G. Bieszczad et al. effectivenes
Page 111 and 112:
98 G. Bieszczad et al. such a way t
Page 113 and 114:
100 G. Bieszczad et al. Table 1. No
Page 115 and 116:
102 G. Bieszczad et al. Fig. 7. Pri
Page 117 and 118:
104 G. Bieszczad et al. Fig. 10. Re
Page 119 and 120:
106 G. Bieszczad et al. Fig. 13. Ti
Page 121 and 122:
108 G. Bieszczad et al. Fig. 16. Op
Page 123 and 124:
110 G. Bieszczad et al. has to make
Page 125 and 126:
112 G. Bieszczad et al. MTF functio
Page 127 and 128:
114 G. Bieszczad et al. [11] Krupi
Page 129 and 130:
116 D. Bereska et al. Presented in
Page 131 and 132:
118 D. Bereska et al. Fig. 2. Image
Page 133 and 134:
120 D. Bereska et al. Fig. 4. Ortho
Page 135 and 136:
Omnidirectional Video Acquisition D
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Part III Design of Vision Based Con
Page 151 and 152:
Vision System for Group of Mobile R
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
A Distributed Control Group of Mobi
Page 171 and 172:
Page 173 and 174:
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
178 D. Bereska et al. The aim of th
Page 191 and 192:
180 D. Bereska et al. 3 Control Alg
Page 193 and 194:
182 D. Bereska et al. Fig. 5. Schem
Page 195 and 196:
184 D. Bereska et al. 4.1 Mechanica
Page 197 and 198:
186 D. Bereska et al. 4.1.3 Open-Lo
Page 199 and 200:
188 D. Bereska et al. Fig. 16. Comp
Page 201 and 202:
Probabilistic Approach to Planning
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
Page 213 and 214:
Page 215 and 216:
206 Practical Applications of Class
Page 217 and 218:
208 M. Mellado and K. Skrzypczyk an
Page 219 and 220:
210 M. Mellado and K. Skrzypczyk a
Page 221 and 222:
212 M. Mellado and K. Skrzypczyk wh
Page 223 and 224:
214 M. Mellado and K. Skrzypczyk Fi
Page 225 and 226:
216 M. Mellado and K. Skrzypczyk 6
Page 227 and 228:
Prototyping the Autonomous Flight A
Page 229 and 230:
Page 231 and 232:
Page 233 and 234:
Page 235 and 236:
Page 237 and 238:
Page 239 and 240:
Page 241 and 242:
Feature Extraction and HMM-Based Cl
Page 243 and 244:
Page 245 and 246:
Page 247 and 248:
Page 249 and 250:
Page 251 and 252:
Page 253 and 254:
Page 255 and 256:
248 K. Daniec et al. In this articl
Page 257 and 258:
250 K. Daniec et al. Fig. 2. Applic
Page 259 and 260:
252 K. Daniec et al. by the ability
Page 261 and 262:
254 K. Daniec et al. The idea of us
Page 263 and 264:
Selection of Individual Gait Featur
Page 265 and 266:
Page 267 and 268:
Page 269 and 270:
Page 271 and 272:
Page 273 and 274:
Page 275 and 276:
Page 277 and 278:
Page 279 and 280:
274 A. Nawrat et al. alia weather c
Page 281 and 282:
276 A. Nawrat et al. have an embedd
Page 283 and 284:
278 A. Nawrat et al. particular cha
Page 285 and 286:
280 A. Nawrat et al. • 1-phase el
Page 287 and 288:
282 A. Nawrat et al. The phase cond
Page 289 and 290:
284 A. Nawrat et al. Table 1. Compa
Page 291 and 292:
286 A. Nawrat et al. P3 P3 P3 71101
Page 293 and 294:
288 A. Nawrat et al. S, VA 4,510 4,
Page 295 and 296:
290 A. Nawrat et al. Comparison dif
Page 297 and 298:
Technology Development of Military
Page 299 and 300:
Page 301 and 302:
Page 303 and 304:
Page 305 and 306:
Page 307 and 308:
Page 309 and 310:
Page 311 and 312:
Page 313 and 314:
Page 315 and 316:
312 A. Czornik, A. Nawrat, and M. N
Page 317 and 318:
Page 319 and 320:
Page 321 and 322:
Page 323 and 324:
Page 325 and 326:
Page 327 and 328:
Page 329 and 330:
Page 331 and 332:
328 M. Koźlak, A. Kurzeja, and A.
Page 333 and 334:
Page 335 and 336:
Page 337 and 338:
Page 339 and 340:
Page 341 and 342:
Page 343 and 344:
Page 345 and 346:
Page 347 and 348:
344 Conclusions Control algorithms
show all

[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

Create successful ePaper yourself

Delete template?

Save as template?