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iv Elenco delle Figure A.10 Bounding boxes registration and fusion . . . . . . . . . . . . . . . 72 A.11 Flow of symmetry processing. . . . . . . . . . . . . . . . . . . . . 74 A.12 Symmetry computation steps . . . . . . . . . . . . . . . . . . . . . 75 A.13 Symmetry axis validation . . . . . . . . . . . . . . . . . . . . . . . 76 A.14 The bounding boxes expansion . . . . . . . . . . . . . . . . . . . . 77 A.15 Snake edge energy . . . . . . . . . . . . . . . . . . . . . . . . . . 79 A.16 Snake intensity energy . . . . . . . . . . . . . . . . . . . . . . . . 80 A.17 Snake resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 A.18 Snake: examples of shape extraction . . . . . . . . . . . . . . . . . 84 A.19 Shape Neural network . . . . . . . . . . . . . . . . . . . . . . . . . 85 A.20 Shape Neural network results . . . . . . . . . . . . . . . . . . . . . 86 A.21 A three-layer feed-forward neural network . . . . . . . . . . . . . . 88 A.22 Neural network accuracy on the validation set . . . . . . . . . . . . 89 A.23 Neural network accuracy on the test set . . . . . . . . . . . . . . . 90 A.24 Probabilistic model generation . . . . . . . . . . . . . . . . . . . . 91 A.25 Probabilistic model set . . . . . . . . . . . . . . . . . . . . . . . . 92 A.26 Probabilistic model: foreground enhancement and background removal 93 A.27 Probabilistic model results . . . . . . . . . . . . . . . . . . . . . . 94 A.28 Head detection by pattern matching . . . . . . . . . . . . . . . . . 95 A.29 Head detection by pattern matching: models . . . . . . . . . . . . . 96 A.30 Head model by probabilistic matching. . . . . . . . . . . . . . . . . 96 A.31 Head model by probabilistic matching: model . . . . . . . . . . . . 97 A.32 Head matching by warm area matching . . . . . . . . . . . . . . . 98 A.33 Probabilistic model results . . . . . . . . . . . . . . . . . . . . . . 99 A.34 Symmetry analysis results . . . . . . . . . . . . . . . . . . . . . . 100 A.35 Active contour results . . . . . . . . . . . . . . . . . . . . . . . . . 101 A.36 Neural network results . . . . . . . . . . . . . . . . . . . . . . . . 101 A.37 Head detector results . . . . . . . . . . . . . . . . . . . . . . . . . 102
Elenco delle tabelle 1.1 Numero di confronti tra finestre di correlazione nei test effettuati . . 28 1.2 Tempo di elaborazione: intervallo costante . . . . . . . . . . . . . . 29 1.3 Tempo di elaborazione: intervallo di ricerca basato sul terreno . . . 30 1.4 Tempo di elaborazione: le implementazioni SIMD . . . . . . . . . . 31 1.5 Tempo di elaborazione: immagine di disparità sparsa . . . . . . . . 32 1.6 Tempo di elaborazione: multi-threading . . . . . . . . . . . . . . . 33
Page 1: UNIVERSITÀ DEGLI STUDI DI PARMA DI
Page 5: Alla mia famiglia e ai miei amici
Page 8 and 9: ii Sommario 2.4 La disparità e la
Page 13 and 14: Introduzione Ogni giorno le strade
Page 15: Introduzione 3 Attività di ricerca
Page 18 and 19: 6 Capitolo 1. Algoritmi di visione
Page 47 and 48: Capitolo 2 Ricostruzione 3D su imma
Page 49 and 50: 2.1. Inverse Perspective Mapping 37
Page 51 and 52: 2.2. La disparità e la ricostruzio
Page 57 and 58: 2.3. Cylindric Inverse Perspective
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2.4. La disparità e la ricostruzio
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56 Conclusioni ricerca e finestre m
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58 Appendice A. A stereo based pede
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Bibliografia [1] Care - european ro
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Bibliografia 107 [16] K. C. Frerste
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Bibliografia 109 [35] Massimo Berto
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