Discrete Representation of Spatial Objects in Computer VisionSpringer Science & Business Media, 31 ene 1998 - 216 páginas One of the most natural representations for modelling spatial objects in computers is discrete representations in the form of a 2D square raster and a 3D cubic grid, since these are naturally obtained by segmenting sensor images. However, the main difficulty is that discrete representations are only approximations of the original objects, and can only be as accurate as the cell size allows. If digitisation is done by real sensor devices, then there is the additional difficulty of sensor distortion. To overcome this, digital shape features must be used that abstract from the inaccuracies of digital representation. In order to ensure the correspondence of continuous and digital features, it is necessary to relate shape features of the underlying continuous objects and to determine the necessary resolution of the digital representation. This volume gives an overview and a classification of the actual approaches to describe the relation between continuous and discrete shape features that are based on digital geometric concepts of discrete structures. Audience: This book will be of interest to researchers and graduate students whose work involves computer vision, image processing, knowledge representation or representation of spatial objects. |
Índice
II | 1 |
III | 8 |
IV | 12 |
V | 15 |
VI | 17 |
VII | 22 |
VIII | 24 |
IX | 33 |
XXXIX | 105 |
XL | 107 |
XLI | 108 |
XLII | 109 |
XLIII | 112 |
XLIV | 117 |
XLV | 118 |
XLVI | 119 |
X | 37 |
XII | 41 |
XIII | 45 |
XIV | 46 |
XV | 49 |
XVI | 50 |
XVII | 55 |
XVIII | 57 |
XIX | 59 |
XX | 66 |
XXI | 67 |
XXII | 73 |
XXIV | 75 |
XXV | 77 |
XXVI | 80 |
XXVII | 81 |
XXVIII | 83 |
XXIX | 85 |
XXX | 86 |
XXXI | 89 |
XXXII | 90 |
XXXIII | 93 |
XXXV | 96 |
XXXVI | 101 |
XXXVII | 102 |
XXXVIII | 104 |
XLVII | 122 |
XLVIII | 125 |
XLIX | 128 |
L | 130 |
LI | 132 |
LII | 134 |
LIII | 136 |
LIV | 137 |
LV | 144 |
LVI | 147 |
LVII | 149 |
LVIII | 150 |
LIX | 162 |
LX | 166 |
LXI | 171 |
LXII | 175 |
LXIII | 188 |
LXIV | 191 |
LXV | 192 |
LXVI | 193 |
LXVII | 196 |
LXVIII | 199 |
LXIX | 203 |
214 | |
Otras ediciones - Ver todo
Discrete Representation of Spatial Objects in Computer Vision L.J. Latecki Vista previa restringida - 2013 |
Discrete Representation of Spatial Objects in Computer Vision L.J. Latecki No hay ninguna vista previa disponible - 2010 |
Discrete Representation of Spatial Objects in Computer Vision L.J. Latecki No hay ninguna vista previa disponible - 2014 |
Términos y frases comunes
4-simple 8-adjacency 8-component adjacency relations arc(x assume bdCA(X binary black points boundary point CA(X camera closed half-plane color computer vision concept connected components connectedness contained continuous analog continuous functions convex set cubes defined definition deformation retract deleted denote Dig(A digital image digital line digital object digital picture digital set digital topology digitization process discrete representations endpoints Euler characteristic example exists geometric graph structure homeomorphic interior point iob(x IR² IR³ Jordan curve theorem Latecki Lemma line of support line segment metric continuous multicolor obtain oob(x osculating ball Par(A par(r)-regular set pixel planar plane polygonal arc preserves Proof property CP3 Proposition real objects Rosenfeld rotations s₁ Section semi-proximity spaces shown in Figure simple closed curve simple point simple polygon sp-continuous spatial straight line subarc subset supported arc thinning algorithm topological spaces topology well-composed pictures well-composed sets white points
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