Discrete Representation of Spatial Objects in Computer VisionSpringer Science & Business Media, 2013 M04 17 - 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. |
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Página 4
... pixels ) as the discrete representations . The extraction of object properties can be viewed as a mapping from representations in categories 1 and 2 to a symbolic representation in form of predicates , which belongs to category 4 ...
... pixels ) as the discrete representations . The extraction of object properties can be viewed as a mapping from representations in categories 1 and 2 to a symbolic representation in form of predicates , which belongs to category 4 ...
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... object : Two digital representations A and B of a spatial 1The adjectives " discrete " and " digital " are used as equivalent concepts here . object may not be equal on the pixel ( or 5 Embedding Approach Continuous Analog.
... object : Two digital representations A and B of a spatial 1The adjectives " discrete " and " digital " are used as equivalent concepts here . object may not be equal on the pixel ( or 5 Embedding Approach Continuous Analog.
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... pixel ( or voxel ) distortions are less influential . This can be done by relating shape features of the underlying continuous object to the corresponding features obtained from a digital representation of this object . The relation ...
... pixel ( or voxel ) distortions are less influential . This can be done by relating shape features of the underlying continuous object to the corresponding features obtained from a digital representation of this object . The relation ...
Página 11
... the plane . They also combine the concept of well- composed pictures with parameter - dependent components , such that each connected com- ponent of a well - composed picture may contain pixels 1.1 . GRAPH - BASED APPROACH 11.
... the plane . They also combine the concept of well- composed pictures with parameter - dependent components , such that each connected com- ponent of a well - composed picture may contain pixels 1.1 . GRAPH - BASED APPROACH 11.
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L.J. Latecki. ponent of a well - composed picture may contain pixels of different gray values . 1.2 Axiomatic Approach A common axiomatic theory that describes both continuous and discrete structures is developed . Certain sets or set ...
L.J. Latecki. ponent of a well - composed picture may contain pixels of different gray values . 1.2 Axiomatic Approach A common axiomatic theory that describes both continuous and discrete structures is developed . Certain sets or set ...
Contenido
5 | |
Graphbased Approach | 45 |
Axiomatic Approach | 73 |
Continuous Representations of Real Objects | 107 |
A co co co | 142 |
Digitization Approach | 147 |
Bibliography | 175 |
37 | 190 |
45 | 202 |
Otras ediciones - Ver todas
Discrete Representation of Spatial Objects in Computer Vision L.J. Latecki Vista previa limitada - 1998 |
Discrete Representation of Spatial Objects in Computer Vision L.J. Latecki Sin vista previa disponible - 2010 |
Discrete Representation of Spatial Objects in Computer Vision L.J. Latecki Sin vista previa disponible - 2014 |
Términos y frases comunes
4-simple 8-adjacency 8-component arc(x 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 IR² Jordan curve theorem Latecki Lemma line of support line segment metric continuous multicolor N₁(P obtain oob(x osculating ball Par(A par(r)-regular set parallel regular 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