Discrete Representation of Spatial Objects in Computer by Longin Jan Latecki (auth.)

By Longin Jan Latecki (auth.)

One of the main traditional representations for modelling spatial items in pcs is discrete representations within the kind of a 2nd sq. raster and a 3D cubic grid, on account that those are clearly received by way of segmenting sensor photographs. in spite of the fact that, the most hassle is that discrete representations are just approximations of the unique gadgets, and will simply be as actual because the mobilephone measurement permits. If digitisation is finished by way of actual sensor units, then there's the extra hassle of sensor distortion. to beat this, electronic form positive factors has to be used that summary from the inaccuracies of electronic illustration. as a way to make sure the correspondence of constant and electronic good points, it is vital to narrate form positive factors of the underlying non-stop items and to figure out the required answer of the electronic illustration.
This quantity offers an summary and a category of the particular methods to explain the relation among non-stop and discrete form beneficial properties which are in response to electronic geometric options of discrete buildings.
Audience: This ebook can be of curiosity to researchers and graduate scholars whose paintings includes computing device imaginative and prescient, photo processing, wisdom illustration or illustration of spatial objects.

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Parametric models such as superquadrics and generalized cylinders. Digital representations allow us to develop domain independent, universal geometric shape features of spatial objects that can be used as parts of image search engines. The structural non-parametric shape descriptions of digital representations can be based on digital geometry. Digital geometry is a theory of shape properties of objects in digital images, and therefore, it provides a suitable basis for domain-independent shape models as it describes geometric shape properties on the pixel level.

To infer this, it is enough for the robot to identify that its starting and goal locations do not belong to the same connected component. This can be done by a connected component labeling algorithm, presupposing that the discrete representation of the fence separates the robot's digital imagery into two components. To infer this, it is necessary that the Jordan curve theorem holds in the robot's digital imagery if the pixels representing the fence form a simple closed curve. Note that the robot need not have explicit knowledge about the Jordan curve theorem.

A set S is arc-connected if for every pair of points p, q E S, there is an arc joining p and q contained in S. A closed arc-connected subset of lR2 will be sometimes called a figure. Let P be an arc which is the image of the closed interval I <;;; lR by the homeomorphism f : I -t lRn , and let J be a closed subinterval of I. The image f{J) will be called a subarc of P. Note that the restriction of f to J is a homeomorphism: J -t lR2 ; hence a subarc of an arc is an arc. We now state some propositions which are useful in the proofs of some theorems in this book.

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