Local Invariant Features for 3D Image Analysis: Dissertation - Image 1

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Südwestdeutscher Verlag für Hochschulschriften Local Invariant Features for 3D Image Analysis: Dissertation

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Description

In this thesis, we present a novel framework which provides generic methods for the automatic analysis of 3D volume data. We combine local invariant feature descriptors with learning techniques to infer mathematical models describing 3D objects (structures) in dense and cluttered data. Using annotated training examples, our overall framework is able to adapt to a wide range of different problems by learning local formations of shape and texture properties. Local feature descriptors play the key role in our concept. Due to the oftentimes high intra class variations and anisotropic nature of the data, we derive features that are invariant towards the most common data transformations, including rotation, gray-scale changes and for some applications also scaling and translation. Throughout this thesis, we provide as many as 14 different local 3D features: from general texture and shape features to very specific and highly specialized detectors

Product Specifications

Brand: Südwestdeutscher Verlag für Hochschulschriften
Format: paperback
ASIN: 3838110838
Category: Books > Subjects > Computing & Internet > Programming > Graphics & Multimedia
Domain: Amazon UK
Publication Date: 11 September 2009
Listed Since: 21 September 2009

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Südwestdeutscher Verlag für Hochschulschriften Local Invariant Features for 3D Image Analysis: Dissertation

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