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Continuous dimensionality characterization of image structures
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Date
2009-05-04
Author
Felsberg, Michael
Kalkan, Sinan
Kruger, Norbert
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Intrinsic dimensionality is a concept introduced by statistics and later used in image processing to measure the dimensionality of a data set. In this paper, we introduce a continuous representation of the intrinsic dimension of an image patch in terms of its local spectrum or, equivalently, its gradient field. By making use of a cone structure and barycentric co-ordinates, we can associate three confidences to the three different ideal cases of intrinsic dimensions corresponding to homogeneous image patches, edge-like structures and junctions. The main novelty of our approach is the representation of confidences as prior probabilities which can be used within a probabilistic framework. To show the potential of our continuous representation, we highlight applications in various contexts such as image structure classification, feature detection and localisation, visual scene statistics and optic flow evaluation.
Subject Keywords
Computer Vision and Pattern Recognition
URI
https://hdl.handle.net/11511/36631
Journal
IMAGE AND VISION COMPUTING
DOI
https://doi.org/10.1016/j.imavis.2008.06.018
Collections
Department of Computer Engineering, Article