Continuous dimensionality characterization of image structures

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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

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Citation Formats

M. Felsberg, S. Kalkan, and N. Kruger, “Continuous dimensionality characterization of image structures,” IMAGE AND VISION COMPUTING, pp. 628–636, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36631.