Continuous dimensionality characterization of image structures
2009 (English)In: Image and Vision Computing, ISSN 0262-8856, Vol. 27, no 6, 628-636 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2009. Vol. 27, no 6, 628-636 p.
Intrinsic dimensionality; Feature extraction and classification
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-18087DOI: 10.1016/j.imavis.2008.06.018OAI: oai:DiVA.org:liu-18087DiVA: diva2:214515
Michael Felsberg, Sinan Kalkan and Norbert Krüger, Continuous dimensionality characterization of image structures, 2009, Image and Vision Computing, (27), 6, 628-636.
Copyright: Elsevier Science B.V., Amsterdam.