Continuous dimensionality characterization of image structures
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- 作者：Michael Felsberg ; Sinan Kalkan ; Norbert Krü ; ger
- 关键词：Intrinsic dimensionality ; Feature extraction and classification
- 刊名：Image and Vision Computing
- 出版年：2009
- 出版时间：4 May 2009
- 年：2009
- 卷：27
- 期：6
- 页码：628-636
- 全文大小：862 K
文摘
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.