Continuous dimensionality characterization of image structures

Download

index.pdf

Date

2009-05-04

Author

Felsberg, Michael
Kalkan, Sinan
Kruger, Norbert

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

237
views

113
downloads

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

Suggestions

OpenMETU
Core

Nested local symmetry set Tarı, Zehra Sibel (Elsevier BV, 2000-08-01) A local-symmetry-based representation for shapes in arbitrary dimensions and a method for its computation are presented. The method depends on analyzing the Hessian of a specific boundaryness function, v, which is computed as the minimizer of an energy functional. The method is basically a generalized ridge finding scheme in which the ridges are defined in terms of the orbit of the gradient vector del v under the action of the Hessian of v. Once the ridges are determined, the local extrema of the magnitude ...
Phase-space window and degrees of freedom of optical systems with multiple apertures Ozaktas, Haldun M.; Öktem, Sevinç Figen (The Optical Society, 2013-04-01) We show how to explicitly determine the space-frequency window (phase-space window) for optical systems consisting of an arbitrary sequence of lenses and apertures separated by arbitrary lengths of free space. If the space-frequency support of a signal lies completely within this window, the signal passes without information loss. When it does not, the parts that lie within the window pass and the parts that lie outside of the window are blocked, a result that is valid to a good degree of approximation for ...
Data-driven image captioning via salient region discovery Kilickaya, Mert; Akkuş, Burak Kerim; Çakıcı, Ruket; Erdem, Aykut; Erdem, Erkut; İKİZLER CİNBİŞ, NAZLI (Institution of Engineering and Technology (IET), 2017-09-01) n the past few years, automatically generating descriptions for images has attracted a lot of attention in computer vision and natural language processing research. Among the existing approaches, data-driven methods have been proven to be highly effective. These methods compare the given image against a large set of training images to determine a set of relevant images, then generate a description using the associated captions. In this study, the authors propose to integrate an object-based semantic image r...
Information permeability for stereo matching Cigla, Cevahir; Alatan, Abdullah Aydın (Elsevier BV, 2013-10-01) A novel local stereo matching algorithm is introduced to address the fundamental challenge of stereo algorithms, accuracy and computational complexity dilemma. The time consuming intensity dependent aggregation procedure of local methods is improved in terms of both speed and precision. Providing connected 2D support regions, the proposed approach exploits a new paradigm, namely separable successive weighted summation (SWS) among horizontal and vertical directions enabling constant operational complexity. T...
Low-level multiscale image segmentation and a benchmark for its evaluation Akbaş, Emre (Elsevier BV, 2020-10-01) In this paper, we present a segmentation algorithm to detect low-level structure present in images. The algorithm is designed to partition a given image into regions, corresponding to image structures, regardless of their shapes, sizes, and levels of interior homogeneity. We model a region as a connected set of pixels that is surrounded by ramp edge discontinuities where the magnitude of these discontinuities is large compared to the variation inside the region. Each region is associated with a scale that d...

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.