Open Access
The GET Operator
Michael Felsberg
- 01 Jan 2004
TL;DR: In this paper, the gradient energy tensor (GET) is defined based on Gaussian derivatives up to third order using different scales, which can be used to estimate the local amplitude, the local phase, and the local orientation of an image.
read more
Abstract: In this paper we propose a new operator which combines advantages of monogenic scale-space and Gaussian scale-space, of the monogenic signal and the structure tensor. The gradient energy tensor (GET) defined in this paper is based on Gaussian derivatives up to third order using different scales. These filters are commonly available, separable, and have an optimal uncertainty. The response of this new operator can be used like the monogenic signal to estimate the local amplitude, the local phase, and the local orientation of an image, but it also allows to measure the coherence of image regions as in the case of the structure tensor.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Low Level Operations and Learning in Computer Vision
Björn Johansson
- 01 Jan 2004
TL;DR: This thesis presents some concepts and methods for low level computer vision and learning, with object recognition as the primary application, and an efficient method for detection of local rotational symmetries in images is presented.
Statistical shape model to 3D ultrasound registration for spine interventions using enhanced local phase features.
Ilker Hacihaliloglu,Abtin Rasoulian,Robert Rohling,Purang Abolmaesumi +3 more
- 22 Sep 2013
TL;DR: This paper proposes a robust and accurate registration method that matches local phase bone features extracted from ultrasound images to a statistical shape model, which achieves significant improvement in registration accuracy compared to state of the art local phase image processing methods.
20
Analysis of Multiple Orientations
Matthias Mühlich,Til Aach +1 more
TL;DR: This work develops a unifying mathematical model for multiple orientations that allows the unified treatment of additively and occludingly superimposed oriented structures as well as of combinations of these.
Semi-automated segmentation of neuroblastoma nuclei using the gradient energy tensor: a user driven approach
Florian Kromp,Sabine Taschner-Mandl,Magdalena Schwarz,Johanna Blaha,Tamara Weiss,Peter F. Ambros,Michael Reiter +6 more
- 14 Feb 2015
TL;DR: A user-driven method for the segmentation of neuroblastoma nuclei in microscopic fluorescence images involving the gradient energy tensor is proposed, designed for clinicians and biologists who may have only limited image processing background.
References
A Combined Corner and Edge Detector
Chris Harris,Mike Stephens +1 more
- 01 Jan 1988
TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.
The structure of images
TL;DR: It is shown that any image can be embedded in a one-parameter family of derived images (with resolution as the parameter) in essentially only one unique way if the constraint that no spurious detail should be generated when the resolution is diminished, is applied.
Multidimensional orientation estimation with applications to texture analysis and optical flow
TL;DR: The theory is developed for the case when orientation computations are necessary at all local neighborhoods of the n-dimensional Euclidean space and a certainty measure, based on the error of the fit, is proposed.
619
•Proceedings Article
Optimal Orientation Detection of Linear Symmetry
Josef Bigun
- 01 Jan 1987
TL;DR: The problem of optimal detection of orientation in arbitrary neighborhoods is solved in the least squares sense and it is shown that this corresponds to fitting an axis in the Fourier domain of the n-dimensional neighborhood, the solution of which is a well known solution of a matrix eigenvalue problem.
584