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Journal ArticleDOI

The design and use of steerable filters

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TLDR
The authors present an efficient architecture to synthesize filters of arbitrary orientations from linear combinations of basis filters, allowing one to adaptively steer a filter to any orientation, and to determine analytically the filter output as a function of orientation.
Abstract
The authors present an efficient architecture to synthesize filters of arbitrary orientations from linear combinations of basis filters, allowing one to adaptively steer a filter to any orientation, and to determine analytically the filter output as a function of orientation. Steerable filters may be designed in quadrature pairs to allow adaptive control over phase as well as orientation. The authors show how to design and steer the filters and present examples of their use in the analysis of orientation and phase, angularly adaptive filtering, edge detection, and shape from shading. One can also build a self-similar steerable pyramid representation. The same concepts can be generalized to the design of 3-D steerable filters. >

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Citations
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Journal ArticleDOI

Affine adaptive filtering of CT data.

TL;DR: A novel method for resampling and enhancing image data using multidimensional adaptive filters is presented and clearly shows an improvement over conventional resampled techniques such as cubic spline interpolation and sinc interpolation.
Journal ArticleDOI

A Feature-based Approach for Dense Segmentation and Estimation of Large Disparity Motion

TL;DR: A novel framework for motion segmentation that combines the concepts of layer-based methods and feature-based motion estimation is presented, and a dense, piecewise smooth assignment of pixels to motion layers is achieved using a fast approximate graphcut algorithm based on a Markov random field formulation.
Journal ArticleDOI

Understanding Image Representations by Measuring Their Equivariance and Equivalence

TL;DR: This work investigates two key mathematical properties of representations: equivariance and equivalence and identifies several predictors of geometric and architectural compatibility, including the spatial resolution of the representation and the complexity and depth of the models.
Journal ArticleDOI

Augmented Lagrangian based reconstruction of non-uniformly sub-Nyquist sampled MRI data

TL;DR: A new imaginary value suppressing prior is introduced, which attenuates imaginary components of MRI images during reconstruction, resulting in a better overall image quality and is applicable not only to sub-Nyquist sampled k-space reconstruction, but also to MR image fusion and/or resolution enhancement.
Book ChapterDOI

A new sharpness measure based on gaussian lines and edges

TL;DR: This work measures the sharpness of natural (complex) images using Gaussian models and proposes the 5 th percentile of the sigmas or the fraction of line/edge pixels with a sigma smaller than 1.
References
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Journal ArticleDOI

A Computational Approach to Edge Detection

TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Journal ArticleDOI

A theory for multiresolution signal decomposition: the wavelet representation

TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Book

Methods of Mathematical Physics

TL;DR: In this paper, the authors present an algebraic extension of LINEAR TRANSFORMATIONS and QUADRATIC FORMS, and apply it to EIGEN-VARIATIONS.