Journal ArticleDOI
The design and use of steerable filters
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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. >read more
Citations
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Journal ArticleDOI
Vineyard identification and description of spatial crop structure by per-field frequency analysis
TL;DR: In this paper, a simple crop geometry model based on general knowledge and field observations was applied to the Fourier power spectrum of aerial colour imagery obtained over the La Peyne valley (Herault, France).
Journal ArticleDOI
Complex Wavelet Bases, Steerability, and the Marr-Like Pyramid
D. Van De Ville,Michael Unser +1 more
TL;DR: The aim in this paper is to tighten the link between wavelets, some classical image-processing operators, and David Marr's theory of early vision with a new complex wavelet basis that behaves like a smoothed version of the Gradient-Laplace operator.
Journal ArticleDOI
Automatic Craniomaxillofacial Landmark Digitization via Segmentation-Guided Partially-Joint Regression Forest Model and Multiscale Statistical Features
TL;DR: A segmentation-guided partially-joint regression forest model to automatically digitize craniomaxillofacial landmarks efficiently and accurately from cone-beam computed tomography (CBCT) images, by addressing the challenge caused by large morphological variations across patients and image artifacts of CBCT images.
Journal ArticleDOI
Contrast-Independent Curvilinear Structure Detection in Biomedical Images
TL;DR: The proposed phase congruency tensor (PCT) is shown to be largely insensitive to intensity variations along the curve and provides successful detection within noisy regions of curvilinear structures.
Book ChapterDOI
Wavelets in Medical Image Processing: Denoising, Segmentation, and Registration
TL;DR: Wavelet transforms and other multi-scale analysis functions used for compact signal and image representations in de-noising, compression and feature detection processing problems for about twenty years have proven that space-frequency and spacescale expansions with this family of analysis functions provided a very efficient framework for signal or image data.
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.
Journal ArticleDOI
Methods of Theoretical Physics. By P.M. Morse and H. Feschbach. 2vols., Pp.xxii, 1978. 120s. each vol. 1953.(McGraw-Hill)
Book
Methods of Mathematical Physics
Richard Courant,David Hilbert +1 more
TL;DR: In this paper, the authors present an algebraic extension of LINEAR TRANSFORMATIONS and QUADRATIC FORMS, and apply it to EIGEN-VARIATIONS.