Explicit schemes in seismic migration and isotropic multiscale representations

Jain, Saurabh; Papadakis, Manos; Dussaud, E.

doi:10.1090/conm/464/09084

Cited by 5 publications

(6 citation statements)

References 9 publications

(15 reference statements)

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“…For different anatomical regions of the face the regularity of those ramps varies spatially providing patterns that can be characteristic of those regions. To capture those local patterns of variation in the spatial configuration of the regularity of I, we use a representation using three filters that arise as analysis filters from an Isotropic Multiresolution Analysis (IMRA) [17], [18]…”

Section: B Multi-resolution Isotropic Filtersmentioning

confidence: 99%

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Facial component-landmark detection

Efraty

Papadakis

Profitt

et al. 2011

Face and Gesture 2011

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Landmark detection has proven to be a very challenging task in biometrics. In this paper, we address the task of facial component-landmark detection. By "component" we refer to a rectangular subregion of the face, containing an anatomical component (e.g., "eye"). We present a fullyautomated system for facial component-landmark detection based on multi-resolution isotropic analysis and adaptive bagof-words descriptors incorporated into a cascade of boosted classifiers. Specifically, first each component-landmark detector is applied independently and then the information obtained is used to make inferences for the localization of multiple components. The advantage of our approach is that it has robustness to pose as well as illumination. Our method has a failure rate lower than that of commercial software. Additionally, we demonstrate that using our method for the initialization of a point landmark detector results in performance comparable with that of stateof-the-art methods. All of our experiments are carried out using data from a publicly available database.

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Section: B Multi-resolution Isotropic Filtersmentioning

confidence: 99%

“…The corresponding synthesis filters are of the same kind but with different cut-off frequencies to facilitate exact reconstruction [18]. Features are generated by means of the representation T given by T (I) = (T 1 (I),…”

Section: B Multi-resolution Isotropic Filtersmentioning

confidence: 99%

Facial component-landmark detection

Efraty

Papadakis

Profitt

et al. 2011

Face and Gesture 2011

Self Cite

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“…We only give the statement here. For a complete proof of this result the reader is referred to [46]. …”

Section: Observe That This Impliesmentioning

confidence: 99%

“…in seismic imaging [46], then it is legitimate to question whether X φψ forms a Parseval frame of L 2 (R d ). This problem is the focus of the present section.…”

Section: Extension Principles Revisitedmentioning

confidence: 99%

The geometry and the analytic properties of isotropic multiresolution analysis

et al. 2008

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ABSTRACT. In this paper we investigate Isotropic Multiresolution Analysis (IMRA), isotropic refinable functions, and wavelets. The main results are the characterization of IMRAs in terms of the Lax-Wiener Theorem, and the characterization of isotropic refinable functions in terms of the support of their Fourier transform. As an immediate consequence of these results, there are no compactly supported (in the space domain) isotropic refinable functions in many dimensions. Next we study the approximation properties of IMRAs. Finally, we discuss the application of IMRA wavelets to 2D and 3D-texture segmentation in natural and biomedical images.

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“…The computational implementation of this rule is done by approximating ρ α (k) = ρ, T αk φ by ρ, T 2 −j 0 k φ , where k ∈ Z 3 , by taking j 0 > 0 to be high enough so that points 2 −j0 k and αk are sufficiently close. This computation is performed by iteratively applying j 0 -steps of the reconstruction algorithm of the Fast IMRA-transform [4,9] on the data set {ρ α (k)} k∈Z 3 . This algorithm uses the low-pass synthesis filter h 0 whose transfer function is given by:…”

Section: A Monoscale 3d-rigid Motion Invariant Texture Distancementioning

confidence: 99%

3D-rigid motion invariant discrimination and classification of 3D-textures

et al. 2011

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In this paper we implement a method for the 3D-rigid motion invariant texture discrimination and binary classification for discrete 3D-textures that are spatially homogeneous by modelling them as stationary Gaussian random fields. We use a novel 'distance' between 3D-textures that remains invariant under all 3D-rigid motions of the texture to develop rules for 3D-rigid motion invariant texture discrimination and binary classification of textures.

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Explicit schemes in seismic migration and isotropic multiscale representations

Cited by 5 publications

References 9 publications

Facial component-landmark detection

Facial component-landmark detection

The geometry and the analytic properties of isotropic multiresolution analysis

3D-rigid motion invariant discrimination and classification of 3D-textures

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