Multiscale optical flow computation from the monogenic signal

Alessandrini, Martino; Bernard, Olivier; Basarab, Adrian; Liebgott, Hervé

doi:10.1016/j.irbm.2012.12.015

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…The three elements can be computed via convolutions by quadrature filters, yielding a linear feature vector, [11], [26], [33], tuned to a fixed scale. The resulting phase does not coincide with the FT phase if a dominant direction cannot be computed, [1]. By contrast, phase interpretation provided by narrow bandwidth Gabor filters coincides with that of the FT phase even if the input signal is not linearly symmetric.…”

Section: Introductionmentioning

confidence: 86%

Analytic Signal Phase in $N-D$ by Linear Symmetry Tensor--fingerprint modeling

Bigün¹,

Alonso‐Fernandez²

2020

Preprint

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We reveal that the Analytic Signal phase, and its gradient have a hitherto unstudied discontinuity in 2-D and higher dimensions. The shortcoming can result in severe artifacts whereas the problem does not exist in 1-D signals. Direct use of Gabor phase, or its gradient, in computer vision and biometric recognition e.g., as done in influential studies [12,36], may produce undesired results that will go unnoticed unless special images similar to ours reveal them. Instead of the Analytic Signal phase, we suggest the use of Linear Symmetry phase, relying on more than one set of Gabor filters, but with a negligible computational add-on, as a remedy. Gradient magnitudes of this phase are continuous in contrast to that of the analytic signal whereas continuity of the gradient direction of the phase is guaranteed if Linear Symmetry Tensor replaces gradient vector. The suggested phase has also a built-in automatic scale estimator, useful for robust detection of patterns by multi-scale processing. We show crucial concepts on synthesized fingerprint images, where ground truth regarding instantaneous frequency, (scale & direction), and phase are known with favorable results. A comparison to a baseline alternative is also reported. To that end, a novel multi-scale minutia model where location, direction, and scale of minutia parameters are steerable, without creation of uncontrollable minutia is also presented. This is a useful tool, to reduce development times of minutia detection methods with explainable behavior. A revealed consequence is that minutia directions are not determined by the linear phase alone, but also by each other and the influence must be corrected to obtain steerability and accurate ground truths. Essential conclusions are readily transferable to N -D, and unrelated applications, e.g. optical flow or disparity estimation in stereo. * The paper is best read when viewed on a screen with zoom or when printed in color in 1200 dpi resolution due to fine details and color.

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Section: Introductionmentioning

confidence: 86%

Analytic Signal Phase in $N-D$ by Linear Symmetry Tensor--fingerprint modeling

Bigün¹,

Alonso‐Fernandez²

2020

Preprint

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“…First, the extraction of orientation maps using the monogenic signal is not done in a pixelwise way. Instead, a more robust approach based on the maximization of the directional Hilbert transform response averaged over a local neighborhood is used [12,13]. In [12] it is shown that the solution of this optimization is equivalent to the pixelwise result if the local neighborhood considered reduces to one pixel.…”

Section: Proposed Flow Estimation Methodsmentioning

confidence: 99%

Monogenic orientation-based blood flow estimation in high-frequency ultrasound imaging

Basarab

Vray

Kouamé

2012

2012 IEEE International Ultrasonics Symposium

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Classical blood flow estimation techniques are based on the Doppler effect, but suffer from several limitations: inadequate estimations of low velocities, limited spatial resolution and impossibility to estimate flows perpendicular to the beam axis. In a recent work, the velocity was related to the texture orientation in spatiotemporal planes extracted from an ultrasound 2D+t volume. This texture orientation was previously estimated using a bank of orientated Gabor filters. This represents one of the major drawbacks of this method, as the accuracy of the estimates is directly linked to the angular step between two consecutive filters. We propose a novel velocity estimation method, based on the same assumption that a moving target leaves a trace in the spatio-temporal plane, generating an orientation of the texture related to its velocity. In our approach, we propose to estimate this orientation using the framework of the monogenic signal and thus to avoid the large amount of Gabor filters classically used.

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“…In addition to speckle tracking algorithms, which track motion by some measure of similarity like cross Brandon Rebholz bpr5072@psu.edu 1 School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, USA correlation, there are other methods of estimating motion in ultrasound. Optical flow methods [6,7] are one of these alternatives for speckle tracking. Optical flow methods estimate displacement by a function of the gradients in either the spatial or temporal domain; however, the results are inaccurate when the displacement is large relative to the resolution of the image.…”

Section: Introductionmentioning

confidence: 99%

Two-dimensional iterative projection method for subsample speckle tracking of ultrasound images

Rebholz

Zheng

Almekkawy

2020

Med Biol Eng Comput

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Speckle tracking provides robust motion estimation necessary to create accurate post-processed images. These methods are known to be less accurate in the lateral dimension compared with the axial dimension due to the limitations on the lateral resolution of ultrasound scanning. This paper proposes a two-dimensional iterative projection (TDIP) algorithm using the Riesz transform to generate the analytic signals. The TDIP is an improvement to an already accurate speckle tracking algorithm called the phase coupled (PC) method. The PC method projects the intersection of gradients on the correlation map to the zero phase contour to estimate displacement. The TDIP method performs iterative projections and uses the aggregate of these projected locations to estimate the motion, in addition to rejecting inaccurate projections by checking them against the aggregate projection location. The TDIP additionally adopts the Riesz transform to generate two-dimensional analytic signals to improve lateral accuracy. The Riesz transform is a multidimensional extension of the Hilbert transform into the nD Euclidean space and therefore can be used to include data in both axial and lateral dimensions as opposed to the commonly used Hilbert transform which is one dimensional. The accuracy of the TDIP is quantitatively proven on simulated datasets from the Field II simulation program and on experimental data from two flow phantoms. At all cases, the TDIP is more accurate than the PC algorithm at two-dimensional displacement estimation.

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Multiscale optical flow computation from the monogenic signal

Cited by 11 publications

References 12 publications

Analytic Signal Phase in $N-D$ by Linear Symmetry Tensor--fingerprint modeling

Analytic Signal Phase in $N-D$ by Linear Symmetry Tensor--fingerprint modeling

Monogenic orientation-based blood flow estimation in high-frequency ultrasound imaging

Two-dimensional iterative projection method for subsample speckle tracking of ultrasound images

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