TV-L1 Optical Flow Estimation with Image Details Recovering Based on Modified Census Transform

Mohamed, Mostafa; Mertsching, Bärbel

doi:10.1007/978-3-642-33179-4_46

Cited by 12 publications

(8 citation statements)

References 16 publications

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“…Otherwise, the linearisation becomes invalid and the resulting data term would not be suitable for a typical variational optic flow framework [14]. Assuming small flow components u and v as well as a small change of the directional derivative ∂ eϕ f (x) in time, we propose a twofold linearisation of the regularised version of constraint (4). For this purpose, let ∇ 3 := (∂ x , ∂ y , ∂ t ) denote the spatio-temporal gradient and…”

Section: Census-based Constancy Assumptionmentioning

confidence: 99%

“…A hash table-based indexing scheme provides flow estimates in realtime and is well-suited for large displacements. Müller et al [3] as well as Mohamed and Mertsching [4] exploit these sparse feature matches to handle large displacements and to recover image details lost in a coarse-to-fine minimisation technique, respectively. Furthermore, Müller et al [5] embed the census transform as a data term into a variational optic flow framework.…”

Section: Introductionmentioning

confidence: 99%

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Why Is the Census Transform Good for Robust Optic Flow Computation?

Hafner

Demetz

Weickert

2013

Lecture Notes in Computer Science

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Abstract. The census transform is becoming increasingly popular in the context of optic flow computation in image sequences. Since it is invariant under monotonically increasing grey value transformations, it forms the basis of an illumination-robust constancy assumption. However, its underlying mathematical concepts have not been studied so far. The goal of our paper is to provide this missing theoretical foundation. We study the continuous limit of the inherently discrete census transform and embed it into a variational setting. Our analysis shows two surprising results: The census-based technique enforces matchings of extrema, and it induces an anisotropy in the data term by acting along level lines. Last but not least, we establish links to the widely-used gradient constancy assumption and present experiments that confirm our findings.

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Section: Census-based Constancy Assumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Why Is the Census Transform Good for Robust Optic Flow Computation?

Hafner

Demetz

Weickert

2013

Lecture Notes in Computer Science

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“…Census transform [283] recently regained interest and was promoted in [226] for optical flow estimation [108,118,179,182,183,205,252]. The Census signature is a bit string reflecting relative value of pixels of a patch with respect to the center pixel.…”

Section: Patch-based Similarity Measuresmentioning

confidence: 99%

Optical flow modeling and computation: A survey

Fortun

Bouthémy

Kervrann

2015

Computer Vision and Image Understanding

363

182

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a b s t r a c tOptical flow estimation is one of the oldest and still most active research domains in computer vision. In 35 years, many methodological concepts have been introduced and have progressively improved performances, while opening the way to new challenges. In the last decade, the growing interest in evaluation benchmarks has stimulated a great amount of work. In this paper, we propose a survey of optical flow estimation classifying the main principles elaborated during this evolution, with a particular concern given to recent developments. It is conceived as a tutorial organizing in a comprehensive framework current approaches and practices. We give insights on the motivations, interests and limitations of modeling and optimization techniques, and we highlight similarities between methods to allow for a clear understanding of their behavior.

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“…This type of approach has become increasingly popular in image matching in recent years. Examples include the rank transform and the census transform [47,22,10,11], and more recently the complete rank transform [7]. While both robust and invariant data terms have been shown to give very good results in a wide array of applications, they induce a fixed measure of variation that does not directly model variation in the data.…”

Section: Introductionmentioning

confidence: 99%

Most Likely Separation of Intensity and Warping Effects in Image Registration

Kühnel¹,

Sommer²,

Pai³

et al. 2017

SIAM J. Imaging Sci.

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This paper introduces a class of mixed-effects models for joint modeling of spatially correlated intensity variation and warping variation in 2D images. Spatially correlated intensity variation and warp variation are modeled as random effects, resulting in a nonlinear mixed-effects model that enables simultaneous estimation of template and model parameters by optimization of the likelihood function. We propose an algorithm for fitting the model which alternates estimation of variance parameters and image registration. This approach avoids the potential estimation bias in the template estimate that arises when treating registration as a preprocessing step. We apply the model to datasets of facial images and 2D brain magnetic resonance images to illustrate the simultaneous estimation and prediction of intensity and warp effects.keywords template estimation, image registration, separation of phase and intensity variation, nonlinear mixed-effects model *

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TV-L1 Optical Flow Estimation with Image Details Recovering Based on Modified Census Transform

Cited by 12 publications

References 16 publications

Why Is the Census Transform Good for Robust Optic Flow Computation?

Why Is the Census Transform Good for Robust Optic Flow Computation?

Optical flow modeling and computation: A survey

Most Likely Separation of Intensity and Warping Effects in Image Registration

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