Multiresolution parametric estimation of transparent motions

Auvray, Vincent; Bouthémy, Patrick; Liénard, Jean‐Sylvain

doi:10.1109/icip.2005.1529707

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…One nevertheless has to note that many other sophisticated image transitions cannot be dealt with using this version of our algorithm even with more elaborate predictors. For example, transitions involving complex motion effects such as transparent motion (e.g., [2], [24]) require one to keep track of the displacement of y's significance, S(c), much more adaptively. We point to such cases and the above outlined items as avenues for future work.…”

Section: Discussionmentioning

confidence: 99%

Spatial Sparsity-Induced Prediction (SIP) for Images and Video: A Simple Way to Reject Structured Interference

Hua

Guleryuz

2011

IEEE Trans. on Image Process.

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We propose a prediction technique that is geared toward forming successful estimates of a signal based on a correlated anchor signal that is contaminated with complex interference. The corruption in the anchor signal involves intensity modulations, linear distortions, structured interference, clutter, and noise just to name a few. The proposed setup reflects nontrivial prediction scenarios involving images and video frames where statistically related data is rendered ineffective for traditional methods due to cross-fades, blends, clutter, brightness variations, focus changes, and other complex transitions. Rather than trying to solve a difficult estimation problem involving nonstationary signal statistics, we obtain simple predictors in linear transform domain where the underlying signals are assumed to be sparse. We show that these simple predictors achieve surprisingly good performance and seamlessly allow successful predictions even under complicated cases. None of the interference parameters are estimated as our algorithm provides completely blind and automated operation. We provide a general formulation that allows for nonlinearities in the prediction loop and we consider prediction optimal decompositions. Beyond an extensive set of results over many different types of complex transitions, the proposed method is also implemented to operate inside a state-of-the-art compression codec and results show significant improvements on scenes that are difficult to encode using traditional prediction techniques.

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Section: Discussionmentioning

confidence: 99%

Spatial Sparsity-Induced Prediction (SIP) for Images and Video: A Simple Way to Reject Structured Interference

Hua

Guleryuz

2011

IEEE Trans. on Image Process.

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“…A multi-resolution non linear least squares estimation is performed, using a Levenberg-Marquardt algorithm. Recently, Auvray et al proposed in [26] an algorithm based on equation (7). The method is also multi-resolution, uses a simplex algorithm for its initialization and adds a postprocessing step, especially efficient when the two velocities are "close".…”

Section: Related Work On Multiple Motion Estimation For Transparementioning

confidence: 99%

Index of Terms

Squiers¹

2014

An Introduction to the Social and Political Philosophy of Bertolt Brecht

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Most optical flow algorithms provide flow fields as single valued functions of the image sequence domains. Only a very few of them attempt to recover multiple motion vectors at given location, which is necessary when some transparent layers are moving independently. In this report we introduce a novel framework for modeling multivalued motion fields, and propose an energy minimization formulation with smoothing terms and terms implementing velocity model competition. We illustrate the capabilities of this approach on synthetic and real sequences.

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“…Tasks in the image analysis cannot be easily switched off, since that would lead to an incomplete or unacceptable result. The presented flow graph is based on a cascade of stages which are individually described in [5,6,7,8]. Summarizing, our application contains two parallel branches of different nature and the complete infrastructure should operate in real time.…”

Section: Medical Imaging Applicationmentioning

confidence: 99%