Pixelwise-adaptive blind optical flow assuming nonstationary statistics

Foroosh, Hassan

doi:10.1109/tip.2004.840685

Cited by 6 publications

(5 citation statements)

References 74 publications

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“…Here only results from about 1994 are given. We have excluded the newly published results in [14] because the numbers seem not to be reasonable 2 . The best results sofar is from Brox et al [15] with less than 2 The velocity arrows in Figure 1c) in the article does not correspond to the average error of 0.2 degrees that is given in the Table I for comparison with other methods.…”

Section: Error Statisticsmentioning

confidence: 99%

A Study of the Yosemite Sequence Used as a Test Sequence for Estimation of Optical Flow

Austvoll

2005

Image Analysis

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Since the publication of the comparative study done by Barron et al. on optical flow estimation, a race was started to achieve more and more accurate and dense velocity fields. For comparison a few synthetic image sequences has been used. The most complex of these is the Yosemite Flying sequence that contains both a diverging field, occlusion and multiple motions at the horizon. About 10 years ago it was suggested to remove the sky region because the correct flow used in earlier work was not found to be the real ground truth for this region. In this paper we present a study of the sky region in this test sequence, and discuss its usefulness for evaluation of optical flow estimation.

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Section: Error Statisticsmentioning

confidence: 99%

A Study of the Yosemite Sequence Used as a Test Sequence for Estimation of Optical Flow

Austvoll

2005

Image Analysis

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“…There are various different ways that one could categorize image registration methods. In terms of functioning space, they could be either spatial domain [52]- [55] or transform domain methods [56]- [64]. On the other hand, in terms of their dependency on feature/point correspondences they may be categorized as either dependent [65]- [71] or independent [52]- [64] of feature/point correspondences.…”

Section: Introductionmentioning

confidence: 99%

“…In terms of functioning space, they could be either spatial domain [52]- [55] or transform domain methods [56]- [64]. On the other hand, in terms of their dependency on feature/point correspondences they may be categorized as either dependent [65]- [71] or independent [52]- [64] of feature/point correspondences. Finally, in terms of the complexity of the image transformation, they may be categorized as linear parametric (e.g.…”

Section: Introductionmentioning

confidence: 99%

In-band sub-pixel registration of wavelet-encoded images from sparse coefficients

Aydin

Foroosh

2017

SIViP

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Sub-pixel registration is a crucial step for applications such as super-resolution in remote sensing, motion compensation in magnetic resonance imaging, and non-destructive testing in manufacturing, to name a few. Recently, these technologies have been trending towards wavelet encoded imaging and sparse/compressive sensing. The former plays a crucial role in reducing imaging artifacts, while the latter significantly increases the acquisition speed. In view of these new emerging needs for applications of wavelet encoded imaging, we propose a sub-pixel registration method that can achieve direct wavelet domain registration from a sparse set of coefficients. We make the following contributions: (i) We devise a method of decoupling scale, rotation, and translation parameters in the Haar wavelet domain, (ii) We derive explicit mathematical expressions that define in-band sub-pixel registration in terms of wavelet coefficients, (iii) Using the derived expressions, we propose an approach to achieve in-band subpixel registration, avoiding back and forth transformations. (iv) Our solution remains highly accurate even when a sparse set of coefficients are used, which is due to localization of signals in a sparse set of wavelet coefficients. We demonstrate the accuracy of our method, and show that it outperforms the state-of-the-art on simulated and real data, even when the data is sparse. Index Terms Subpixel Registration Wavelet Decomposition Haar Wavelets Image PyramidsRecently, there has been a trend in various imaging modalities and applications such as non-destructive testing and Magnetic Resonance Imaging (MRI) to adopt wavelet-encoded imaging [81], [82] and sparse sensing [83]- [85], with the aim of achieving better resolution, reduced distortions, higher SNR, and quick acquisition time, which are crucial for these applications. Subpixel registration is an integral step of various applications involving these wavelet-encoded compressive imaging technologies. Therefore, in this paper, our goal is to obtain a wavelet domain sub-pixel registration method that can achieve highly accurate results from a sparse set of wavelet coefficients. We make the following major contributions towards this goal: (i) We devise a method of decoupling scale, rotation, and translation parameters in the Haar wavelet domain, (ii) We derive explicit mathematical expressions that define in-band sub-pixel registration in terms of Haar wavelet coefficients, (iii) Using the derived expressions, we propose a multiscale approach to achieve in-band sub-pixel registration, avoiding back and forth transformations. (iv) Our solution remains highly accurate even when a sparse set of coefficients are used, due to signal energy localization in a sparse set of wavelet coefficients. Extensive experiments are used to validate our method both on simulated and real data under various scenarios. II. RELATED WORKThe earliest methods related to our work are based on image pyramids, with the aim of reducing computational time and avoiding local extrema. Examples ...

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“…Reliable motion estimation/compensation can substantially reduce the residual energy in the coding of video data. Motion estimation methods are either global [6], [20]- [22], [24]- [27], [30], [31], [60]- [62], [64], [65], [118], [119], or local [57]- [59], [110] in their nature in terms of treating the transformation relating two images. There is also a separate but related body of work on camera motion quantification, which requires online or offline calibration of camera [9], [28], [40]- [42], [42]- [44], [46], [50]- [52], [63], [72], [73], [80], [81], [88]- [91], [96].…”

Section: Introductionmentioning

confidence: 99%

Motion-Compensated Temporal Filtering for Critically-Sampled Wavelet-Encoded Images

Aydin,

Foroosh

2017

Preprint

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We propose a novel motion estimation/compensation (ME/MC) method for wavelet-based (in-band) motion compensated temporal filtering (MCTF), with application to low-bitrate video coding. Unlike the conventional in-band MCTF algorithms, which require redundancy to overcome the shift-variance problem of critically sampled (complete) discrete wavelet transforms (DWT), we perform ME/MC steps directly on DWT coefficients by avoiding the need of shift-invariance. We omit upsampling, the inverse-DWT (IDWT), and the calculation of redundant DWT coefficients, while achieving arbitrary subpixel accuracy without interpolation, and high video quality even at very low-bitrates, by deriving the exact relationships between DWT subbands of input image sequences. Experimental results demonstrate the accuracy of the proposed method, confirming that our model for ME/MC effectively improves video coding quality.

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Pixelwise-adaptive blind optical flow assuming nonstationary statistics

Cited by 6 publications

References 74 publications

A Study of the Yosemite Sequence Used as a Test Sequence for Estimation of Optical Flow

A Study of the Yosemite Sequence Used as a Test Sequence for Estimation of Optical Flow

In-band sub-pixel registration of wavelet-encoded images from sparse coefficients

Motion-Compensated Temporal Filtering for Critically-Sampled Wavelet-Encoded Images

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