Two Applications of Graph-Cuts to Image Processing

Cooke, Tristrom

doi:10.1109/dicta.2008.32

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…Within any level of the segmentation hierarchy, first a sparse sampling of the possible flows is used, followed by a denser sampling with a reduced range around the solution from the previous iteration. The algorithm in [20] iteratively alternates between two steps. In the first step, all the states are allocated to the horizontal motion, which is estimated similarly to stereo, assuming the vertical motion is zero.…”

Section: Dynamically Reparameterizing Sparse State-spacesmentioning

confidence: 99%

“…Huber-L1 [82] X X X X X X DPOF [39] X X X X X X X TV-L1-improved [80] X X X X CBF [74] X X X X X Brox et al [54] X X X X X F-TV-L1 [79] X X X X Second-order prior [75] X X X Fusion [40] X X X X X X X Dynamic MRF [27] X X X Seg OF [83] X X X X X X Learning Flow [70] X X X X X X Filter Flow [65] X X X X X X X Graph Cuts [20] X X X X Black & Anandan [11] X X X SPSA-learn [41] X X X X X Horn & Schunck [33] X…”

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confidence: 99%

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A Database and Evaluation Methodology for Optical Flow

Baker

Scharstein

Lewis³

et al. 2007

2007 IEEE 11th International Conference on Computer Vision

1,008

1,434

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The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance. The challenges for optical flow algorithms today go beyond the datasets and evaluation methods proposed in that paper. Instead, they center on problems associated with complex natural scenes, including nonrigid motion, real sensor noise, and motion discontinuities. We propose a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms. To that end, we contribute four types of data to test different aspects of optical flow algorithms: (1) sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture, (2) realistic synthetic sequences, (3) high frame-rate video used to study interpolation error, and (4) modified stereo sequences of static scenes. In addition to the average angular error used by Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and results at motion discontinuities and in textureless regions. In October 2007, we published the performance of several well-known methods on a preliminary version of our data to establish the current state of the art. We also made the data freely available on the web at

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Section: Dynamically Reparameterizing Sparse State-spacesmentioning

confidence: 99%

mentioning

confidence: 99%

A Database and Evaluation Methodology for Optical Flow

Baker

Scharstein

Lewis³

et al. 2007

2007 IEEE 11th International Conference on Computer Vision

1,008

1,434

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“…For more details about existing move-spaces, see [249,255]. Applications for optical flow have increased rapidly in recent years [59,64,88,101,99,161,164,234,279].…”

Section: Combinatorial Methodsmentioning

confidence: 99%

Optical flow modeling and computation: A survey

Fortun

Bouthémy

Kervrann

2015

Computer Vision and Image Understanding

357

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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“…local algorithms, are also important here, since global algorithms always contain a local measurement as a core ingredient, on top of which global optimization is applied. However, while in many algorithms local and global computations can be separated (Cooke 2008;Felzenszwalb and Huttenlocher 2006), this is not always the case (Horn and Schunck 1981;Weickert and Schnörr 2001;Bruhn et al 2005;Papenberg et al 2006).…”

Section: Introductionmentioning

confidence: 99%

A Local Algorithm for the Computation of Image Velocity via Constructive Interference of Global Fourier Components

Dellen

Wörgötter

2010

Int J Comput Vis

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A novel Fourier-based technique for local motion detection from image sequences is proposed. In this method, the instantaneous velocities of local image points are inferred directly from the global 3D Fourier components of the image sequence. This is done by selecting those velocities for which the superposition of the corresponding Fourier gratings leads to constructive interference at the image point. Hence, image velocities can be assigned locally even though position is computed from the phases and amplitudes of global Fourier components (spanning the whole image sequence) that have been filtered based on the motionconstraint equation, reducing certain aperture effects typically arising from windowing in other methods. Regularization is introduced for sequences having smooth flow fields. Aperture effects and their effect on optic-flow regularization are investigated in this context. The algorithm is tested on both synthetic and real image sequences and the results are compared to those of other local methods. Finally, we show that other motion features, i.e. motion direction, can be computed using the same algorithmic framework without requiring an intermediate representation of local velocity, which is an important characteristic of the proposed method.

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Two Applications of Graph-Cuts to Image Processing

Cited by 11 publications

References 11 publications

A Database and Evaluation Methodology for Optical Flow

A Database and Evaluation Methodology for Optical Flow

Optical flow modeling and computation: A survey

A Local Algorithm for the Computation of Image Velocity via Constructive Interference of Global Fourier Components

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