Fast Optical Flow Estimation Based on the Split Bregman Method

Chen, Jun; Cai, Zemin; Lai, Jianhuang; Xie, Xiaohua

doi:10.1109/tcsvt.2016.2615324

Cited by 23 publications

(21 citation statements)

References 45 publications

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“…One typical approach is to consider it as a labeling problem as mentioned in Section 1. The motion field can be estimated by solving an energy minimization problem based on brightness constancy and spatial smoothness [13][14][15]. Since the space of all possible labels is usually too large or even infinite [16,17], some strategies have been proposed to reduce the label set.…”

Section: Motion Estimationmentioning

confidence: 99%

Homography-guided stereo matching for wide-baseline image interpolation

et al. 2021

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Image interpolation has a wide range of applications such as frame rate-up conversion and free viewpoint TV. Despite significant progresses, it remains an open challenge especially for image pairs with large displacements. In this paper, we first propose a novel optimization algorithm for motion estimation, which combines the advantages of both global optimization and a local parametric transformation model. We perform optimization over dynamic label sets, which are modified after each iteration using the prior of piecewise consistency to avoid local minima. Then we apply it to an image interpolation framework including occlusion handling and intermediate image interpolation. We validate the performance of our algorithm experimentally, and show that our approach achieves state-of-the-art performance.

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Section: Motion Estimationmentioning

confidence: 99%

Homography-guided stereo matching for wide-baseline image interpolation

et al. 2021

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“…Optical flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer and a scene [ 18 , 19 ]. The American psychologist Gibson introduced the concept of optical flow in the 1940s [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Improved Seam-Line Searching Algorithm for UAV Image Mosaic with Optical Flow

Guo

Liao

et al. 2018

Sensors

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Ghosting and seams are two major challenges in creating unmanned aerial vehicle (UAV) image mosaic. In response to these problems, this paper proposes an improved method for UAV image seam-line searching. First, an image matching algorithm is used to extract and match the features of adjacent images, so that they can be transformed into the same coordinate system. Then, the gray scale difference, the gradient minimum, and the optical flow value of pixels in adjacent image overlapped area in a neighborhood are calculated, which can be applied to creating an energy function for seam-line searching. Based on that, an improved dynamic programming algorithm is proposed to search the optimal seam-lines to complete the UAV image mosaic. This algorithm adopts a more adaptive energy aggregation and traversal strategy, which can find a more ideal splicing path for adjacent UAV images and avoid the ground objects better. The experimental results show that the proposed method can effectively solve the problems of ghosting and seams in the panoramic UAV images.

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“…In this paper, in order to take into account the image structure information better in the overlapped area of the adjacent images, we use the optical flow value of pixels in the overlapped area as a constraint condition for constructing the energy function based on graph cuts. The Horn-Schunck method of estimating optical flow is a global method [27,28]. This paper calculates the optical flow fields of experimental UAV remote sensing images based on this method.…”

Section: Optical Flow Fieldmentioning

confidence: 99%

“…Figure 1 is a schematic of the calculated optical flow field. Optical flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer and scene [27,28]. Optical flow fields are dense correspondence fields for accurate displacement optical flow estimation, they contain rich three-dimensional structural information of feature objects in images.…”

Section: Optical Flow Fieldmentioning

confidence: 99%

Automatic Seam-Line Detection in UAV Remote Sensing Image Mosaicking by Use of Graph Cuts

Guo

et al. 2018

IJGI

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Image mosaicking is one of the key technologies in data processing in the field of computer vision and digital photogrammetry. For the existing problems of seam, pixel aliasing, and ghosting in mosaic images, this paper proposes and implements an optimal seam-line search method based on graph cuts for unmanned aerial vehicle (UAV) remote sensing image mosaicking. This paper first uses a mature and accurate image matching method to register the pre-mosaicked UAV images, and then it marks the source of each pixel in the overlapped area of adjacent images and calculates the energy value contributed by the marker by using the target energy function of graph cuts constructed in this paper. Finally, the optimal seam-line can be obtained by solving the minimum value of target energy function based on graph cuts. The experimental results show that our method can realize seamless UAV image mosaicking, and the image mosaic area transitions naturally.

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Fast Optical Flow Estimation Based on the Split Bregman Method

Cited by 23 publications

References 45 publications

Homography-guided stereo matching for wide-baseline image interpolation

Homography-guided stereo matching for wide-baseline image interpolation

Improved Seam-Line Searching Algorithm for UAV Image Mosaic with Optical Flow

Automatic Seam-Line Detection in UAV Remote Sensing Image Mosaicking by Use of Graph Cuts

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