Dense Motion Estimation for Smoke

Chen, Da; Li, Wenbin; Hall, Peter M.

doi:10.1007/978-3-319-54190-7_14

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…Also, minimizing a variational energy function with a full-scale dense initial flows as the input, can generate better results compared to the conventional coarse-to-fine scheme ( Brox et al, 2004 ; Sun, Roth & Black, 2014 ). This is applicable to a scene with boundary overlapping or thin objects ( Chen, Li & Hall, 2016 ; Revaud et al, 2015 ). These phenomena often occur in fluid motion scenes, as the diffusion of fluid may generate some thin fluid areas in the surroundings.…”

Section: Methodsmentioning

confidence: 99%

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Determining dense velocity fields for fluid images based on affine motion

Zhang,

Li,

Xiang

et al. 2024

PeerJ Computer Science

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In this article, we address the problem of estimating fluid flows between two adjacent images containing fluid and non-fluid objects. Typically, traditional optical flow estimation methods lack accuracy, because of the highly deformable nature of fluid, the lack of definitive features, and the motion differences between fluid and non-fluid objects. Our approach captures fluid motions using an affine motion model for each small patch of an image. To obtain robust patch matches, we propose a best-buddies similarity-based method to address the lack of definitive features but many similar features in fluid phenomena. A dense set of affine motion models was then obtained by performing nearest-neighbor interpolation. Finally, dense fluid flow was recovered by applying the affine transformation to each patch and was improved by minimizing a variational energy function. Our method was validated using different types of fluid images. Experimental results show that the proposed method achieves the best performance.

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Section: Methodsmentioning

confidence: 99%

“… Chen, Li & Hall (2016) proposed a very different approach, using skeletal matching to characterize smoke motion. This method is not a physical-base method, and it has a limit ability to estimate the flow of smoke, because it is hard to extract skeletons from fluid phenomena in general, such as seas and clouds.…”

Section: Related Workmentioning

confidence: 99%

Determining dense velocity fields for fluid images based on affine motion

Zhang,

Li,

Xiang

et al. 2024

PeerJ Computer Science

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show abstract

“…[37]. Numbers of extensive works have been proposed to conquer these challenges by introducing additional constraints and more advanced optimization procedure [38,39,40,41,42,43,44,45,46,47,48]. Brox et al [39] bring a gradient constancy assumption into the data term in order to reduce the dependency of BCC, and bring a discontinuity-preserving spatio-temporal smoothness constraint to deal with motion discontinuities.…”

Section: Optical Flowmentioning

confidence: 99%

Learn to model blurry motion via directional similarity and filtering

Chen

et al. 2018

Pattern Recognition

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It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects. In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. We first conduct a CNN architecture using a novel learnable directional filtering layer. Such layer encodes the angle and distance similarity matrix between blur and camera motion, which is able to enhance the blur features of the camera-shake footages. The proposed CNNs are then integrated into an iterative optical flow framework, which enable the capability of modeling and solving both the blind deconvolution and the optical flow estimation problems simultaneously. Our framework is trained end-to-end on a synthetic dataset and yields competitive precision and performance against the state-of-the-art approaches

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“…To automatically extract structural assets, correspondences e.g. optical flow [16][17][18][19][20][21][22][23][24], from any input CAD file to analysed files is introduced. Such correspondences give a hidden link from unknown visual elements to reference and further prorogate the actual properties back to the input CAD.…”

Section: Converting Floor Plan Cad Filesmentioning

confidence: 99%

Automatic structural scene digitalization

Tang¹,

Wang²,

Cosker³

et al. 2017

PLoS ONE

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In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate.

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Dense Motion Estimation for Smoke

Cited by 5 publications

References 43 publications

Determining dense velocity fields for fluid images based on affine motion

Determining dense velocity fields for fluid images based on affine motion

Learn to model blurry motion via directional similarity and filtering

Automatic structural scene digitalization

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