Dense Motion Estimation for Smoke

Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.

[1]  Gilbert Strang,et al.  The Discrete Cosine Transform , 1999, SIAM Rev..

[2]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[3]  Michael J. Black,et al.  A Naturalistic Open Source Movie for Optical Flow Evaluation , 2012, ECCV.

[4]  Brian Wyvill,et al.  Robust iso-surface tracking for interactive character skinning , 2014, ACM Trans. Graph..

[5]  Peter Hall,et al.  Learning similarity metrics for dynamic scene segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Hidetomo Sakaino.  Fluid motion estimation method based on physical properties of waves , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Luiz A. T. Machado,et al.  Forecast and Tracking the Evolution of Cloud Clusters (ForTraCC) Using Satellite Infrared Imagery: Methodology and Validation , 2008 .

[8]  Jitendra Malik,et al.  Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Patrick Pérez,et al.  Dense Estimation of Fluid Flows , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Frédo Durand,et al.  Refraction Wiggles for Measuring Fluid Depth and Velocity from Video , 2014, ECCV.

[11]  Patrick Pérez,et al.  Estimating fluid optical flow , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[12]  Robert M. Rabin,et al.  Multiscale storm identification and forecast , 2003 .

[13]  J. Fehrenbach,et al.  Identification of velocity fields for geophysical fluids from a sequence of images , 2011 .

[14]  T. Corpetti,et al.  Fluid experimental flow estimation based on an optical-flow scheme , 2006 .

[15]  Jingyi Yu,et al.  Reconstructing Gas Flows Using Light-Path Approximation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Rikio Onai,et al.  Extracting fluid from a video for efficient post-production , 2012, DigiPro '12.

[17]  Adrian G. Bors,et al.  Robust Processing of Optical Flow of Fluids , 2010, IEEE Transactions on Image Processing.

[18]  Thomas Brox,et al.  High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.

[19]  C. Beaulieu.  Fluid motion recovery by coupling dense and parametric vector fields , 1999 .

[20]  Narendra Ahuja,et al.  Extracting a fluid dynamic texture and the background from video , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Cordelia Schmid,et al.  EpicFlow: Edge-preserving interpolation of correspondences for optical flow , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Yasuyuki Matsushita,et al.  Motion detail preserving optical flow estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[24]  Feng Li,et al.  Dynamic fluid surface acquisition using a camera array , 2011, 2011 International Conference on Computer Vision.

[25]  Damien Garcia,et al.  Robust smoothing of gridded data in one and higher dimensions with missing values , 2010, Comput. Stat. Data Anal..

[26]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..

[27]  Wolfgang Heidrich,et al.  From capture to simulation , 2014, ACM Trans. Graph..

[28]  Richard Szeliski,et al.  A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[29]  Bernard Monnier,et al.  Rain clouds tracking with radar image processing based on morphological skeleton matching , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[30]  Feng Li,et al.  Recovering fluid-type motions using Navier-Stokes potential flow , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  Matti Pietikäinen,et al.  Automatic Dynamic Texture Segmentation Using Local Descriptors and Optical Flow , 2013, IEEE Transactions on Image Processing.

[32]  Thomas Brox,et al.  FlowNet: Learning Optical Flow with Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[33]  Michal Haindl,et al.  Unsupervised Dynamic Textures Segmentation , 2013, CAIP.

[34]  J. Steinhoff,et al.  Modification of the Euler equations for ‘‘vorticity confinement’’: Application to the computation of interacting vortex rings , 1994 .

[35]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[36]  Michael J. Black,et al.  Secrets of optical flow estimation and their principles , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[37]  Yasuyuki Matsushita,et al.  Motion detail preserving optical flow estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[38]  Yi Liu,et al.  A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations , 2012, Environ. Model. Softw..

[39]  Nuno Vasconcelos,et al.  Variational layered dynamic textures , 2009, CVPR.

[40]  Lakshman Anumolu,et al.  Simulation of Smoke in OpenFOAM Framework , 2012 .

[41]  Philip J. Willis,et al.  Water Surface Modeling from a Single Viewpoint Video , 2013, IEEE Transactions on Visualization and Computer Graphics.

[42]  A. N. Strahler.  Quantitative analysis of watershed geomorphology , 1957 .