Presentation is loading. Please wait.

Presentation is loading. Please wait.

Surface reconstruction of sea-ice through stereo - initial steps Rohith MV Gowri Somanath VIMS Lab.

Published byCarol Harvey Modified over 9 years ago

Similar presentations

Presentation on theme: "Surface reconstruction of sea-ice through stereo - initial steps Rohith MV Gowri Somanath VIMS Lab."— Presentation transcript:

1 Surface reconstruction of sea-ice through stereo - initial steps Rohith MV Gowri Somanath VIMS Lab

2 Sea ice IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

3 Overview Introduction Need for reconstruction Previous approaches Camera system and field trip Stereo on ice images Our algorithm Results Conclusion IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

4 Need for reconstruction “The feasibility of using snow surface roughness to infer ice thickness and ice bottom roughness is promising….” “…the goal of a circumpolar high resolution data set of Antarctic sea ice and snow thickness distributions has not yet been achieved …” “…crucial for future validation of satellite observations, climate models, and for assimilation into forecast models…” Ref: Workshop on Antarctic Sea Ice Thickness, 2006; Annals of Glaciology IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

5 Previous methods – LIDAR Echelmeyer, K.A., V.B. Valentine, and S.L. Zirnheld, (2002, updated 2004): Airborne surface profiling of Alaskan glaciers. Boulder, CO: National Snow and Ice Data Center. Digital media. Dalå, N. S., R. Forsberg, K. Keller, H. Skourup, L. Stenseng, S. M.Hvidegaard, (2004): Airborne LIDAR measurements of sea ice north of Greenland and Ellesmere Island 2004, GreenICe/SITHOS/CryoGreen/A 76 Projects, Final Report, pp 73. IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

6 Camera system IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

7 Field trip IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

8 Samples IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

9 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

10 Features in data Smoothly changing disparity No edge Low color variation IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

11 Features in data Specular Highlights IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

12 Stereo Disparity (d) Edge based matching(c) Non-Linear Diffusion(b) Membrane Diffusion IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

13 Diffusion 1 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

14 Diffusion 10 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

15 Diffusion 20 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

16 Diffusion 50 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

17 Diffusion 80 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

18 Diffusion 120 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

19 Diffusion 150 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

20 Diffusion 200 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

21 Diffusion 250 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

22 Diffusion 300 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

23 Classification Unambiguous Low Variance Occluded IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

24 Algorithm for Classification IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

25 How to fill Low Variance areas? Don’t have any unambiguous information about the depth at those pixels Interpolate from Boundary True Map Surface IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

26 Interpolation 63 Sampled VerticesTrue Map IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

27 How to Interpolate? Given n points on the boundary Triangulate… Which Triangulation? Delaunay Triangulation True Map 61 faces IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

28 Subdivide Loop Subdivision True Map 244 faces IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

29 Subdivide True Map 3904 faces 976 faces IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

30 What if…? True Map 104 faces 225 faces 425 faces 244 faces subdivision IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

31 Towards Algorithm Don’t know vertices…Don’t know edges Given Vertices…What are the best edges? Delaunay Triangulation Outline Scatter Points Triangulate Move Points Repeat… IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

32 Unstructured Triangulation Algorithm IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

33 Advantages Very simple Quality of Triangles is high Errors in Interpolation are low Can handle concave shapes and regions with holes IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

34 Negatives Uses Delaunay to triangulate every iteration May become unstable with wrong choice of parameters (very rare) May not converge IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

35 Finite Element Method Courtesy : A Pragmatic Introduction to the Finite Element Method for Thermal and Stress Analysis, Petr Krysl IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

36 Finite Element Method Courtesy : A Pragmatic Introduction to the Finite Element Method for Thermal and Stress Analysis, Petr Krysl IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

37 Finite Element Method Courtesy :http://cfdlab.ae.utexas.edu/~roystgnr/libmesh_intro.pdf IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

38 True surface True map 63 samples on boundary Interpolation with Delaunay Delaunay Triangulation (61 faces)Delaunay + Loop Subdivision (244 faces) Interpolation of Delaunay + Loop Subdivision Unstructured triangulation From [1] Interpolation with Unstructured triangulation IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

39 Result Ambiguous Unambiguous disparity Triangulation Final disparity IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

40 Comparison (c) Non-Linear Diffusion (b) Membrane Diffusion IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion (e) Ground Truth

41 More results IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

42 More results IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

43 Conclusions In areas containing very low color variation, interpolation gives better results than image matching Heuristic for classifying image regions Efficient methods for interpolation using triangulation and FEM IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

44 Future Directions Include disparity variance in factors for classification Change the differential equation to model developable surfaces IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

45 Publications Towards Estimation of Dense Disparities from Stereo Images Containing Large Textureless Regions. Rohith MV, Gowri Somanath, Chandra Kambhamettu, Cathleen Geiger. 19 th International Conference on Pattern Recognition. December 2008. Tampa, USA Reconstruction Of Snow And Ice Surfaces Using Multiple View Vision Techniques. Gowri Somanath, Rohith MV, Cathleen Geiger, Chandra Kambhamettu. 65 th Eastern Snow Conference, May 2008, Vermont, USA. IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

46 Bibliography Daniel Scharstein, Richard Szeliski. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. IJCV 2001. D. Scharstein, R. Szeliski, Stereo matching with Non-linear Diffusion. Computer Science TR 96-1575, Cornell University, Mar 1996. D. Scharstein, R. Szeliski. Stereo Matching with Non-linear diffusion. CVPR. June 1996. Jochen Alberty, Carsten Carstensen, Stefan Funken, Remarks Around 50 Lines of MATLAB: Short Finite Element Implementation, Numerical Algorithms,Volume 20, 1999. P. Persson, G.Strang. A simple mesh generator in Matlab. SIAM Review, Volume 46 (2), June 2004.. IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

47 Acknowledgements Dr. Chandra Kambhamettu Dr. Cathleen Geiger This work was made possible by National Science Foundation (NSF) Office of Polar Program grants, ANT0636726 and ARC0612105. IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Download ppt "Surface reconstruction of sea-ice through stereo - initial steps Rohith MV Gowri Somanath VIMS Lab."

Similar presentations

Bayesian Belief Propagation

Bayesian Belief Propagation
Active Shape Models Suppose we have a statistical shape model –Trained from sets of examples How do we use it to interpret new images? Use an “Active Shape.

Active Shape Models Suppose we have a statistical shape model –Trained from sets of examples How do we use it to interpret new images? Use an “Active Shape.
The fundamental matrix F

The fundamental matrix F
CSE473/573 – Stereo and Multiple View Geometry

CSE473/573 – Stereo and Multiple View Geometry
Efficient High-Resolution Stereo Matching using Local Plane Sweeps Sudipta N. Sinha, Daniel Scharstein, Richard Szeliski @ CVPR 2014 Yongho Shin.

Efficient High-Resolution Stereo Matching using Local Plane Sweeps Sudipta N. Sinha, Daniel Scharstein, Richard CVPR 2014 Yongho Shin.
Stereo Vision Reading: Chapter 11

Stereo Vision Reading: Chapter 11
Recap from Previous Lecture Tone Mapping – Preserve local contrast or detail at the expense of large scale contrast. – Changing the brightness within.

Recap from Previous Lecture Tone Mapping – Preserve local contrast or detail at the expense of large scale contrast. – Changing the brightness within.
Stereo.

Stereo.
December 5, 2013Computer Vision Lecture 20: Hidden Markov Models/Depth 1 Stereo Vision Due to the limited resolution of images, increasing the baseline.

December 5, 2013Computer Vision Lecture 20: Hidden Markov Models/Depth 1 Stereo Vision Due to the limited resolution of images, increasing the baseline.
Boundary matting for view synthesis Samuel W. Hasinoff Sing Bing Kang Richard Szeliski Computer Vision and Image Understanding 103 (2006) 22–32.

Boundary matting for view synthesis Samuel W. Hasinoff Sing Bing Kang Richard Szeliski Computer Vision and Image Understanding 103 (2006) 22–32.
Last Time Pinhole camera model, projection

Last Time Pinhole camera model, projection
Stanford CS223B Computer Vision, Winter 2005 Lecture 6: Stereo 2 Sebastian Thrun, Stanford Rick Szeliski, Microsoft Hendrik Dahlkamp and Dan Morris, Stanford.

Stanford CS223B Computer Vision, Winter 2005 Lecture 6: Stereo 2 Sebastian Thrun, Stanford Rick Szeliski, Microsoft Hendrik Dahlkamp and Dan Morris, Stanford.
CS6670: Computer Vision Noah Snavely Lecture 17: Stereo

CS6670: Computer Vision Noah Snavely Lecture 17: Stereo
CSci 6971: Image Registration Lecture 4: First Examples January 23, 2004 Prof. Chuck Stewart, RPI Dr. Luis Ibanez, Kitware Prof. Chuck Stewart, RPI Dr.

CSci 6971: Image Registration Lecture 4: First Examples January 23, 2004 Prof. Chuck Stewart, RPI Dr. Luis Ibanez, Kitware Prof. Chuck Stewart, RPI Dr.
Multiple View Geometry 15-463: Computational Photography Alexei Efros, CMU, Fall 2005 © Martin Quinn …with a lot of slides stolen from Steve Seitz and.

Multiple View Geometry : Computational Photography Alexei Efros, CMU, Fall 2005 © Martin Quinn …with a lot of slides stolen from Steve Seitz and.
Direct Methods for Visual Scene Reconstruction Paper by Richard Szeliski & Sing Bing Kang Presented by Kristin Branson November 7, 2002.

Direct Methods for Visual Scene Reconstruction Paper by Richard Szeliski & Sing Bing Kang Presented by Kristin Branson November 7, 2002.
The plan for today Camera matrix

The plan for today Camera matrix
Computer Vision Optical Flow Marc Pollefeys COMP 256 Some slides and illustrations from L. Van Gool, T. Darell, B. Horn, Y. Weiss, P. Anandan, M. Black,

Computer Vision Optical Flow Marc Pollefeys COMP 256 Some slides and illustrations from L. Van Gool, T. Darell, B. Horn, Y. Weiss, P. Anandan, M. Black,
CS 223b 1 More on stereo and correspondence. CS 223b 2 =?f g Mostpopular For each window, match to closest window on epipolar line in other image. (slides.

CS 223b 1 More on stereo and correspondence. CS 223b 2 =?f g Mostpopular For each window, match to closest window on epipolar line in other image. (slides.
3D from multiple views 15-463: Rendering and Image Processing Alexei Efros …with a lot of slides stolen from Steve Seitz and Jianbo Shi.

3D from multiple views : Rendering and Image Processing Alexei Efros …with a lot of slides stolen from Steve Seitz and Jianbo Shi.

Similar presentations

Ads by Google