1 / 23

Stereo analysis of low textured regions with application towards sea-ice reconstruction

Stereo analysis of low textured regions with application towards sea-ice reconstruction. Rohith MV, Gowri Somanath , Chandra Kambhamettu Video /Image Modeling and Synthesis (VIMS) Lab , Dept . of Computer and Information Sciences Cathleen Geiger

marcin
Download Presentation

Stereo analysis of low textured regions with application towards sea-ice reconstruction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Stereo analysis of low textured regions with application towards sea-ice reconstruction RohithMV, GowriSomanath, Chandra Kambhamettu Video/Image Modeling and Synthesis(VIMS) Lab, Dept. of Computer and Information Sciences Cathleen Geiger Center for Climatic Research, Department. of Geography University of Delaware, USA

  2. Sea ice

  3. 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

  4. Outline • Stereo in presence of large texture-less areas • Entropy based Segmentation • Our Approach • Two stage estimation • MRF Formulation • Occlusion Model • Comparison of results • Conclusion

  5. Sample Images

  6. Some characteristics in images Smoothly changing disparity No edge Low color variation

  7. Stereo Left Image Hierarchical BP Our Algorithm Graph Cuts

  8. Previous approaches

  9. Entropy based segmentation argmax

  10. Entropy based segmentation • Convert the image to grayscale and calculate the histogram. • Estimate the brightness threshold as the gray value that maximizes the entropy of the segmented image. • Partition the histogram based on that threshold into two parts. Equalize the two histograms. For each histogram repeat steps 2 and 3.

  11. Comparison with mean shift Entropy based segmentation Left Image Entropy based segments Mean Shift segments

  12. Our approach • Two stage solution S1 S1 S2 S2 S1 S2 S3 S3 S3 • Segment disparity • Single disparity per segment • Fewer disparity levels • Segment neighborhood • Pixel disparity • Disparity per pixel • Full range of disparities • Pixel neighborhood • Occlusion Detection

  13. Example

  14. MRF Formulation • Segment Level Disparity

  15. MRF Model • Pixel Level Disparity

  16. Occlusion Model Rohith MV, GowriSomanath, Chandra Kambhamettu, Cathleen Geiger Towards estimation of dense disparities from stereo images containing large textureless regions. 19th International Conference on Pattern Recognition(ICPR), 2008

  17. Results

  18. Results Results

  19. Results Results

  20. Middlebury dataset

  21. Conclusions • Entropy based segmentation to handle large texture-less regions • Two step MRF formulation • Solution using belief propagation • Can handle large disparity ranges

  22. Future work • Explore combination of segmentations based on region characteristics • Use priors over segmentation and disparity calculation in sequence of images

  23. Acknowledgements • This workwas made possible by National Science Foundation(NSF) Office of Polar Program grants, ANT0636726 and ARC0612105.

More Related