Research at VIMS Lablliao/signewgrad12f/faculty_talks/chand… · Research at VIMS Lab Author:...
Transcript of Research at VIMS Lablliao/signewgrad12f/faculty_talks/chand… · Research at VIMS Lab Author:...
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Research at VIMS Lab
www.cis.udel.edu/~vims
Gowri Somanath, Rohith MV, Abhishek
Vincent Ly, Scott Sorensen, Philip Saponaro, Guoyu Lu, Xiaolong Wang, Ryan O'Dowd
Chandra Kambhamettu
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Current projects
• Nonrigid Motion Analysis
• Tumor analysis
• Object categorization and recognition
• Stereo analysis and reconstruction of ice
topography
• Single camera stereo
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Nonrigid Motion: Main Goals
• Investigate, design and implement methods for Nonrigid motion analysis using intrinsic differential geometry measures.
• Develop algorithms for Nonrigid Motion Classification and segmentation.
• Develop metrics for evaluating the performance of motion models, and approaches to apply simple to complex hierarchy of motion models.
• Integrate developed algorithms into the structure and motion recovery process from 2D image sequences.
• Devise quantitative and qualitative evaluation measures of Nonrigid motion analysis for a variety of applications.
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3D Images
Stereo/Multiple Views
Monocular
Structure & Nonrigid
Motion Analysis System
(SMAS)
3D Scene Flow
Estimation
Nonrigid Motion
Analysis
Point Correspondences
Nonrigid Motion Models
Dense 3D Map
- Complete and automatic system to compute dense 3D
scene flow and scene structure.
- Seamless integration of 2D motion and stereo constraints.
Nonrigid
Approaches
Rigid
Isometric
Conformal
General
- General hierarchical system to integrate
local and global motion analysis for
shape-based and non-shape based applications.
- Initial Structure is assumed.
System Overview
- Differential geometry based methods
- Geometric & spline based methods
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Applications and Results
Scene Flow
Cloud Motion
Facial Motion
Protein Docking
Rigid and Nonrigid motion analysis based on
surface segments
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Lung Tumors: GGOs
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GGO Extracted
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Stereo
• How do we see depth? - Stereo
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Stereo
• Meeting point of 2 lines
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Reconstruction recipe
• Find the two lines
– Start with searching for the same point in the
two images
– Define the rays in the same co-ordinate
system
• Figure out the pinhole positions
• Figure out where the pinholes are wrt each other
• Now see where the two lines meet – that’s
the 3D point
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Prism Cameras
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Indoor Stereo
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Ice cameras
Dimension=20mt by 60mt; scene is 15-25 mt from camera
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Ice cameras
Mean error between the
stereo and 3D laser
scanned surfaces when
smoothness parameter
α is varied.
Reconstruction from laser scanner (100,000 vertices)
Reconstruction from stereo (mesh with 1,337,079 vertices)
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• Structure based
– Surface roughness
– Ice thickness
• Appearance based
– Snow cover
– Floe size distribution
– Lead structure and polynya density
– Ice color
Products for habitat study
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Proposed setup
20m 50m
20m
30m
Stereo Swath
Video Swath
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Stereo pipeline
Calibration Images
Calibration
Data
Field Images Disparity
True scale 3D reconstruction
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3D Reconstruction
(a) One image from the sequence captured on river ice.
(b) Reconstructed point cloud in 3D of the region shown in (a)
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Structure from motion
Video frames Feature tracks
3D Reconstruction
of feature points
Surface reconstruction by interpolation