Spring 2005 Mid-Term Presentation

1. Spring 2005 Mid-Term Presentation Chris Kammerud Imaging, Robotics, & Intelligent Systems Laboratory The University of Tennessee February 23, 2005

2. Outline • Instrumentation • Viz-tek • Large Chamber Scanning Microscope (LCSEM) @ Oak Ridge • Implementation : MEMS data • Rectification for stereo matching • Theory : Fusion Formalism • Publication and thesis work

3. Viz-Tek • Mirror attached to frame • Parts ordered for assembly to wall

4. Drawings of Hinged Mirror C-channel attached to wall, hinge attached to c-channel

5. LCSEM • Y-12 National Security Complex recently acquired a Large Chamber Scanning Electron Microscope • Instrumentation task is to characterize this microscope www.lcsem.com

6. LCSEM Capabilities • Can scan specimen with dimensions up to 1 m x 1m and weighing as much as 300 kg • Magnification 10x-200,000x • Different sensors • Backscatter Electron Detector (BSD) • Energy dispersive X-ray spectroscopy (EDX) • Fourier transform infrared spectroscopy (FT-IR) • Electron backscatter diffraction (EBSD) • Focused ion beam (FIB)

7. Energy Dispersive X-ray Spectroscopy • Multi-spectral sensor used for elemental analysis • Looks at spectrum peaks for evidence of a specific element’s presence in the sample http://www.lcsem.com http://www.wsl.ch/staff/beat.frey/edx.jpg

8. EDX Texture Map • Several papers have been published on the generation of EDX maps which can describe elemental composition of a specimen’s surface [1]-[3] • These maps are often lower in resolution than secondary or backscattered resolutions (a) (b) (c) SEM-EDX mapping of plate coated with copper particles. (a) SEM image, (b) EDX mapping of copper, (c) EDX mapping of carbon [1]

9. Fusion Formalism • The goal is to acquire secondary electron images as well as EDX elemental maps • Digital elevation maps can be created through the secondary electron images, and the EDX elemental maps can be applied through texture mapping of a low resolution image to a higher resolution model

10. SEM Stereo Implementation • Last semester implemented sparse matching that contained several mis-matches and was not able to produce a dense disparity map • This semester working on calibration and rectification of the images to enable more robust stereo matching • Goal is to interpolate a surface between points matched with high confidence 1024 x 768 , Tilt angle = 4o ~0.1 um per pixel 1024 x 768 , Tilt angle = 8o ~0.1 um per pixel

11. Rectification • Rectification can aid in the efficiency and accuracy of stereo matching by reducing the search space from a 2D-window to a 1-D space along the epipolar line • From [4], the epipolar line equation when adopting the parallel projection model is: • G1x + G2y + G3x’ + G4y’ = 1 • Where (x,y) and (x’,y’) are corresponding points in the left and right image respectively • G1-G4 can be found using a small set of matched points and solving the resulting set of equations

12. False Matches Left Stereo Image Right Stereo Image • Small errors in matched points can lead to large errors in rectification Bad Rectification

13. Feature Matching • In order to help ensure correct matches, features, edges, etc. can be detected instead of points • Implemented the Harris Corner detector as described by Chris Broaddus [5] Left Stereo Image, detected corners in green Right Stereo Image, detected corners in green

14. Rectification Results • Corner matching was implemented as an enhancement of point to point matching • Matched corners were used to solve for G1-G4 Unrectified stereo pair Rectified stereo pair

15. Rectification Closer Look • Zooming in a small difference can be seen between the unrectified and rectified images Zoomed view of unrectified stereo pair showing a horizontal line that intersects the same circular feature at different points in the left and right stereo image Zoomed view of rectified stereo pair showing a horizontal line that intersects the same circular feature at the same point in the left and right stereo image

16. Conclusions & Future Work • Mirror and frame are ready to be mounted to the wall • A possible fusion of sensor data from the LCSEM at Oak Ridge was proposed • Rectification has been implemented • Corner matching was implemented to increase accuracy of point matching for rectification • Subsequent work will include the generation of a dense 3D model from rectified stereo pairs along

17. References [1] Cai, S., Xia, X., Xie, C. (2005, May) Corrosion behavior of copper/LDPE nanocomposites in simulated uterine solution. Biomaterials, Vol. 26, No. 15, pp. 2671-276. [2] Campos, C.E.M., Pizani, P.S. (2002, November) Morphologica studies of annealed GaAs and GaSb surfaces by micro-Raman spectroscopy and EDX microanalysis. Applied Surface Science, Vol. 200, pp. 111- 116. [3] Paclik, P., Duin, R.P.W., van Kempen, G.M.P., Kohlus, R. (2002, August), Supervised segmentation of backscatter images for product analysis. Proceedings of International Conference on Pattern Recognition, Quebec City, Canada. [4] Morgan, M., Kim, K., Jeong, S., Habib, A. (2004, July). Indirect Epipolar Resampling of Scenes Using Parallel Projection Modeling of Linear Array Scanners. XXth Congress of ISPRS. [5] Broaddus, C. (2004). 3D Structure from Motion to Calculate Barge Dimensions. http://imaging.utk.edu/classes/spring2004/cbroaddus/Thesis/webfiles/reports.htm