1. CS290 Spring 2000 Slide:1 Structured Light for Laparoscopic Surgery CS290 Computer Vision Jeremy Ackerman CS290 Computer Vision Jeremy Ackerman

2. CS290 Spring 2000 Slide:2 Experimental Setup Light patterns *Projected from LCD video projector *Images in PowerPoint slide show Image capture *Pulnix progressive scan camera attached to laparoscope *Record to MiniDV tape Light patterns *Projected from LCD video projector *Images in PowerPoint slide show Image capture *Pulnix progressive scan camera attached to laparoscope *Record to MiniDV tape

3. CS290 Spring 2000 Slide:3 Data Collected Image sets acquired from two positions: *“wet” series *talc coated series Images with induced pig movement All series captured with room lights on and off. Image sets acquired from two positions: *“wet” series *talc coated series Images with induced pig movement All series captured with room lights on and off.

4. CS290 Spring 2000 Slide:4 Image Processing (1) Finding stripes *Subtract “negative” image from the “positive” *Threshold “on” stripes for points with intensity >0 *Threshold “off” stripes for points with intensity <0 Finding stripes *Subtract “negative” image from the “positive” *Threshold “on” stripes for points with intensity >0 *Threshold “off” stripes for points with intensity <0

5. CS290 Spring 2000 Slide:5 Image Processing (2) Create the encoded image: *for each pair of images, assign a single bit to set for “on” or off. *Set an error bit for any pixels not identified as “on” or “off” *OR bit assignments from all images Create the encoded image: *for each pair of images, assign a single bit to set for “on” or off. *Set an error bit for any pixels not identified as “on” or “off” *OR bit assignments from all images

6. CS290 Spring 2000 Slide:6 Image Processing (3) Create a “disparity” image: *for each column, find the center of mass of each stripe *subtract center of mass of stripes from a set of stripes at known distance (there is a lot of calibration that should be done here) *apply simplified range formula... Create a “disparity” image: *for each column, find the center of mass of each stripe *subtract center of mass of stripes from a set of stripes at known distance (there is a lot of calibration that should be done here) *apply simplified range formula...

7. CS290 Spring 2000 Slide:7 Results (1) Texture mapped range images *reasonable (looking) range images could be acquired from both talced (Lambertian) and specular surfaces *more holes are observed with smaller numbers of stripes for specular surfaces Texture mapped range images *reasonable (looking) range images could be acquired from both talced (Lambertian) and specular surfaces *more holes are observed with smaller numbers of stripes for specular surfaces

8. CS290 Spring 2000 Slide:8 Results (2) Talc coated vs. wet: rendered range images

9. CS290 Spring 2000 Slide:9 Lessons Learned *synchronizing cameras (or light sources) is tough *hardware never cooperates under time pressure *the orientation of stripe patterns is important *aliasing is bad… *these techniques seem to work well *synchronizing cameras (or light sources) is tough *hardware never cooperates under time pressure *the orientation of stripe patterns is important *aliasing is bad… *these techniques seem to work well

10. CS290 Spring 2000 Slide:10 Acknowledgements Thanks to: Kurtis Keller Tim Johnson and lab Michael Rosenthal Thanks to: Kurtis Keller Tim Johnson and lab Michael Rosenthal