Jen`s Personal Blog

I think of using rotation of camera to obtain depth values while I was surveying some automated calibration methods.
Key of my idea is to measure per pixel correspondence between image sequence taken from same camera that is rotated by small angles on each capture. If the rotating angle is a known constant and the distances in pixel are calculated correctly, it’s possible to derive depth values. Unfortunately such trivial methodology has already been proposed 17 years ago, in any case my concept is on the right way

Shape from Rotation:

http://ieeexplore.ieee.org/Xplore/login.jsp?url=/iel2/340/3774/00139764.pdf?isnumber=3774&prod=CNF&arnumber=139764&arSt=625&ared=631&arAuthor=Szeliski%2C+R.

A good introduction to PCA which is easier to read than Wiki page.
The elementary math skills required are covered and stated in a clear and easy to understand way.

http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf

http://www.peoplenomics.com/history.htm

Wiimote IR sensors as Multitouch input, that sounds interesting.

http://gizmodo.com/gadgets/clips/wiimote-hack-is-wireless-multitouch-tv-321329.php

Find the boss of boss of boss…

http://genealogy.math.ndsu.nodak.edu/

Object reconstruction using a pencil and desk lamp, huh?

http://www.vision.caltech.edu/bouguetj/ICCV98/html_report/extended.html

• Lambertium Reflectance - reflect light in all directions
• Specular Reflectance (鏡面反射) - reflect light only if its incident angle is equal to reflected angle
• Stereoscopic Image - a image that make stereo sense to human brain by being looked by both eyes
• Silhouette

I got my first traffic ticket today

This morning I was stopped by police on my way home, after the TA training program.

My car was clamped because I drove it on motorcycle-only lane.

Alright, always look on the bright side, yup?

At least I wasn’t heavily fined, what an unlucky day!

The following papers we interested in are found from IEEE Xplorer

1. Shape reconstruction for color objects using segmentation and photometric stereo
   Ikeda, O.;
   Image Processing, 2004. ICIP ‘04. 2004 International Conference on
2. 3D shape measurements using stereo image scanner with three color light sources
   Ukida, H.; Takamatsu, S.;
   Instrumentation and Measurement Technology Conference, 2004. IMTC 04. Proceedings of the 21st IEEE
3. Determining surface curvature with photometric stereo
   Woodham, R.J.;
   Robotics and Automation, 1989. Proceedings., 1989 IEEE International Conference on
4. Facial feature measurements with photometric stereo
   Nakamura, Y.; Fujishima, T.; Nagao, M.;
   Robot and Human Communication, 1992. Proceedings., IEEE International Workshop on
5. Appearance Characterization of Linear Lambertian Objects, Generalized Photometric Stereo, and Illumination-Invariant Face Recognition
   Zhou, S.K.; Aggarwal, G.; Chellappa, R.; Jacobs, D.W.;
   Pattern Analysis and Machine Intelligence, IEEE Transactions on
6. Efficient photometric stereo technique for three-dimensional surfaces with unknown BRDF
   Li Shen; Machida, T.; Takemura, H.;
   3-D Digital Imaging and Modeling, 2005. 3DIM 2005. Fifth International Conference on
7. Automatic planning of light source placement for an active photometric stereo system
   Sakane, S.; Sato, T.; Kakikura, M.;
   Intelligent Robots and Systems ‘90. ‘Towards a New Frontier of Applications’, Proceedings. IROS ‘90. IEEE International Workshop on
8. Shape reconstruction from photometric stereo
   Lee, K.M.; Kuo, C.-C.J.;
   Computer Vision and Pattern Recognition, 1992. Proceedings CVPR ‘92., 1992 IEEE Computer Society Conference on
9. Photometric stereo using point light sources
   Kolagani, N.; Fox, J.S.; Blidberg, D.R.;
   Robotics and Automation, 1992. Proceedings., 1992 IEEE International Conference on
10. 3-D reconstruction using photometric stereo and silhouette informations
    Changsuk Cho; Minamitani, H.;
    Industrial Electronics, Control and Instrumentation, 1994. IECON ‘94., 20th International Conference on
11. Shape reconstruction from two color images using photometric stereo combined with segmentation and stereopsis
    Ikeda, O.;
    Advanced Video and Signal Based Surveillance, 2005. AVSS 2005. IEEE Conference on
12. Neural-Network-Based Photometric Stereo for 3D Surface Reconstruction
    Wen-Chang Cheng;
    Neural Networks, 2006. IJCNN ‘06. International Joint Conference on
13. The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows
    Barsky, S.; Petrou, M.;
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
14. Optimal illumination for three-image photometric stereo using sensitivity analysis
    Spence, A.D.; Chantler, M.J.;
    Vision, Image and Signal Processing, IEE Proceedings-
15. On optimal light configurations in photometric stereo
    Drbohlav, O.; Chantler, M.;
    Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on
16. Shape and spatially-varying BRDFs from photometric stereo
    Goldman, D.B.; Curless, B.; Hertzmann, A.; Seitz, S.M.;
    Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on
17. Performance results of the improved method of photometric stereo using local shape from shading on variable albedo samples
    Sakarya, U.; Erkmen, I.;
    Signal Processing and Communications Applications Conference, 2004. Proceedings of the IEEE 12th
18. Self-calibration and neural network implementation of photometric stereo
    Iwahori, Y.; Watanabe, Y.; Woodham, R.J.; Iwata, A.;
    Pattern Recognition, 2002. Proceedings. 16th International Conference on
19. hadows and highlights detection in 4-source colour photometric stereo
    Petrou, M.; Barsky, S.;
    Image Processing, 2001. Proceedings. 2001 International Conference on
20. Neural network based photometric stereo for object with non-uniform reflectance factor
    Iwahori, Y.; Woodham, R.J.; Shoaib Bhuiyan, M.; Ishii, N.;
    Neural Information Processing, 1999. Proceedings. ICONIP ‘99. 6th International Conference on
21. Calculation of surface position and orientation using the photometric stereo method
    Kim, B.; Burger, P.;
    Computer Vision and Pattern Recognition, 1988. Proceedings CVPR ‘88., Computer Society Conference on
22. Active photometric stereo
    Clark, J.J.;
    Computer Vision and Pattern Recognition, 1992. Proceedings CVPR ‘92., 1992 IEEE Computer Society Conference on
23. ShadowCuts: Photometric Stereo with Shadows
    Chandraker, Manmohan; Agarwal, Sameer; Kriegman, David;
    Computer Vision and Pattern Recognition, 2007. CVPR ‘07. IEEE Conference on
24. Accurate Photometric Stereo Using Four Surface Normal Approximations
    Ikeda, O.;
    Computer and Robot Vision, 2006. The 3rd Canadian Conference on
25. Principal components analysis and neural network implementation of photometric stereo
    Iwahori, Y.; Woodham, R.J.; Bagheri, A.;
    Physics-Based Modeling in Computer Vision, 1995., Proceedings of the Workshop on
26. Uniform converting matrix in photometric stereo shape reconstruction method
    Ikeda, O.;
    Circuits and Systems, 2004. MWSCAS ‘04. The 2004 47th Midwest Symposium on
27. Evaluation of 3D face analysis and synthesis techniques
    Chan, M.; Chia-Yen Chen; Barton, G.; Delmas, P.; Gimel’farb, G.; Leclercq, P.; Fischer, T.;
    Multimedia and Expo, 2004. ICME ‘04. 2004 IEEE International Conference on
28. Shape and materials by example: a photometric stereo approach
    Hertzmann, A.; Seitz, S.M.;
    Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on
29. Integration techniques for 3D surface reconstruction
    Thiele, H.; Klette, R.;
    Computer Graphics International, 1998. Proceedings
30. Shape measurement method integrating stereo vision and shape-from-shading with evolutionary programming
    Kobayashi, F.; Fukuda, T.; Shimojima, K.; Takusagawa, T.;
    Intelligent Robots and Systems, 1997. IROS ‘97., Proceedings of the 1997 IEEE/RSJ International Conference on
31. Shape reconstruction using extended photometric stereo
    Tae-Eun Kim; Sun-Ho Lee; Seok-Hyun Ryu; Jong-Soo Choi;
    Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on
32. 3-D object recognition by matching the total view information
    Bellaire, G.; Schluns, K.;
    Pattern Recognition, 1996., Proceedings of the 13th International Conference on
33. 3D object recognition from color intensity images
    Mustafa, A.A.Y.; Shapiro, L.G.; Ganter, M.A.;
    Pattern Recognition, 1996., Proceedings of the 13th International Conference on
34. Analysis of shape from shading algorithms for fast and realistic 3D face reconstruction APCCAS2002
    Fanany, M.I.; Kumazawa, I.;
    Circuits and Systems, 2002. APCCAS ‘02. 2002 Asia-Pacific Conference on
35. Automatic 3D reconstruction for face recognition
    Yuxiao Hu; Dalong Jiang; Shuicheng Yan; Lei Zhang; Hongjiang zhang;
    Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on
36. Low cost 3D face acquisition and modeling
    Garcia, E.; Dugelay, J.-L.;
    Information Technology: Coding and Computing, 2001. Proceedings. International Conference on
    2-4 April 2001 Page(s):657 - 661
    Digital Object Identifier 10.1109/ITCC.2001.918872
37. A New Method for Automatic 3D Face Registration
    Ayyagari, V.R.; Boughorbel, F.; Koschan, A.; Abidi, M.A.;
    Computer Vision and Pattern Recognition, 2005 IEEE Computer Society Conference on
38. Acquisition of Face Depth Information from Near Infrared Images
    Zheng, Ying; Li, Stan Z.; Chang, Jianglong; Wang, Zengfu;
    Information Acquisition, 2007. ICIA ‘07. International Conference on

This paper (2 pages) brief us an introduction to…

What the Photometric Stereo is:

Photometric Stereo is a method that takes three or more images of an object from the same camera view with lightings from different known directions for recovering a patch of surface from image.

What the advantages it have:

• Photometric Stereo makes no assumption of smoothness…
• Can use low-cost equipment…
• Very simple set-up…

What the principle it base on and how we can implement it

To read this paper, the readers should know the prerequisite: radiosity algorithm.