Efficient Photometric Stereo Technique for Three-Dimensional Surfaces with Unknown BRDF

2005 ◽  
Author(s):  
Li Shen ◽  
T. Machida ◽  
H. Takemura
Keyword(s):  
Three Dimensional ◽  
Photometric Stereo

scholarly journals Real-time three-dimensional fingerprint acquisition via a new photometric stereo means

2013 ◽  
Vol 52 (10) ◽  
pp. 103103 ◽  
Author(s):  
Wuyuan Xie ◽  
Zhan Song ◽  
Ronald Chung
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Photometric Stereo

SURFACE MORPHOLOGY OF CHALKBOARD TIPS CAPTURES THE UNIQUENESS OF THE USER'S HAND STROKES

2010 ◽  
Vol 21 (04) ◽  
pp. 535-548
Author(s):  
CHRISTOPHER MONTEROLA ◽  
IRENE CRISOLOGO ◽  
JERIC TUGAFF ◽  
RENE BATAC ◽  
ANTHONY LONGJAS
Keyword(s):  
Three Dimensional ◽  
Principal Component ◽  
Photometric Stereo ◽  
Linear Discriminant ◽  
Average Classification Accuracy ◽  
Test Sets ◽  
Classification And Regression ◽  
High Degree ◽  
Dimensional Surface ◽  
Better Than

Penmanship has a high degree of uniqueness as exemplified by the standard use of hand signature as identifier in contract validations and property ownerships. In this work, we demonstrate that the distinctiveness of one's writing patterns is possibly embedded in the molding of chalk tips. Using conventional photometric stereo method, the three-dimensional surface features of blackboard chalk tips used in Math and Physics lectures are microscopically resolved. Principal component analysis (PCA) and neural networks (NN) are then combined in identifying the chalk user based on the extracted topography. We show that NN approach applied to eight lecturers allow average classification accuracy (Φ NN ) equal to 100% and 71.5 ± 2.7% for the training and test sets, respectively. Test sets are chalks not seen previously by the trained NN and represent 25% or 93 of the 368 chalk samples used. We note that the NN test set prediction is more than five-fold higher than the proportional chance criterion (PCC, Φ PCC = 12.9%), strongly hinting to a high degree of unique correlation between the user's hand strokes and the chalk tip features. The result of NN is also about three-fold better than the standard methods of linear discriminant analysis (LDA, Φ LDA = 27.0 ± 4.2%) or classification and regression trees (CART, Φ CART = 17.3 ± 3.7%). While the procedure discussed is far from becoming a practical biometric tool, our work offers a fundamental perspective to the extent on which the uniqueness of hand strokes of humans can be exhibited.

Shape Reproduction of Object with Many-Colored Surface by Photometric Stereo Method

2012 ◽  
Vol 516 ◽  
pp. 492-497
Author(s):  
Ryo Nakatani ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
Seishi Nakamura
Keyword(s):  
Spline Interpolation ◽  
Three Dimensional ◽  
Digital Camera ◽  
Photometric Stereo ◽  
Normal Vector ◽  
Object Shape ◽  
Colour Variation ◽  
Small Change ◽  
Photographic Imagery ◽  
Shape Data

Three-dimensional shapes created from photographic imagery using a photometric stereo method is restored in the present study. We used one object and one digital camera. The source of light was moved and the camera took a picture of two or more object shape data. The radiance value of the obtained photographic imagery was requested respectively. It was restored to three-dimensional geometry by calculating the normal vector from the radiance value, and combining the data of the photographic imagery. As for this method, it is well-known that application is difficult for objects that have lustre. Therefore, an improvement was attempted using spline interpolation. It has been understood that the method which reproduces it by using spline interpolation as a result is effective. Furthermore, because an error margin is caused when an object with colour variation is reproduced, an improvement was tried. First of all, the colour component of the image was extracted. Then, the image with a small change in brightness was chosen and reproduced. Consequently, a shape near the theoretical value was able to be reproduced for the reproduction of a two-colour object including white.

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