A factorization‐based approach to photometric stereo

This article presents an adaptation of a factorization technique to tackle the photometric stereo problem. That is to recover the surface normals and reflectance of an object from a set of images obtained under different lighting conditions. The main contribution of the proposed approach is to consider pixels in shadow and saturated regions as missing data, in order to reduce their influence to the result. Concretely, an adapted Alternation technique is used to deal with missing data. Experimental results considering both synthetic and real images show the viability of the proposed factorization‐based strategy. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 115–119, 2011.

[1]  R. Hartley,et al.  PowerFactorization : 3D reconstruction with missing or uncertain data , 2003 .

[2]  Hideki Hayakawa Photometric stereo under a light source with arbitrary motion , 1994 .

[3]  Daniel Snow,et al.  Determining Generative Models of Objects Under Varying Illumination: Shape and Albedo from Multiple Images Using SVD and Integrability , 1999, International Journal of Computer Vision.

[4]  Ira Kemelmacher-Shlizerman,et al.  Photometric Stereo with General, Unknown Lighting , 2006, International Journal of Computer Vision.

[5]  Pedro M. Q. Aguiar,et al.  Estimation of Rank Deficient Matrices from Partial Observations: Two-Step Iterative Algorithms , 2003, EMMCVPR.

[6]  Amnon Shashua,et al.  On Photometric Issues in 3D Visual Recognition from a Single 2D Image , 2004, International Journal of Computer Vision.

[7]  Robert J. Woodham,et al.  Photometric method for determining surface orientation from multiple images , 1980 .

[8]  Maria Petrou,et al.  Recursive photometric stereo when multiple shadows and highlights are present , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Peter Kovesi,et al.  Shapelets correlated with surface normals produce surfaces , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[10]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[11]  Jiebo Luo,et al.  A Subspace Model-Based Approach to Face Relighting Under Unknown Lighting and Poses , 2008, IEEE Transactions on Image Processing.

[12]  P. Belhumeur,et al.  Learning Object Representations from LightingVariationsR , 1996 .

[13]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[14]  Li Zhang,et al.  Shape and motion under varying illumination: unifying structure from motion, photometric stereo, and multiview stereo , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[15]  Alan L. Yuille,et al.  Learning Object Representation form Lighting Variations , 1996, Object Representation in Computer Vision.