An Accurate Illumination Model of Machined Surface Based on Micro-Image

This paper focused on the illumination model of machined surface based on micro-image. According to micro-image forming condition, the theory that the image brightness is related to the microfacet topography and surface reflection characteristics is presented. The distribution rule of micro-topography and reflection characteristics of sample surface is analyzed according to the measured data. An illumination mode of machined surface is established based on the analysis result, and the model parameters are obtained by using the simulated annealing algorithm. Verification results show that this proposed model can improve the simulation accuracy significantly and describe the lighting effect of machined surface. The research will provide a new idea and method for the 3D reconstruction.

[1]  K. Torrance,et al.  Theory for off-specular reflection from roughened surfaces , 1967 .

[2]  W. Jack Bouknight,et al.  A procedure for generation of three-dimensional half-toned computer graphics presentations , 1970, CACM.

[3]  Bui Tuong Phong Illumination for computer generated pictures , 1975, Commun. ACM.

[4]  L D Brooks,et al.  Bidirectional reflectance distribution function of the Infrared Astronomical Satellite solar-shield material. , 1982, Applied optics.

[5]  Robert L. Cook,et al.  A Reflectance Model for Computer Graphics , 1987, TOGS.

[6]  R M Baul,et al.  Experimental Evaluation of ‘Shape from Shading’ for Engineering Component Profile Measurement , 1989 .

[7]  Gregory J. Ward,et al.  Measuring and modeling anisotropic reflection , 1992, SIGGRAPH.

[8]  Christophe Schlick,et al.  A Survey of Shading and Reflectance Models , 1994, Comput. Graph. Forum.

[9]  Kazufumi Kaneda,et al.  An accurate illumination model for objects coated with multilayer films , 2001, Comput. Graph..

[10]  Shree K. Nayar,et al.  Generalization of the Lambertian model and implications for machine vision , 1995, International Journal of Computer Vision.

[11]  P. Tse,et al.  An improved Hilbert–Huang transform and its application in vibration signal analysis , 2005 .

[12]  Cheng Wu,et al.  A Simulated Annealing Based Beam Search Algorithm for the Flow-Shop Scheduling Problem , 2008, Int. J. Pattern Recognit. Artif. Intell..

[13]  Xinman Zhang,et al.  Fast viscosity solutions for shape from shading under a more realistic imaging model , 2009 .

[14]  Z. Du,et al.  Simple three-dimensional laser radar measuring method and model reconstruction for hot heavy forgings , 2012 .

[15]  Chidchanok Lursinsap,et al.  Stereoscopic Face Reconstruction from a Single 2-Dimensional Face Image Using Orthogonality of Normal Surface and Y-Ratio , 2016, Int. J. Pattern Recognit. Artif. Intell..

[16]  Guohui Wang,et al.  Three-dimensional reconstruction of hybrid surfaces using perspective shape from shading , 2016 .

[17]  Ioannis A. Kakadiaris,et al.  3D-2D face recognition with pose and illumination normalization , 2017, Comput. Vis. Image Underst..

[18]  Liang Zhao,et al.  Concrete CT Image Quick Three-Dimensional Reconstruction Research , 2017, Int. J. Pattern Recognit. Artif. Intell..

[19]  Yu Han,et al.  A Novel Weighted Variational Model for Image Denoising , 2017, Int. J. Pattern Recognit. Artif. Intell..

[20]  Hong Song,et al.  Study of Dynamometer Cards Identification Based on Root-Mean-Square Error Algorithm , 2018, Int. J. Pattern Recognit. Artif. Intell..

[21]  Weichao Shi,et al.  Measurement and modeling of bidirectional reflectance distribution function (BRDF) on cutting surface based on the coaxial optical microscopic imaging , 2018, Optik.