Development of microscopic shape measuring system using iterative photometric stereo techniques

In the manufacturing process of machine products, it is important to automate the manual visual inspection in detecting microscopic surface defects in order to improve the efficiency and eliminate human errors as well. The basic hardware system consists of a high-resolution camera equipped with a telecentric lens, and a device to change light directions using 60 LED light bulbs. We introduce an accurate automatic system to detect such surface defects based on the novel hardware system and the iterative photometric stereo techniques, which iteratively improve the quality of the estimation of the surface shape. Complex examples are provided to demonstrate the effectiveness of the proposed system.

[1]  E. North Coleman,et al.  Obtaining 3-dimensional shape of textured and specular surfaces using four-source photometry , 1982, Comput. Graph. Image Process..

[2]  Harry Shum,et al.  Interactive normal reconstruction from a single image , 2008, SIGGRAPH Asia '08.

[3]  Kiyoharu Aizawa,et al.  Robust photometric stereo using sparse regression , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Takeshi Shakunaga,et al.  Analysis of photometric factors based on photometric linearization. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[5]  Katsushi Ikeuchi,et al.  Efficient Estimation and Representation of 3D model with Sensor Fusion , 2010 .

[6]  Jovan Popović,et al.  Dynamic shape capture using multi-view photometric stereo , 2009, SIGGRAPH 2009.

[7]  Masashi Baba,et al.  High Density Shapes Using Photometric Stereo and Laser Range Sensor under Unknown Light-Source Direction , 2013, MVA.

[8]  Xuelong Li,et al.  Accurate Normal and Reflectance Recovery Using Energy Optimization , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Budirijanto Purnomo,et al.  Digital Hammurabi: design and development of a 3D scanner for cuneiform tablets , 2006, Electronic Imaging.

[10]  Michael J. Brooks,et al.  The variational approach to shape from shading , 1986, Comput. Vis. Graph. Image Process..

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

[12]  Katsushi Ikeuchi,et al.  Separating reflection components of textured surfaces using a single image , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Katsushi Ikeuchi,et al.  Median Photometric Stereo as Applied to the Segonko Tumulus and Museum Objects , 2009, International Journal of Computer Vision.

[14]  Edward H. Adelson,et al.  Microgeometry capture using an elastomeric sensor , 2011, SIGGRAPH 2011.

[15]  Takeo Kanade,et al.  Determining shape and reflectance of hybrid surfaces by photometric sampling , 1989, IEEE Trans. Robotics Autom..

[16]  Yasuyuki Matsushita,et al.  A hand-held photometric stereo camera for 3-D modeling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Diego F. Nehab,et al.  Efficiently combining positions and normals for precise 3D geometry , 2005, SIGGRAPH 2005.

[18]  Szymon Rusinkiewicz,et al.  Efficiently combining positions and normals for precise 3D geometry , 2005, ACM Trans. Graph..

[19]  Takashi Maekawa,et al.  Shape reconstruction from a normal map in terms of uniform bi-quadratic B-spline surfaces , 2015, Comput. Aided Des..

[20]  Dmitry B. Goldgof,et al.  A Simple Strategy for Calibrating the Geometry of Light Sources , 2001, IEEE Trans. Pattern Anal. Mach. Intell..