Optimising Light Source Positions to Minimise Illumination Variation for 3D Vision

Machine vision lighting systems must be designed to evenly illuminate scenes, so that objects' appearances do not change depending on their position. This is challenging when imaging 3D scenes where the camera's field of view or depth of field are large. This paper describes a scheme to design effective lighting systems in such situations: lighting across the scene is modelled, and positions of lights are optimised to minimise variation in illumination levels. The scheme is used to design a lighting system for an agricultural robot, which reduces variation in the appearance of crops due to uneven lighting. The lighting for a compact medical imaging device is redesigned, which will result in a 53% reduction in the range of lighting levels in the scene.

[1]  Stephen M. Rock,et al.  A LOW ENERGY SENSOR FOR AUV-BASED JELLYFISH TRACKING , 2001 .

[2]  V. Leitáo,et al.  Computer Graphics: Principles and Practice , 1995 .

[3]  Yi Zhang,et al.  IVS 09: Future Research in Vehicle Vision Systems , 2009, IEEE Intelligent Systems.

[4]  M. Carter.  Computer graphics: Principles and practice , 1997 .

[5]  Michael R. M. Jenkin,et al.  Computational principles of mobile robotics , 2000 .

[6]  Éric Marchand,et al.  Control Camera and Light Source Positions using Image Gradient Information , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[7]  Steven Mills,et al.  Design and calibration of a hybrid computer vision and structured light 3D imaging system , 2011, The 5th International Conference on Automation, Robotics and Applications.

[8]  Michael Jenkin,et al.  Computational Principles of Mobile Robotics: Bibliography , 2010 .

[9]  William H. Press,et al.  Numerical recipes in C , 2002 .

[10]  Robert M. Haralick,et al.  Automatic sensor and light source positioning for machine vision , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[11]  Rihard Karba,et al.  Wide-angle camera distortions and non-uniform illumination in mobile robot tracking , 2004, Robotics Auton. Syst..

[12]  Yud-Ren Chen,et al.  Machine vision technology for agricultural applications , 2002 .

[13]  J. G. Chase,et al.  Digital Image-Based Elasto-Tomography: Proof of Concept Studies for Surface Based Mechanical Property Reconstruction∗ , 2004 .

[14]  Chii-Maw Uang,et al.  Better reading light system with light-emitting diodes using optimized Fresnel lens , 2006 .

[15]  Aviv Bergman,et al.  Determining the camera and light source location for a visual task , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[16]  Euripides G. M. Petrakis,et al.  A survey on industrial vision systems, applications, tools , 2003, Image Vis. Comput..

[17]  Hiroyuki Fujita.  What can MEMS do for Robotics , 2000 .

[18]  Bill Triggs,et al.  Automatic Task Planning for Robot Vision , 1996 .

[19]  Keiichi Yamada,et al.  Robot vision robust to changes of lighting conditions using a wide‐dynamic‐range visual sensor , 1997 .