Physics-based models of color and IR video for sensor fusion

Physics-based sensor fusion attempts to utilize the phenomenology of sensors to combine external conditions with data collected by the sensors into a global consistent dynamic representation. Although there have been a few approaches using this paradigm, it is still not entirely clear what kinds of physical models are appropriate for different sensing devices and conditions. We provide physical models that are suitable for the visible and infrared region of the spectrum. The physical models are described in detail. Moreover, the advantages and disadvantages of each model, their applicability, and guidelines for selecting the appropriate parameters are provided. Experimental results are also provided to indicate the applicability of the physical models.

[1]  Bir Bhanu,et al.  Multistrategy fusion using mixture model for moving object detection , 2001, Conference Documentation International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI 2001 (Cat. No.01TH8590).

[2]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[3]  M. E. Ulug,et al.  Feature and data-level fusion of infrared and visual images , 1999, Defense, Security, and Sensing.

[4]  Ioannis Pavlidis,et al.  Automatic detection of vehicle passengers through near-infrared fusion , 1999, Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383).

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

[6]  Dennis M. Buede,et al.  A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[7]  Jake K. Aggarwal,et al.  Physics-based integration of multiple sensing modalities for scene interpretation , 1997 .

[8]  Dimitris N. Metaxas,et al.  Dynamic 3D models with local and global deformations: deformable superquadrics , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[9]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[10]  Charles W. Therrien,et al.  An adaptive technique for the enhanced fusion of low-light visible with uncooled thermal infrared imagery , 1997, Proceedings of International Conference on Image Processing.

[11]  Stephen B. Campana,et al.  The Infrared & Electro-Optical Systems Handbook. Passive Ellectro-Optical Systems, Volume 5, , 1993 .

[12]  Bir Bhanu,et al.  Moving shadow detection using a physics-based approach , 2002, Object recognition supported by user interaction for service robots.

[13]  张鸿宾 Multi-sensor Data Fusion by Improved Hough Transformation , 1995 .

[14]  Demetri Ter Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics , 1990 .

[15]  Y. Jaluria,et al.  An Introduction to Heat Transfer , 1950 .

[16]  Belur V. Dasarathy,et al.  Fusion strategies for enhancing decision reliability in multisensor environments , 1996 .

[17]  George J. Zissis,et al.  The Infrared & Electro-Optical Systems Handbook. Sources of Radiation, Volume 1, , 1993 .

[18]  Gregory J. Ward,et al.  The RADIANCE lighting simulation and rendering system , 1994, SIGGRAPH.