Thermal-depth matching in dynamic scene based on affine projection and feature registration

This paper aims to study the construction of 3D temperature distribution reconstruction system based on depth and thermal infrared information. Initially, a traditional calibration method cannot be directly used, because the depth and thermal infrared camera is not sensitive to the color calibration board. Therefore, this paper aims to design a depth and thermal infrared camera calibration board to complete the calibration of the depth and thermal infrared camera. Meanwhile a local feature descriptors in thermal and depth images is proposed. The belief propagation matching algorithm is also investigated based on the space affine transformation matching and local feature matching. The 3D temperature distribution model is built based on the matching of 3D point cloud and 2D thermal infrared information. Experimental results show that the method can accurately construct the 3D temperature distribution model, and has strong robustness.

[1]  A Improved Infrared and Visible Images Matching Based on SURF , 2013 .

[2]  George Wolberg,et al.  Multiview Geometry for Texture Mapping 2D Images Onto 3D Range Data , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  Yan Shi,et al.  3D depth information extraction with omni-directional camera , 2015, Inf. Process. Lett..

[4]  Chengdong Wu,et al.  3D Temperature Distribution Model Based on Thermal Infrared Image , 2017, J. Sensors.

[5]  Scott Sorensen,et al.  Material classification with thermal imagery , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Aymen Ben Azouz,et al.  Development of a teat sensing system for robotic milking by combining thermal imaging and stereovision technique , 2015, Comput. Electron. Agric..

[7]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Ahmed Adnan Aqrawi,et al.  Improved Fault Segmentation Using a Dip Guided And Modified 3D Sobel Filter , 2011 .

[9]  Chia-Hung Yeh,et al.  3D Reconstruction from IR Thermal Images and Reprojective Evaluations , 2015 .

[10]  Narendra Ahuja,et al.  A constant-space belief propagation algorithm for stereo matching , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Ioannis Stamos,et al.  Automatic 3D to 2D registration for the photorealistic rendering of urban scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Chengdong Wu,et al.  Projector calibration algorithm in omnidirectional structured light , 2017, Other Conferences.

[13]  Song Zhiwei,et al.  A new sensor fusion framework to deal with false detections for low-cost service robot localization , 2013, 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[14]  Tong Jia,et al.  Scene Depth Perception Based on Omnidirectional Structured Light , 2016, IEEE Transactions on Image Processing.

[15]  Liang-Gee Chen,et al.  Efficient message reduction algorithm for stereo matching using belief propagation , 2010, 2010 IEEE International Conference on Image Processing.

[16]  Diego González-Aguilera,et al.  Novel approach to 3D thermography and energy efficiency evaluation , 2012 .

[17]  Ludek Zalud,et al.  Robot mapping with range camera, CCD cameras and thermal imagers , 2014, 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR).