Artificial Intelligence based Camera Calibration

Camera calibration technique plays a vital role in three dimensional computer vision systems. The aim of this technique is to calibrate the camera in order to collect more precise three dimensional data from the images to utilize for robot navigation, three dimensional reconstruction, biomedical, virtual reality and visual surveillance. In camera calibration one of the major issue is to search out the set of image parameters describing the mapping between three dimensional images reference coordinates and two-dimensional images reference coordinates. Currently, MATLAB toolbox and OpenCv are the most popular tools used by the researchers for camera calibration. We utilize the concept of deep learning to recognize the chessboard corners. Our presented technique is a convolutional neural network (CNN) trained on a huge number of chessboard images. The network is trained on different datasets: noisy and with high lens malformation images. The proposed scheme is more accurate than the conventional MATLAB algorithm technique. The presented technique is more accurate against the different sort of ruination present in the training set. Results reaffirmed the correctness and effectiveness of our proposed CNN technique.

[1]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[3]  Wang Qi,et al.  Review on camera calibration , 2010, 2010 Chinese Control and Decision Conference.

[4]  Y. Li,et al.  A camera calibration technique based on OpenCV , 2010, The 3rd International Conference on Information Sciences and Interaction Sciences.

[5]  Leandro Bezerra Marinho,et al.  Localization of Mobile Robots with Topological Maps and Classification with Reject Option using Convolutional Neural Networks in Omnidirectional Images , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[6]  Zhou Jianjiang.  Improved camera self-calibration method based on circular points , 2007 .

[7]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[8]  Sohaib Khan,et al.  Camera calibration and three-dimensional world reconstruction of stereo-vision using neural networks , 2001, Int. J. Syst. Sci..

[9]  Elli Angelopoulou,et al.  ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration , 2014, ECCV.

[10]  Elena Casiraghi,et al.  CORNER LOCALIZATION IN CHESSBOARDS FOR CAMERA CALIBRATION , 2005 .

[11]  Qiao Yan-feng.  Camera Calibration of Attitude Measurement System Based on BP Neural Network , 2007 .

[12]  Yvan Saeys,et al.  Automated Membrane Detection in Electron Microscopy using Convolutional Neural Networks , 2016 .

[13]  Stephen M. Smith,et al.  SUSAN—A New Approach to Low Level Image Processing , 1997, International Journal of Computer Vision.

[14]  Azra Fetic,et al.  The procedure of a camera calibration using Camera Calibration Toolbox for MATLAB , 2012, 2012 Proceedings of the 35th International Convention MIPRO.

[15]  Stephanie Powell,et al.  Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures , 2008, NeuroImage.

[16]  Wilfried Philips,et al.  MATE: Machine Learning for Adaptive Calibration Template Detection , 2016, Sensors.

[17]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[18]  Hu Zhan.  A Review on Some Active Vision Based Camera Calibration Techniques , 2002 .

[19]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Junghee Jun,et al.  Robust camera calibration using neural network , 1999, Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030).

[21]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.