{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T17:10:19Z","timestamp":1723396219679},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T00:00:00Z","timestamp":1478217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004385","name":"Universiteit Gent","doi-asserted-by":"publisher","award":["01D21213"],"id":[{"id":"10.13039\/501100004385","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"The problem of camera calibration is two-fold. On the one hand, the parameters are estimated from known correspondences between the captured image and the real world. On the other, these correspondences themselves\u2014typically in the form of chessboard corners\u2014need to be found. Many distinct approaches for this feature template extraction are available, often of large computational and\/or implementational complexity. We exploit the generalized nature of deep learning networks to detect checkerboard corners: our proposed method is a convolutional neural network (CNN) trained on a large set of example chessboard images, which generalizes several existing solutions. The network is trained explicitly against noisy inputs, as well as inputs with large degrees of lens distortion. The trained network that we evaluate is as accurate as existing techniques while offering improved execution time and increased adaptability to specific situations with little effort. The proposed method is not only robust against the types of degradation present in the training set (lens distortions, and large amounts of sensor noise), but also to perspective deformations, e.g., resulting from multi-camera set-ups.<\/jats:p>","DOI":"10.3390\/s16111858","type":"journal-article","created":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T15:18:38Z","timestamp":1478272718000},"page":"1858","source":"Crossref","is-referenced-by-count":27,"title":["MATE: Machine Learning for Adaptive Calibration Template Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7461-8589","authenticated-orcid":false,"given":"Simon","family":"Donn\u00e9","sequence":"first","affiliation":[{"name":"iMinds - IPI, Ghent University, Ghent B-9000, Belgium"}]},{"given":"Jonas","family":"De Vylder","sequence":"additional","affiliation":[{"name":"iMinds - IPI, Ghent University, Ghent B-9000, Belgium"}]},{"given":"Bart","family":"Goossens","sequence":"additional","affiliation":[{"name":"iMinds - IPI, Ghent University, Ghent B-9000, Belgium"}]},{"given":"Wilfried","family":"Philips","sequence":"additional","affiliation":[{"name":"iMinds - IPI, Ghent University, Ghent B-9000, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/0600000023","article-title":"Camera models and fundamental concepts used in geometric computer vision","volume":"6","author":"Sturm","year":"2011","journal-title":"Found. Trends Comput. Graph. Vis."},{"key":"ref_2","first-page":"444","article-title":"Decentering distortion of lenses","volume":"32","author":"Brown","year":"1966","journal-title":"Photom. Eng."},{"key":"ref_3","unstructured":"Bouguet, J.Y. Camera Calibration Toolbox for Matlab. Available online: http:\/\/www.vision.caltech.edu\/bouguetj\/calib_doc\/."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","unstructured":"Zhang, Z. (1999, January 20\u201327). Flexible camera calibration by viewing a plane from unknown orientations. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece."},{"key":"ref_6","unstructured":"Sturm, P.F., and Maybank, S.J. (1999, January 23\u201325). On plane-based camera calibration: A general algorithm, singularities, applications. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/JRA.1987.1087109","article-title":"A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses","volume":"3","author":"Tsai","year":"1987","journal-title":"IEEE J. Robot. Autom."},{"key":"ref_8","unstructured":"Harris, C., and Stephens, M. (September, January 31). A combined corner and edge detector. Proceedings of the Alvey Vision Conference, Manchester, UK."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1023\/A:1007963824710","article-title":"SUSAN\u2014A new approach to low level image processing","volume":"23","author":"Smith","year":"1997","journal-title":"Int. J. Comput. Vis."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, W., Ma, C., Xia, L., and Li, X. (2009, January 17\u201319). A fast and accurate algorithm for chessboard corner detection. Proceedings of the IEEE 2nd International Congress on Image and Signal Processing, 2009 (CISP\u201909), Tianjin, China.","DOI":"10.1109\/CISP.2009.5304332"},{"key":"ref_11","unstructured":"Moravec, H.P. (1977, January 22\u201325). Towards Automatic Visual Obstacle Avoidance. Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, MA, USA."},{"key":"ref_12","unstructured":"Arca, S., Casiraghi, E., and Lombardi, G. (April, January 30). Corner localization in chessboards for camera calibration. Proceedings of the International Conference on Multimedia, Image Processing and Computer Vision (IADAT-MICV2005), Madrid, Spain."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Su, J., Duan, X., and Xiao, J. (2013, January 27\u201328). Fast detection method of checkerboard corners based on the combination of template matching and Harris Operator. Proceedings of the IEEE 2013 International Conference on Information Science and Technology (ICIST), Yangzhou, China.","DOI":"10.1109\/ICIST.2013.6747676"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Placht, S., F\u00fcrsattel, P., Mengue, E.A., Hofmann, H., Schaller, C., Balda, M., and Angelopoulou, E. (2014, January 6\u201312). Rochade: Robust checkerboard advanced detection for camera calibration. Proceedings of the 2014 European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_50"},{"key":"ref_15","unstructured":"Vezhnevets, V. OpenCV Calibration Object Detection, Part of the Free Open-Source OpenCV Image Processing Library. Available online: http:\/\/opencv.org\/."},{"key":"ref_16","unstructured":"Scaramuzza, D. OCamCalib: Omnidirectional Camera Calibration Toolbox for Matlab. Available online: https:\/\/sites.google.com\/site\/scarabotix\/ocamcalib-toolbox."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rufli, M., Scaramuzza, D., and Siegwart, R. (2008, January 22\u201326). Automatic detection of checkerboards on blurred and distorted images. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4650703"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1016\/j.amc.2006.05.210","article-title":"Recognition and location of the internal corners of planar checkerboard calibration pattern image","volume":"185","author":"Wang","year":"2007","journal-title":"Appl. Math. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.3390\/s100302027","article-title":"Automatic chessboard detection for intrinsic and extrinsic camera parameter calibration","volume":"10","author":"Armingol","year":"2010","journal-title":"Sensors"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.cviu.2013.10.008","article-title":"ChESS\u2014Quick and robust detection of chess-board features","volume":"118","author":"Bennett","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.patrec.2015.12.008","article-title":"Automated Checkerboard Detection and Indexing using Circular Boundaries","volume":"71","author":"Bok","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"067205","DOI":"10.1117\/1.3156053","article-title":"Automatic detection of chessboard and its applications","volume":"48","author":"Ha","year":"2009","journal-title":"Opt. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rosten, E., and Drummond, T. (2006, January 7\u201313). Machine learning for high-speed corner detection. Proceedings of the European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11744023_34"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/TPAMI.2008.275","article-title":"Faster and better: A machine learning approach to corner detection","volume":"32","author":"Rosten","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","unstructured":"Roels, J., Vylder, J.D., Aelterman, J., Saeys, Y., and Philips, W. (2016, January 12\u201313). Automated membrane detection in electron microscopy using convolutional neural networks. Proceedings of the 25th Belgian-Dutch Conference on Machine Learning, Kortrijk, Belgium."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.neuroimage.2007.05.063","article-title":"Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures","volume":"39","author":"Powell","year":"2008","journal-title":"Neuroimage"},{"key":"ref_27","unstructured":"Donn\u00e9, S., Luong, H., Goossens, B., Dhondt, S., Wuyts, N., Inz\u00e9, D., and Philips, W. (2016, January 12\u201313). Machine learning for maize plant segmentation. Proceedings of the 25th Belgian-Dutch Conference on Machine Learning, Kortrijk, Belgium."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2016, January 12\u201313). Learning a deep convolutional network for image super-resolution. Proceedings of the 2014 European Conference on Computer VISION (ECCV2014), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009 (CVPR 2009), Miami Beach, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_30","unstructured":"LeCun, Y., Cortes, C., and Burges, C.J. The MNIST Database of Handwritten Digits. Available online: http:\/\/yann.lecun.com\/exdb\/mnist."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in Neural Networks: An Overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1080\/00207720010024276","article-title":"Camera calibration and three-dimensional world reconstruction of stereo-vision using neural networks","volume":"32","author":"Memon","year":"2001","journal-title":"Int. J.Syst. Sci."},{"key":"ref_33","unstructured":"Jun, J., and Kim, C. (1999, January 15\u201317). Robust camera calibration using neural network. Proceedings of the IEEE Region 10 Conference (TENCON 99), Cheju Island, Korea."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ahmed, M.T., Hemayed, E.E., and Farag, A.A. (1999, January 20\u201327). Neurocalibration: A neural network that can tell camera calibration parameters. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.791257"},{"key":"ref_35","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the International Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL, USA."},{"key":"ref_36","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_37","unstructured":"Lucchese, L., and Mitra, S.K. (2002, January 28\u201331). Using saddle points for subpixel feature detection in camera calibration targets. Proceedings of the IEEE 2002 Asia-Pacific Conference on Circuits and Systems, 2002 (APCCAS\u201902), Bali, Indonesia."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2010, January 22\u201327). Large-scale machine learning with stochastic gradient descent. Proceedings of 19th International Conference on Computational Statistics (COMPSTAT\u20192010), Paris, France.","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"ref_39","unstructured":"Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Le, Q.V., and Ng, A.Y. (July, January 28). On optimization methods for deep learning. Proceedings of the 28th International Conference on Machine Learning (ICML-11), Bellevue, WA, USA."},{"key":"ref_40","unstructured":"Sutskever, I., Martens, J., Dahl, G., and Hinton, G. (2013, January 16\u201321). On the importance of initialization and momentum in deep learning. Proceedings of the 30th international conference on machine learning (ICML-13), Atlanta, GA, USA."},{"key":"ref_41","unstructured":"Heikkila, J., and Silv\u00e9n, O. (1997, January 17\u201319). A four-step camera calibration procedure with implicit image correction. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1109\/TPAMI.2006.153","article-title":"A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses","volume":"28","author":"Kannala","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Goossens, B., Vylder, J.D., and Philips, W. (2014, January 27\u201330). Quasar: A new heterogeneous programming framework for image and video processing algorithms on CPU and GPU. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7025441"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/11\/1858\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T09:25:54Z","timestamp":1717665954000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/11\/1858"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,4]]},"references-count":43,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2016,11]]}},"alternative-id":["s16111858"],"URL":"https:\/\/doi.org\/10.3390\/s16111858","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,11,4]]}}}