| 词条 | Dlib |
| 释义 |
| logo = Dlib c++ library logo.png | author = Davis E. King | released = {{Start date|2002}} | latest release version = 19.17 | latest release date = {{Start date and age|2019|3|10}}[1] | programming language = C++ | operating system = Cross-platform | genre = Library, machine learning | license = Boost }} Dlib is a general purpose cross-platform software library written in the programming language C++. Its design is heavily influenced by ideas from design by contract and component-based software engineering. Thus it is, first and foremost, a set of independent software components. It is open-source software released under a Boost Software License. Since development began in 2002, Dlib has grown to include a wide variety of tools. As of 2016, it contains software components for dealing with networking, threads, graphical user interfaces, data structures, linear algebra, machine learning, image processing, data mining, XML and text parsing, numerical optimization, Bayesian networks, and many other tasks. In recent years, much of the development has been focused on creating a broad set of statistical machine learning tools and in 2009 Dlib was published in the Journal of Machine Learning Research.[2] Since then it has been used in a wide range of domains.[3][4][5][6][7][8][9][10][11][12][13][14][15] See also
References1. ^{{cite web |url=http://dlib.net/release_notes.html |title=Release notes |website=Dlib|accessdate=11 March 2019}} 2. ^{{cite journal |last1=King |first1=D. E. |year=2009 |title=Dlib-ml: A Machine Learning Toolkit |journal=J. Mach. Learn. Res. |volume=10 |issue=Jul |pages=1755–1758 |url=http://www.jmlr.org/papers/volume10/king09a/king09a.pdf |citeseerx=10.1.1.156.3584}} 3. ^[https://scholar.google.com/scholar?q=%22dlib.net%22+OR+%22dlib-ml%22+OR+%22dclib.sourceforge.net%22 Scholarly research using Dlib] 4. ^Dlib on mloss.org 5. ^[https://books.google.com/books?id=DgkeBAAAQBAJ&dq=dlib+c%2B%2B Autonome Mobile Systeme 2009] 6. ^ESS: Extremely Simple Serialization for C++ 7. ^{{cite journal |last1=Gould |first1=S. |year=2012 |title=Darwin: A Framework for Machine Learning and Computer Vision Research and Development |journal=J. Mach. Learn. Res. |volume=13 |issue=Dec |pages=3533–3537 |url=http://www.jmlr.org/papers/volume13/gould12a/gould12a.pdf |citeseerx=10.1.1.413.8518}} 8. ^Yan, Junchi, et al. "Online incremental regression for electricity price prediction." Service Operations and Logistics, and Informatics (SOLI), 2012 IEEE International Conference on. IEEE, 2012. {{Cite book |doi= 10.1109/SOLI.2012.6273500| chapter= Online incremental regression for electricity price prediction| title= Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics| pages= 31| year= 2012| last1= Yan |first1= J. |last2= Tian |first2= C. |last3= Wang |first3= Y. |last4= Huang |first4= J. |isbn= 978-1-4673-2401-4}} 9. ^Kuijf, Hugo J., Max A. Viergever, and Koen L. Vincken. "Automatic Extraction of the Curved Midsagittal Brain Surface on MR Images." Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. Springer Berlin Heidelberg, 2013. 225-232. {{Cite book |doi= 10.1007/978-3-642-36620-8_22| chapter= Automatic Extraction of the Curved Midsagittal Brain Surface on MR Images| title= Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging| volume= 7766| pages= 225| series= Lecture Notes in Computer Science| year= 2013| last1= Kuijf |first1= H. J. |last2= Viergever |first2= M. A. |last3= Vincken |first3= K. L. |isbn= 978-3-642-36619-2}} 10. ^Bormann, Richard Klaus Eduard. "Vision-based place categorization." (2010). 11. ^Brodu, Nicolas, and Dimitri Lague. "3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology." ISPRS Journal of Photogrammetry and Remote Sensing 68 (2012): 121–134. 12. ^Aung, Zeyar, et al. "Towards accurate electricity load forecasting in smart grids." DBKDA 2012, The Fourth International Conference on Advances in Databases, Knowledge, and Data Applications. 2012. 13. ^Rodriguez, Alberto, et al. "Abort and retry in grasping." Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on. IEEE, 2011. {{Cite book |doi= 10.1109/IROS.2011.6095100| chapter= Abort and retry in grasping| title= 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems| pages= 1804| year= 2011| last1= Rodriguez |first1= A. |last2= Mason |first2= M. T. |last3= Srinivasa |first3= S. S. |last4= Bernstein |first4= M. |last5= Zirbel |first5= A. |isbn= 978-1-61284-456-5}} 14. ^Mohan, Vandana, et al. "Intraoperative prediction of tumor cell concentration from Mass Spectrometry Imaging." Int. Symp. Math. Theo. Netw. Syst. 2010. 15. ^Nakashima, Yuta, Noboru Babaguchi, and Jianping Fan. "Detecting intended human objects in human-captured videos." Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on. IEEE, 2010. {{Cite book |doi= 10.1109/CVPRW.2010.5543721| chapter= Detecting intended human objects in human-captured videos| title= 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops| pages= 33| year= 2010| last1= Nakashima |first1= Y. |last2= Babaguchi |first2= N. |last3= Fan |first3= J. |isbn= 978-1-4244-7029-7}} External links
7 : C++ libraries|C++ numerical libraries|Computer vision software|Data mining and machine learning software|Free software programmed in C++|Free statistical software|Software using the Boost license |
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