OpenCV

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 7 million. The library is used extensively in companies, research groups and by governmental bodies. ..


References in zbMATH (referenced in 108 articles )

Showing results 1 to 20 of 108.
Sorted by year (citations)

1 2 3 4 5 6 next

  1. Abdellali, Hichem; Kato, Zoltan: 3D reconstruction with depth prior using graph-cut (2021)
  2. Chen, Yida; Faden, Eric; Ryan, Nathan: KALMUS: tools for color analysis of films (2021) not zbMATH
  3. Christopher P. Bridge, Chris Gorman, Steven Pieper, Sean W. Doyle, Jochen K. Lennerz, Jayashree Kalpathy-Cramer, David A. Clunie, Andriy Y. Fedorov, Markus D. Herrmann: Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology (2021) arXiv
  4. Eriksson, Jens; Styrström, Daniel; Sellin, Mikael E.: Cellocity: A Python package for analysis of confluent cell layer dynamics (2021) not zbMATH
  5. Jon Schwenk; Jayaram Hariharan: RivGraph: Automatic extraction and analysis of river and delta channel network topology (2021) not zbMATH
  6. Partha Pratim Das, Muthu Ram Prabhu Elenchezhian, Vamsee Vadlamudi, Kenneth Reifsnider, Rassel Raihan: RealPi2dDIC: A Low-cost and open-source approach to in situ 2D Digital Image Correlation (DIC) applications (2021) not zbMATH
  7. Philippe Apparicio, David Maignan, Jérémy Gelb: VIFECO: An Open-Source Software for Counting Features on a Video (2021) not zbMATH
  8. Pulido, Jesus; da Silva, Ricardo Dutra; Livescu, Daniel; Hamann, Bernd: Multiresolution classification of turbulence features in image data through machine learning (2021)
  9. Chunggi Lee, Sanghoon Kim, Dongyun Han, Hongjun Yang, Young-Woo Park, Bum Chul Kwon, Sungahn Ko: GUIComp: A GUI Design Assistant with Real-Time, Multi-Faceted Feedback (2020) arXiv
  10. Cohen, Raymond C. Z.; Harrison, Simon M.; Cleary, Paul W.: Dive mechanic: bringing 3D virtual experimentation using biomechanical modelling to elite level diving with the Workspace workflow engine (2020)
  11. Freudenthaler, Gerhard; Meurer, Thomas: PDE-based multi-agent formation control using flatness and backstepping: analysis, design and robot experiments (2020)
  12. Hueber, Thomas; Tatulli, Eric; Girin, Laurent; Schwartz, Jean-Luc: Evaluating the potential gain of auditory and audiovisual speech-predictive coding using deep learning (2020)
  13. Khan, Hassan Ali; Jue, Wu; Mushtaq, Muhammad; Mushtaq, Muhammad Umer: Brain tumor classification in MRI image using convolutional neural network (2020)
  14. Ruman Gerst; Anna Medyukhina; Marc Thilo Figge: MISA++: A standardized interface for automated bioimage analysis (2020) not zbMATH
  15. Thompson, S., Dowrick, T., Xiao, G., Ramalhinho, J., Robu, M., Ahmad, M., Taylor, D., Clarkson, M.J.: SnappySonic: An Ultrasound Acquisition Replay Simulator (2020) not zbMATH
  16. Ainsworth, Mark; Tugluk, Ozan; Whitney, Ben; Klasky, Scott: Multilevel techniques for compression and reduction of scientific data-quantitative control of accuracy in derived quantities (2019)
  17. Brito, Darlan N.; Pádua, Flávio L. C.; Lopes, Aldo P. C.: Using geometric interval algebra modeling for improved three-dimensional camera calibration (2019)
  18. Chandra, Rohan; Bhattacharya, Uttaran; Roncal, Christian; Bera, Aniket; Manocha, Dinesh: RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs (2019) arXiv
  19. Davide Micieli, Triestino Minniti, Giuseppe Gorini: NeuTomPy toolbox, a Python package for tomographic data processing and reconstruction (2019) not zbMATH
  20. Demiröz, Barış Evrim; Altınel, İ. Kuban; Akarun, Lale: Rectangle blanket problem: binary integer linear programming formulation and solution algorithms (2019)

1 2 3 4 5 6 next


Further publications can be found at: http://opencv.org/publications.html