Concept

OpenCV

OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly for real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage, then Itseez (which was later acquired by Intel). The library is cross-platform and licensed as free and open-source software under Apache License 2. Starting in 2011, OpenCV features GPU acceleration for real-time operations. Officially launched in 1999 the OpenCV project was initially an Intel Research initiative to advance CPU-intensive applications, part of a series of projects including real-time ray tracing and 3D display walls. The main contributors to the project included a number of optimization experts in Intel Russia, as well as Intel's Performance Library Team. In the early days of OpenCV, the goals of the project were described as: Advance vision research by providing not only open but also optimized code for basic vision infrastructure. No more reinventing the wheel. Disseminate vision knowledge by providing a common infrastructure that developers could build on, so that code would be more readily readable and transferable. Advance vision-based commercial applications by making portable, performance-optimized code available for free – with a license that did not require code to be open or free itself. The first alpha version of OpenCV was released to the public at the IEEE Conference on Computer Vision and Pattern Recognition in 2000, and five betas were released between 2001 and 2005. The first 1.0 version was released in 2006. A version 1.1 "pre-release" was released in October 2008. The second major release of the OpenCV was in October 2009. OpenCV 2 includes major changes to the C++ interface, aiming at easier, more type-safe patterns, new functions, and better implementations for existing ones in terms of performance (especially on multi-core systems). Official releases now occur every six months and development is now done by an independent Russian team supported by commercial corporations.

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https://en.wikipedia.org/wiki/OpenCV

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Official source

https://en.wikipedia.org/wiki/OpenCV

About this result

Related courses (2)

DH-406: Machine learning for DH

CS-233(a): Introduction to machine learning (BA3)

Related lectures (4)

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Since 2017 Associate Professor, School of Architecture, Civil and Environmental Engineering (ENAC), EPFL. Head of the Earthquake Engineering and Structural Dynamics (EESD) Laboratory 2010-2017 Assistant Professor, EPFL. 2008-2010 Post-doctoral researcher, ETH Zürich. 2003-2007 Ph.D., Roseschool / Università di Pavia, Italy. 2001-2003 Ove Arup & Partners, Advanced Technology and Research Group, London. 2001 Diploma, Civil engineering, ETH Zürich.

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Dr. Christophe Moser is Associate Professor of Microengineering and Industrial Relations at the Swiss Federal Institute of Technology in Lausanne. He was the co-founder and CEO of Ondax,Inc for ten years prior to joining EPFL. During this decade, he raised $15 million from corporate and venture capital sources to fund volume production of thick holographic components in glass and develop devices enabled by these components such as tunable add-drop multiplexers for telecommunications, ultra-narrowband notch filters for low frequency Raman systems, high power semi-conductor frequency-narrowed pumps for fiber lasers, single frequency fixed and tunable lasers for sensing and femtosecond pulse compressors for micro-machining applications. Dr. Moser has a Ph.D. in Electrical Engineering from the California Institute of Technology, a minor in finance and a bachelor degree in Physics from EPFL. He is the co-inventor of 24 patents, 14 peer reviewed scientific publications, 9 IEEE proceedings and a book chapter. Private interests include tennis, running, ski, basketball and family excursions. 2010: Associate Professor, EPFL, faculty Science Technique Ingenieur. 2000-2010: co-founder, CEO Ondax, Inc. 1995-2000: Graduate Research Assistant, California Institute of Technology. 1993-1995: Engineering Project manager , TESA. 1988-1993: Bachelor candidate in Physics, EPFL. MAIN PUBLICATIONS:

Moser C., Havermeyer F,.Ultra-narrowband tunable laserline notch filter, Appl. Phys. B, 95 (3), pp 597-601, 2009.

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Moser C., Maravic I., Schupp B., Adibi A, Psaltis D, Diffraction efficiency of localized holograms in doubly doped LiNbO3 crystals, Opt. Lett. 25: (17),1243-1245, 2000.

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Related concepts (3)

OpenCL

OpenCL (Open Computing Language) is a framework for writing programs that execute across heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors or hardware accelerators. OpenCL specifies programming languages (based on C99, C++14 and C++17) for programming these devices and application programming interfaces (APIs) to control the platform and execute programs on the compute devices.

Video tracking

Video tracking is the process of locating a moving object (or multiple objects) over time using a camera. It has a variety of uses, some of which are: human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging and video editing. Video tracking can be a time-consuming process due to the amount of data that is contained in video. Adding further to the complexity is the possible need to use object recognition techniques for tracking, a challenging problem in its own right.

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.