Примеры использования Computer vision на Английском языке и их переводы на Русский язык
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Machine learning in computer vision.
Computer vision could minimize damage from forest fires in the Far Eastern Federal District".
Processing of images and computer vision.
Conducting lectures on Computer Vision and Robotics at the YOUTH SCHOOL«ATOMOSHPERE.
Through the Eyes of a Machine: Computer Vision.
MV is the application of computer vision to industry and manufacturing.
Since 2012 Mikhail has also served as the CTO at Computer Vision Systems.
Computer vision and augmented reality application development- now it is easy.
Intelligent Computing, Computer Vision profile.
Our calibration algorithms are calculating the best projection mapping automatically based on computer vision.
Professional interests: computer vision, image processing.
It is an interactive program for image processing and computer vision.
Yet another field related to computer vision is signal processing.
This product is realized as web application for providing the test of computer vision systems.
Professional interests: computer vision, image processing, embedded systems.
OpenCV library- porting to the platform and optimizing the computer vision package.
Computer vision often relies on more or less complex assumptions about the scene depicted in an image.
Konstantin interests include image processing, computer vision, and bioinformatics.
Areas of scientific activity: computer vision systems, image recognition, intelligent analysis of multidimensional data.
He will work with Belarusian companies specializing in computer vision and machine learning.
One of the main events for the computer vision community is the annual conference Computer Vision and Pattern Recognition.
Local binary patterns(LBP)is a type of visual descriptor used for classification in computer vision.
The project combined research in robotics, computer vision, and natural language processing.
Computer vision is substantially improved: ImageIdentify can recognize more than 10,000 objects, and performances of Classify on images are enhanced.
Interdisciplinary exchange between biological and computer vision has proven fruitful for both fields.
By using Computer Vision or Machine Learning algorithms all the images in the database are indexed and prepared for fast sorting and selecting according to set parameters.
Deep learning can be applied to many image processing and computer vision problems with great success.
It's not a trivial task; it involves computer vision, manipulators on mobile platforms, the development of mobile robots, and other elements.
The software module is implemented in the programming language C++ using computer vision library OpenCV, which builds a map.
New computer vision technologies developed by Julian Oliver and Damian Stewart with real-time data processing were used during the seminars.