OpenCV was designed for computational ef iiency and with a strong focus on realtime applications. OpenCV is written in optimized C and can take advantage of multicore processors. If you desire further automatic optimization on Intel architectures
[Intel], you can buy Intel’s Integrated Performance Primitives (IPP) libraries [IPP], which
consist of low-level optimized routines in many dif erent algorithmic areas. OpenCV
automatically uses the appropriate IPP library at runtime if that library is installed.
One of OpenCV’s goals is to provide a simple-to-use computer vision infrastructure
that helps people build fairly sophisticated vision applications quickly. T e OpenCV
library contains over 500 functions that span many areas in vision, including factory
product inspection, medical imaging, security, user interface, camera calibration, stereo
vision, and robotics. Because computer vision and machine learning of en go hand-inhand, OpenCV also contains a full, general-purpose Machine Learning Library (MLL).
T is sublibrary is focused on statistical pattern recognition and clustering. T e MLL is
highly useful for the vision tasks that are at the core of OpenCV’s mission, but it is general enough to be used for any machine learning problem.
OpenCV was designed for computational ef iiency and with a strong focus on realtime applications. OpenCV is written in optimized C and can take advantage of multicore processors. If you desire further automatic optimization on Intel architectures
[Intel], you can buy Intel’s Integrated Performance Primitives (IPP) libraries [IPP], which
consist of low-level optimized routines in many dif erent algorithmic areas. OpenCV
automatically uses the appropriate IPP library at runtime if that library is installed.
One of OpenCV’s goals is to provide a simple-to-use computer vision infrastructure
that helps people build fairly sophisticated vision applications quickly. T e OpenCV
library contains over 500 functions that span many areas in vision, including factory
product inspection, medical imaging, security, user interface, camera calibration, stereo
vision, and robotics. Because computer vision and machine learning of en go hand-inhand, OpenCV also contains a full, general-purpose Machine Learning Library (MLL).
T is sublibrary is focused on statistical pattern recognition and clustering. T e MLL is
highly useful for the vision tasks that are at the core of OpenCV’s mission, but it is general enough to be used for any machine learning problem.
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