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Computer Vision System Toolbox provides algorithms and tools for the design and simulation of
computer vision and video processing systems.
You can detect objects in images and video,
track detected objects in video frames,
calibrate cameras,
and perform stereo vision.
Computer vision uses images and video to
detect,
classify,
and track objects or events in order to understand a real-world scene.
Object detection is the process of finding instances of real objects such as
faces,
people,
road signs,
or license plates.
You can use pre-trained object detectors to
detect people,
faces,
and facial features.
You can also create object detectors to locate any object of interest by selecting and assigning regions of interest in training images.
The system toolbox also includes over fifty Simulink blocks.
As shown in this example where example where motion information is used to detect objects in video.
Object tracking is the association of detected objects in adjacent frames of video.
In this example you can see how a face is tracked using feature point tracking.
The system toolbox also provides a framework that you can use to track multiple objects using Kalman filters.
Feature detection,
extraction,
and matching,
is a useful workflow for computer vision, and can be used for applications such as
detecting objects in a cluttered scene,
and stitching multiple images together to form a panorama.
Camera calibration is often used to find characteristics internal to a camera.
You can use this information to correct for optical distortions,
or establish the actual size of objects in images.
Camera calibration also estimates a camera’s location in space; this is a precursor to stereo and 3D vision.
With stereo images you can calculate the depth of points in the scene , and reconstruct a 3D model of the scene.
Computer vision system toolbox is for use with MATLAB and Simulink. For more information on the Computer Vision System toolbox return to
the product page or choose a link below.