What Is Computer Vision Toolbox? - MATLAB & Simulink
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    What Is Computer Vision Toolbox?

    Computer Vision Toolbox™ provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. For 3D vision, the toolbox supports single, stereo, and fisheye camera calibration; stereo vision; 3D reconstruction; and lidar and 3D point cloud processing. Computer vision apps automate ground truth labeling and camera calibration workflows.

    Published: 26 Jan 2021

    Computer Vision Toolbox provides algorithms and tools for designing and testing systems for computer vision, 3D vision, and video processing. You can perform object detection and tracking workflows using feature detection extraction and matching. For 3D vision, you can perform camera calibration for single, stereo, and fisheye cameras.

    You can use the camera data to do visual SLAM. This example uses Orb SLAM to build a map of the environment and estimate the camera's trajectory. You can also perform tasks related to stereo vision, 3D reconstruction, and LiDAR endpoint cloud processing.

    Computer Vision Toolbox supports ground truth labeling, including automation workflows that can accelerate the labeling process. You can use that label data to implement deep learning and machine learning algorithms. For example, you can train a deep neural network object detector using an algorithm like YOLOv2, Faster R-CNN, and ACF. You also have the option of using pre-trained models that detect faces, pedestrians, and other common objects.

    If you're doing semantic segmentation, you can use models like segnet, unet, and deep lab. All of these algorithms and more can be accelerated by running them on multicore processors and GPUs. Most functions have support for C and C# code generation for embedded vision system deployment. For more information about the Computer Vision Toolbox, please return to the product page.