Computer Vision System Toolbox™ provides algorithms, functions, and apps for designing and simulating computer vision and video processing systems. You can perform feature detection, extraction, and matching, as well as object detection and tracking. For 3-D computer vision, the system toolbox supports single, stereo, and fisheye camera calibration; stereo vision; 3-D reconstruction; and 3-D point cloud processing.
Algorithms for deep learning and machine learning enable you to detect faces, pedestrians, and other common objects using pretrained detectors. You can train a custom detector using ground truth labeling with training frameworks such as Faster R-CNN and ACF. You can also classify image categories and perform semantic segmentation.
Algorithms are available as MATLAB® functions, System objects, and Simulink® blocks. For rapid prototyping and embedded system design, the system toolbox supports fixed-point arithmetic and C-code generation.
Deep learning for image classification, object detection, and semantic segmentation
Machine learning using ACF, cascade object detection, and bag-of-features for object detection, object recognition, and image retrieval systems
Object detection and tracking, including the Viola-Jones, Kanade-Lucas-Tomasi (KLT), and Kalman filtering methods
Camera calibration and automation apps for single and stereo cameras, fisheye lens calibration, and automatic checkerboard detection
Stereo vision, including rectification, disparity calculation, and 3-D reconstruction
3-D point cloud processing, including I/O, visualization, registration, denoising, and geometric shape fitting
Support for C-code generation and fixed-point arithmetic (with MATLAB Coder™