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Computer Vision Toolbox

Design and test computer vision systems

Computer Vision Toolbox™ provides algorithms and apps for designing and testing computer vision systems. You can perform visual inspection, object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports stereo vision, point cloud processing, structure from motion, and real-time visual and point cloud SLAM. Computer vision apps enable team-based ground truth labeling with automation, as well as camera calibration.

You can use pretrained object detectors or train custom detectors using deep learning and machine learning algorithms such as YOLO, SSD, and ACF. For semantic and instance segmentation, you can use deep learning algorithms such as U-Net, SOLO, and Mask R-CNN. You can perform image classification using vision transformers such as ViT. Pretrained models let you detect faces and pedestrians, perform optical character recognition (OCR), and recognize other common objects.

You can accelerate your algorithms by running them on multicore processors and GPUs. Toolbox algorithms support C/C++ code generation for integrating with existing code, desktop prototyping, and embedded vision system deployment.

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Learn the basics of Computer Vision Toolbox

Feature Detection and Extraction

Image registration, interest point detection, feature descriptor extraction, point feature matching, and image retrieval

Image and Video Ground Truth Labeling

Interactive image and video labeling, create training data for deep learning with object detection, semantic segmentation, instance segmentation, and image classification

Recognition, Object Detection, and Semantic Segmentation

Recognition, classification, semantic image segmentation, instance segmentation, object detection using features, and deep learning object detection using CNNs, YOLO, and SSD

Camera Calibration

Calibrate single or stereo cameras and estimate camera intrinsics, extrinsics, and distortion parameters using pinhole and fisheye camera models

Structure from Motion and Visual SLAM

Stereo vision, triangulation, 3-D reconstruction, and visual simultaneous localization and mapping (vSLAM)

Point Cloud Processing

Preprocess, visualize, register, fit geometrical shapes, build maps, implement SLAM algorithms, and use deep learning with 3-D point clouds

Tracking and Motion Estimation

Optical flow, activity recognition, motion estimation, object re-identification, and tracking

Code Generation, GPU, and Third-Party Support

C/C++ and GPU code generation and acceleration, HDL code generation, and OpenCV interface for MATLAB and Simulink

Computer Vision with Simulink

Simulink® support for computer vision applications