Get Started with Computer Vision Toolbox
R2026bComputer Vision Toolbox™ provides algorithms, apps, and AI models for designing, simulating, calibrating, and deploying computer vision systems. You can perform object detection and tracking, feature matching, and optical flow. You can automate camera calibration, including multi-sensor configurations. For 3D vision, the toolbox supports structure from motion, real-time SLAM, and novel view synthesis such as NeRF. Computer vision apps enable calibration and multi-user image and video labeling, including automation capabilities.
The toolbox provides AI techniques, including pretrained convolutional neural networks, vision transformers, and vision-language models. You can use these pre-trained models for tasks such as image classification, object detection, segmentation, pose estimation, image captioning, visual question answering, and OCR, or further customize them through transfer learning.
You can generate code in C/C++ and HDL, and for GPU or NPU hardware targets (with MATLAB® Coder™, HDL Coder™, GPU Coder™, and hardware support packages). You can also build custom apps (with MATLAB Compiler™)
Tutorials
- What Is Camera Calibration?
Estimate the parameters of a lens and image sensor of an image or video camera.
- What Is Structure from Motion?
Estimate three-dimensional structures from two-dimensional image sequences.
- Get Started with Object Detection Using Deep Learning
Perform object detection using deep learning neural networks such as YOLOX, YOLO v4, RTMDet, and SSD.
- Get Started with Semantic Segmentation Using Deep Learning
Segment objects by class using deep learning networks such as U-Net and DeepLab v3+.
- Get Started with Code Generation, Deployment, GPU, and OpenCV Support
C/C++ and GPU code generation and acceleration, HDL code generation, and OpenCV interface for MATLAB and Simulink.
- Computer Vision Toolbox with Simulink
Simulink® support for computer vision applications.
App and Workflow Decision Guides
- Choose an App to Label Ground Truth Data
Decide which app to use to label ground truth data: Image Labeler, Video Labeler, Multi-Sensor Labeler, Signal Labeler, or Medical Image Labeler.
- Choose an Object Detector
Compare object detection deep learning models, such as YOLOX, YOLO v4, RTMDet, and SSD.
- Choose SLAM Workflow Based on Sensor Data
Choose the right simultaneous localization and mapping (SLAM) workflow and find topics, examples, and supported features.
- Choose a Point Cloud Viewer (Point Cloud Toolbox)
Compare visualization functions.
Featured Examples
Semantic Segmentation Using Deep Learning
Segment an image using a semantic segmentation network.
Monocular Visual Simultaneous Localization and Mapping
Visual simultaneous localization and mapping (vSLAM).
Multiclass Object Detection Using YOLO v2 Deep Learning
Train a YOLO v2 multiclass object detector and evaluate object detector performance across selected classes and overlap thresholds.
Structure from Motion from Multiple Views
Estimate 3-D structure of a scene from a set of 2-D views using incremental structure from motion.
3-D Point Cloud Registration and Stitching
Combine multiple point clouds to reconstruct a 3-D scene using Iterative Closest Point (ICP) algorithm. It then shows how to leverage color information available in the point clouds to improve the accuracy of the scene.
Find Object in Cluttered Scene Using Image Point Features
Detect a particular object in a cluttered scene, given a reference image of the object.
Measuring Planar Objects with a Calibrated Camera
Measure the diameter of coins in world units using a single calibrated camera.
Automatically Find Image Rotation and Scale
Demonstrates how to automatically determine the geometric transformation between two images. Specifically, when one image is distorted in relation to another due to rotation and scaling, the functions detectSIFTFeatures and estgeotform2d can be employed to identify the rotation angle and scale factor. Subsequently, these parameters can be used to transform the distorted image back to its original appearance.
Motion-Based Multiple Object Tracking
Perform automatic detection and motion-based tracking of moving objects in a video from a stationary camera.
Create Panorama
Automatically stitch multiple images into panorama. The procedure for image stitching is an extension of feature based image registration. Instead of registering a single pair of images, multiple image pairs are successively registered relative to each other to form a panorama.
Interactive Learning
Computer Vision Onramp
Learn how to use Computer Vision Toolbox for object detection and tracking.
Videos
What Is Computer Vision?
Discover how computer vision can be applied to a wide variety of
application areas such as object detection, tracking, and
recognition.
Camera Calibration in MATLAB
Automate checkerboard detection and calibrate pinhole and fisheye cameras
using the Camera Calibrator app
Teaching Resources
Computer Vision Basics
Learn the fundamentals of image segmentation in computer vision.
