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Getting Started with Computer Vision System Toolbox


Local Feature Detection and Extraction

Learn the benefits and applications of local feature detection and extraction

Label Images for Classification Model Training

Label objects in images.

Image Classification with Bag of Visual Words

Use the Computer Vision System Toolbox™ functions for image category classification by creating a bag of visual words.

Single Camera Calibration App

Estimate camera intrinsics, extrinsics, and lens distortion parameters.

Stereo Calibration App

Calibrate a stereo camera, which you can then use to recover depth from images.

Common Applications

Feature Detection and Extraction

Detect SURF Interest Points in a Grayscale Image

Detect and display speeded-up robust features (SURF) interest points.

Extract and Plot HOG Features

Extract histogram of gradient (HOG) features from an image.

Automatically Detect and Recognize Text in Natural Images

This example shows how to detect regions in an image that contain text.

Object Detection, Registration, and Tracking

Face Detection and Tracking Using CAMShift

This example shows how to automatically detect and track a face.

Motion-Based Multiple Object Tracking

This example shows how to perform automatic detection and motion-based tracking of moving objects in a video from a stationary camera.

Object Detection in a Cluttered Scene Using Point Feature Matching

This example shows how to detect a particular object in a cluttered scene, given a reference image of the object.

Tracking Pedestrians from a Moving Car

This example shows how to track pedestrians using a camera mounted in a moving car.

Camera Calibration and Stereo Vision

Evaluating the Accuracy of Single Camera Calibration

This example shows how to evaluate the accuracy of camera parameters estimated using the cameraCalibrator app or the estimateCameraParameters function.

Measuring Planar Objects with a Calibrated Camera

This example shows how to measure the diameter of coins in world units using a single calibrated camera.

Structure From Motion From Two Views

Structure from motion (SfM) is the process of estimating the 3-D structure of a scene from a set of 2-D images.

Structure From Motion From Multiple Views

Structure from motion (SfM) is the process of estimating the 3-D structure of a scene from a set of 2-D views.

Conventions and Preferences

Point Feature Types

Choose functions that return and accept points objects for several types of features

Coordinate Systems

Specify pixel Indices, spatial coordinates, and 3-D coordinate systems

Computer Vision System Toolbox Preferences

Set Computer Vision System Toolbox preferences to enable parallel computing on supported functions.

Block Data Type Support

The Computer Vision System Toolbox Data Type Support Table is available through the Simulink® model Help menu.

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