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Getting Started with Computer Vision System Toolbox


Find Image Rotation and Scale Using Automated Feature Matching

This example shows how to automatically determine the geometric transformation between a pair of images.

Object Detection Using Faster R-CNN Deep Learning

This example shows how to train an object detector using a deep learning technique named Faster R-CNN (Regions with Convolutional Neural Networks).

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.

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.

3-D Point Cloud Registration and Stitching

This example shows how to combine multiple point clouds to reconstruct a 3-D scene using Iterative Closest Point (ICP) algorithm.

About Computer Vision

Local Feature Detection and Extraction

Learn the benefits and applications of local feature detection and extraction

Point Feature Types

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

Label Images for Classification Model Training

Label objects in images.

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.

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