Computer Vision System Toolbox

Feature-Based Registration

Computer Vision System Toolbox supports automatic image registration by providing algorithms that use features to estimate the geometric relationships between images or video frames. Typical uses include video mosaicking, video stabilization, image fusion, and stereo vision.

Feature Detection, Extraction, and Matching

Feature detection, extraction, and matching are the first steps in the feature-based registration workflow. Features are a set of interest points that are likely to be common across a pair of related images. Feature extraction enables you to derive a set of feature vectors, also called descriptors, from pixels surrounding each interest point. The system toolbox offers capabilities to detect features that include corners, edges, and lines; extract descriptors from the features; and find the most likely paired matches of descriptors.

Estimating Geometric Relationships

With a set of matched interest points, you can infer the geometric relationship between two images or video frames. The feature detection, extraction, and matching workflow produces many interest points and typically includes outliers. To estimate the geometric relationship with this set of interest points, you can exclude outliers with statistically robust methods such as RANSAC and least median of squares. With this workflow, the system toolbox can produce a projective or affine transformation that describes the geometric relationship between a pair of images or video frames.

Feature-based registration, as used for video stabilization. Interest points are detected in two sequential video frames (top) using corner features; the putative matches are determined with numerous outliers (bottom left); and outliers are removed using the RANSAC method (bottom right).