Computer Vision System Toolbox0 pages

Computer Vision System Toolbox
Design and simulate computer vision and video processing systems
Computer Vision System Toolbox™ provides algorithms and tools for the design and simulation of computer
vision and video processing systems. The toolbox includes algorithms for feature extraction, motion detection,
object detection, object tracking, stereo vision, video processing, and video analysis. Tools include video file I/O,
video display, drawing graphics, and compositing. Capabilities are provided as MATLAB® functions, MATLAB
System objects™, and Simulink® blocks. For rapid prototyping and embedded system design, the system toolbox
supports fixed-point arithmetic and C code generation.
Key Features
▪ Feature detection, including FAST, Harris, Shi & Tomasi, SURF, and MSER detectors
▪ Feature extraction and putative feature matching
▪ Object detection and tracking, including Viola-Jones detection and CAMShift tracking
▪ Motion estimation, including block matching, optical flow, and template matching
▪ RANSAC-based estimation of geometric transformations or fundamental matrices
▪ Video processing, video file I/O, video display, graphic overlays, and compositing
▪ Block library for use in Simulink
Feature Detection and Extraction
A feature is an interesting part of an image, such as a corner, blob, edge, or line. Feature extraction enables you to
derive a set of feature vectors, also called descriptors, from a set of detected features. Computer Vision System
Toolbox offers capabilities for feature detection and extraction that include:
▪ Corner detection, including Shi & Tomasi, Harris, and FAST methods
▪ SURF and MSER detection for blobs and regions
▪ Extraction of simple pixel neighborhood and SURF descriptors
▪ Visualization of feature location, scale, and orientation
Additionally, the system toolbox provides functionality to match two sets of feature vectors and visualize the
results. When combined into a single workflow, feature detection, extraction, and matching can be used to solve
many computer vision design challenges, such as registration, stereo vision, object detection, and tracking.

SURF (left), MSER (center), and corner detection (right) with Computer Vision System Toolbox. Using the same image, the
three different feature types are detected and results are plotted over the original image.

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