Computer Vision System Toolbox
Computer Vision System Toolbox provides a suite of feature detectors and descriptors. 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 image registration, stereo vision, object detection, and tracking.
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:
Feature matching is the comparison of two sets of feature descriptors obtained from different images to provide point correspondences between images. Computer Vision System Toolbox offers functionality for feature matching that includes:
Statistically robust methods like RANSAC can be used to filter outliers in matched feature sets while estimating the geometric transformation or fundamental matrix, which is useful when using feature matching for image registration, object detection, or stereo vision applications.
Image registration is the transformation of images from different camera views to use a unified co-coordinate system. Computer Vision System Toolbox supports an automatic approach to image registration by using features. Typical uses include video mosaicking, video stabilization, and image fusion.
Feature detection, extraction, and matching are the first steps in the feature-based automatic image registration workflow. You can remove the outliers in the matched feature sets using RANSAC to compute the geometric transformation between images and then apply the geometric transformation to align the two images.
Object detection and recognition are used to locate, identify, and categorize objects in images and video. Computer Vision System Toolbox provides a comprehensive suite of algorithms and tools for object detection and recognition.
You can detect or recognize an object in an image by training an object classifier using pattern recognition algorithms that create classifiers based on training data from different object classes. The classifier accepts image data and assigns the appropriate object or class label.
Face detection using Viola-Jones algorithm
Using a cascade of classifiers to detect faces
Detecting people using pretrained support vector machine(SVM) with histogram of oriented gradient (HOG) features
Text detection and optical character recognition (OCR)
Recognizing text in natural images
Classifying digits using support vector machines (SVM) and HOG feature extraction
Motion-based object detection algorithms use motion extraction and segmentation techniques such as optical flow and Gaussian mixture model (GMM) foreground detection to locate moving objects in a scene. Blob analysis is used to identify objects of interest by computing the blob properties from the output of a segmentation or motion extraction algorithm such as background subtraction.
Feature points are used for object detection by detecting a set of features in a reference image, extracting feature descriptors, and matching features between the reference image and an input. This method of object detection can detect reference objects despite scale and orientation changes and is robust to partial occlusions.
Training is the process of creating an object detector or classifier to detect or recognize a specific object of interest. The training process utilizes:
The system toolbox provides an app to select and assign regions of interest (ROI) and label training images.
Computer vision often involves the tracking of moving objects in video. Computer Vision System Toolbox provides a comprehensive set of algorithms and functions for object tracking and motion estimation tasks.
Computer Vision System Toolbox provides video tracking algorithms, such as continuously adaptive mean shift (CAMShift) and Kanade-Lucas-Tomasi (KLT). You can use these algorithms for tracking a single object or as building blocks in a more complex tracking system. The system toolbox also provides a framework for multiple object tracking that includes Kalman filtering and the Hungarian algorithm for assigning object detections to tracks.
CAMShift uses a moving rectangular window that traverses the back projection of an object’s color histogram to track the location, size, and orientation of the object from frame to frame.
Computer Vision System Toolbox provides an extensible framework to track multiple objects in a video stream and includes the following to facilitate multiple object tracking:
Motion estimation is the process of determining the movement of blocks between adjacent video frames. The system toolbox provides a variety of motion estimation algorithms, such as optical flow, block matching, and template matching. These algorithms create motion vectors, which relate to the whole image, blocks, arbitrary patches, or individual pixels. For block and template matching, the evaluation metrics for finding the best match include MSE, MAD, MaxAD, SAD, and SSD.
Camera calibration is the estimation of a camera’s intrinsic, extrinsic, and lens-distortion parameters. Typical uses of a calibrated camera are correction of optical distortion artifacts, estimating distance of an object from a camera, measuring the size of objects in an image, and constructing 3D views for augmented reality systems.
Computer Vision System Toolbox provides an app and functions to perform all essential tasks in the camera calibration workflow:
The Camera Calibrator app is used to select and filter calibration images, choose the number and type of radial distortion coefficients, view reprojection errors, visualize extrinsic parameters, and export camera calibration parameters.
Camera Calibration with MATLAB
Explore camera calibration capabilities in MATLAB®. Calibrate a camera using the camera calibrator app, perform image undistortion, and measure the actual size of an object using a calibrated camera.
Stereo vision is the process of extracting the 3D structure of a scene from multiple 2D views.
Computer Vision System Toolbox provides functions and algorithms to complete the following steps in the stereo vision workflow:
Stereo calibration is the process of finding the intrinsic and extrinsic parameters of a pair of cameras, as well as the relative positions and orientations of the cameras. Stereo calibration is a precursor to calibrated stereo rectification and 3D scene reconstruction. Computer Vision System Toolbox provides algorithms and functions to calibrate a pair of stereo cameras using a checkerboard calibration pattern.
Stereo image rectification transforms a pair of stereo images so that a corresponding point in one image can be found in the corresponding row in the other image. This process reduces the 2-D stereo correspondence problem to a 1-D problem, and it simplifies how to determine the depth of each point in the scene. Computer Vision System Toolbox provides functionality for stereo rectification that includes:
The relative depths of points in a scene are represented in a stereo disparity map which is calculated by matching corresponding points in a pair of rectified stereo images. The system toolbox provides algorithms for disparity calculation including:
You can reconstruct the 3D structure of a scene by projecting the 2D contents of a scene to three dimensions using the disparity map and information from stereo calibration.
Computer Vision System Toolbox provides algorithms and tools for video processing. You can read and write from common video formats, apply common video processing algorithms such as deinterlacing and chroma-resampling, and display results with text and graphics burnt into the video. Video processing in MATLAB uses System objects™, which avoids excessive memory use by streaming data for processing one frame at a time.
Computer Vision System Toolbox can read and write multimedia files in a wide range of formats, including AVI, MPEG, and WMV. You can stream video to and from MMS sources over the Internet or a local network. You can acquire video directly from web cameras, frame grabbers, DCAM-compatible cameras, and other imaging devices using Image Acquisition Toolbox™. Simulink users can use the MATLAB workspace as a video source or sink.
The system toolbox includes a video viewer that lets you:
Adding graphics to video helps with visualizing extracted information or debugging a system design. You can insert text to display the number of objects or to keep track of other key information. You can insert graphics, such as markers, lines, and polygons to mark found features, delineate objects, or highlight other key features. The system toolbox fuses text and graphics into the image or video itself rather than maintaining a separate layer. You can combine two video sources in a composite that can highlight objects or a key region.
Computer Vision System Toolbox supports the creation of system-level test benches, fixed-point modeling, and code generation within MATLAB and Simulink. This support lets you integrate algorithm development with rapid prototyping, implementation, and verification workflows.
Most System objects, functions, and blocks in Computer Vision System Toolbox can generate ANSI/ISO C code using MATLAB Coder™, Simulink Coder™, or Embedded Coder™. You can select optimizations for specific processor architectures and integrate legacy C code with the generated code to leverage existing intellectual property. You can generate C code for both floating-point and fixed-point data types. The system toolbox ships with an example that shows how to convert an algorithm created in MATLAB to C code using code generation.
Many real-time systems use hardware that requires fixed-point representation of your algorithm. Computer Vision System Toolbox supports fixed-point modeling in most blocks and System objects, with dialog boxes and object properties that help you with configuration.
System toolbox support for fixed point includes:
Computer Vision System Toolbox includes image processing primitives that support fixed-point data types and C-code generation. These System objects and Simulink blocks include: