Computer Vision mit Simulink
Verwenden Sie Computer Vision Toolbox™-Blöcke zur Modellerstellung für Computer-Vision-Anwendungen. Führen Sie Merkmaldetektion, Bildanalyse, FIR-Filterung, Frequenz- und Hough-Transformationen, Morphologie, Kontrastverstärkung und Entrauschung durch.
Lokale Merkmale und deren Deskriptoren sind die grundlegenden Bausteine vieler Computer-Vision-Algorithmen. Ihre Anwendungen umfassen Bildregistrierung, Objekterkennung und -Klassifizierung, Tracking und Bewegungsschätzung.
Bewegungsschätzung und -Tracking sind Schlüsselprozesse in Anwendungen wie Aktivitätserkennung, Verkehrsüberwachung, Automobilsicherheit und Überwachung.
Mit Analyse- und Verbesserungstechniken können Sie das Signal-Rausch-Verhältnis verbessern und Merkmale hervorheben.
Die showvipblockdatatypetable-Funktion umfasst Details zu Blockfunktionen, Beschränkungen bei der Codegenerierung, Variablen-Größenbestimmung und unterstützte Datentypen für alle Computer Vision Toolbox-Blöcke.
Blöcke
Objekte
Simulink.ImageType | Specify image data type (Seit R2021b) |
Themen
- Video Formats
Video data is a series of images over time.
- Image Formats
In the Computer Vision Toolbox software, images are real-valued ordered sets of color or intensity data.
- Fixed-Point Signal Processing
Discusses advantages of fixed-point development in general and of fixed-point support in System Toolbox software in particular, as well as lists common applications of fixed-point signal processing development.
- Fixed-Point Concepts and Terminology
Defines fixed-point concepts and terminology that are helpful to know as you use DSP System Toolbox™ software.
- Arithmetic Operations
Describes the arithmetic operations used by fixed-point DSP System Toolbox blocks, including operations and casts that might invoke rounding and overflow handling methods.
- Fixed-Point Support for MATLAB System Objects
Enable fixed-point support for Computer Vision Toolbox system objects.
- Specify Fixed-Point Attributes for Blocks (DSP System Toolbox)
Teaches you how to specify fixed-point attributes and parameters in software on both the block and system levels.
- Visualize Point Cloud Sequence
This example shows how to visualize a streaming point cloud sequence by using a Point Cloud Viewer block.
Enthaltene Beispiele
Track Marker Using Simulink Images
Track a marker on a whiteboard by using images of the Simulink.ImageType data type.
Multicore Simulation of Video Processing System
Run a video processing system on multiple cores using dataflow execution domain in Simulink®.
Color-based Road Tracking
Use color information to detect and track road edges set in primarily residential settings where lane markings may not be present. The Color-based Tracking example illustrates how to use the Color Space Conversion block, the Hough Transform block, and the Kalman Filter block to detect and track information using hue and saturation.
Lane Departure Warning System
Detect and track road lane markers in a video sequence and notifies the driver if they are moving across a lane. The example illustrates how to use the Hough Transform, Hough Lines and Kalman Filter blocks to create a line detection and tracking algorithm. The example implements this algorithm using the following steps: 1) Detect lane markers in the current video frame. 2) Match the current lane markers with those detected in the previous video frame. 3) Find the left and right lane markers. 4) Issue a warning message if the vehicle moves across either of the lane markers.
Tracking Cars Using Foreground Detection
Detect and count cars in a video sequence using Gaussian mixture models (GMMs).
Tracking Based on Color
Track a person's face and hand using a color-based segmentation method.
Abandoned Object Detection
Track objects at a train station and to determine which ones remain stationary. Abandoned objects in public areas concern authorities since they might pose a security risk. Algorithms, such as the one used in this example, can be used to assist security officers monitoring live surveillance video by directing their attention to a potential area of interest.
Tracking Cars Using Optical Flow
Detect and track cars in a video sequence using optical flow estimation.
Concentricity Inspection
Inspect the concentricity of both the core and the cladding in a cross-section of optical fiber. Concentricity is a measure of how centered the core is within the cladding.
Motion Detection
Use sum of absolute differences (SAD) method for detecting motion in a video sequence. This example applies SAD independently to four quadrants of a video sequence. If motion is detected in a quadrant, the example highlights the quadrant in red.
Pattern Matching
Use the 2-D normalized cross-correlation for pattern matching and target tracking.
Object Counting
Use basic morphological operators to extract information from a video stream. In this case, the model counts the number of staples in each video frame. Note that the focus and lighting change in each video frame.
Scene Change Detection
Segment video in time. The algorithm in this example can be used to detect major changes in video streams, such as when a commercial begins and ends. It can be useful when editing video or when you want to skip ahead through certain content.
Surveillance Recording
Process surveillance video to select frames that contain motion. Security concerns mandate continuous monitoring of important locations using video cameras. To efficiently record, review, and archive this massive amount of data, you can either reduce the video frame size or reduce the total number of video frames you record. This example illustrates the latter approach. In it, motion in the camera's field of view triggers the capture of "interesting" video frames.
Traffic Warning Sign Recognition
Recognize traffic warning signs, such as Stop, Do Not Enter, and Yield, in a color video sequence.
Video Stabilization
Remove the effect of camera motion from a video stream.
Barcode Recognition Using Live Video Acquisition
Use the From Video Device block provided by Image Acquisition Toolbox™ to acquire live image data from a Point Grey Flea® 2 camera into Simulink®. The example uses the Computer Vision Toolbox™ to create an image processing system which can recognize and interpret a GTIN-13 barcode. The GTIN-13 barcode, formally known as EAN-13, is an international barcode standard. It is a superset of the widely used UPC standard.
Video Mosaicking
Create a mosaic from a video sequence. Video mosaicking is the process of stitching video frames together to form a comprehensive view of the scene. The resulting mosaic image is a compact representation of the video data. The Video Mosaicking block is often used in video compression and surveillance applications.
Edge Detection Using Live Video Acquisition
Use the From Video Device block provided by Image Acquisition Toolbox™ to acquire live image data from a Hamamatsu C8484 camera into Simulink®. The Prewitt method is applied to find the edges of objects in the input video stream.
