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Computer Vision avec Simulink
R2024aUtilisez les blocs de Computer Vision Toolbox™ pour créer des modèles destinés aux applications de Computer Vision. Exécutez des opérations de détection des caractéristiques, d’analyse d’images, de filtrage FIR, de transformation de fréquence et de transformée de Hough, de morphologie, d’amélioration du contraste et de suppression du bruit.
Les caractéristiques locales et leurs descripteurs sont les blocs de base d’un grand nombre d’algorithmes de Computer Vision. Leurs applications comprennent le recalage d’images, la détection et la classification d’objets, le pistage et l’estimation du mouvement.
L’estimation du mouvement et le pistage sont des opérations essentielles dans des applications comme la reconnaissance d’activité, le contrôle du trafic, la sécurité automobile et la surveillance.
Certaines techniques d’analyse et d’amélioration vous permettent d’augmenter le rapport signal à bruit et d’accentuer les caractéristiques.
La fonction showvipblockdatatypetable donne des détails sur les fonctionnalités des blocs, les limitations relatives à la génération de code, le dimensionnement des variables et les types de données supportés pour tous les blocs Computer Vision Toolbox.
Blocs
 | Corner Detection | Calculate corner metric matrix and find corners in images |
 | Edge Detection | Find edges of objects in images using Sobel, Prewitt, Roberts, or Canny method |
 | Trace Boundary | Trace object boundaries in binary images |
 | Template Matching | Locate a template in an image |
 | Estimate Geometric Transformation | Estimate geometric transformation from matching point pairs |
 | Find Local Maxima | Find local maxima in matrices |
| Template Matching | Locate a template in an image |
 | Warp | Apply projective or affine transformation |
 | Resize | Enlarge or shrink entire image or region of interest within image |
 | Rotate | Rotate image by specified angle |
 | Shear | Shift rows or columns of an image or a video frame by linearly varying offset |
 | Translate | Translate image or video frame |
 | Deep Learning Object Detector | Detect objects using trained deep learning object detector |
 | Block Matching | Estimate motion between images or video frames |
 | Optical Flow | Estimate object velocities |
| Template Matching | Locate a template in an image |
 | 2-D Autocorrelation | 2-D autocorrelation of input matrix |
 | 2-D Correlation | Compute 2-D correlation of two input matrices |
 | 2-D Histogram | Generate histogram from input |
 | 2-D Maximum | Compute maximum value of input or sequence of inputs |
 | 2-D Mean | Find 2-D mean of input array |
 | 2-D Median | 2-D Median values of input array |
 | 2-D Minimum | Find minimum values in input or sequence of inputs |
 | 2-D Standard Deviation | Compute standard deviation of input or sequence of inputs |
 | 2-D Variance | Compute variance of input or sequence of inputs |
 | Blob Analysis | Statistics for labeled regions |
| Find Local Maxima | Find local maxima in matrices |
 | PSNR | Compute peak signal-to-noise ratio (PSNR) between images |
 | Bottom-hat | Perform morphological bottom-hat filtering on intensity or binary images |
 | Closing | Perform morphological closing on binary or intensity images |
 | Dilation | Dilate binary or intensity image by finding local maxima |
 | Erosion | Find local minima in binary or intensity image |
 | Label | Label connected components in binary image |
 | Opening | Perform morphological opening on binary or intensity images |
 | Top-hat | Perform morphological top-hat filtering on intensity or binary images |
 | Autothreshold | Convert intensity image to binary image |
 | Chroma Resampling | Downsample or upsample chrominance components of images |
 | Color Space Conversion | Convert color space of image |
 | Demosaic | Demosaic Bayer format images |
 | Gamma Correction | Apply or remove gamma correction to or from image or video stream |
 | Image Complement | Compute the complement of pixel values in binary or intensity images |
 | Image Data Type Conversion | Convert and scale input image to specified output data type |
 | Image Pad | Pad image by adding rows, columns, or both |
 | To Simulink Image | Pack numeric matrix into a Simulink image (depuis R2022a) |
 | From Simulink Image | Unpack numeric matrix from Simulink image (depuis R2022a) |
 | Image Attributes | Output attributes of Simulink image signal (depuis R2022b) |
 | 2-D Convolution | Compute 2-D discrete convolution of two input matrices |
 | 2-D FFT | Compute 2-D fast Fourier transform (FFT) |
 | 2-D IFFT | Compute 2-D inverse fast Fourier transform (IFFT) |
 | 2-D DCT | Compute 2-D discrete cosine transform (DCT) |
 | 2-D IDCT | Compute 2-D inverse discrete cosine transform (IDCT) |
 | 2-D FIR Filter | 2-D FIR filter on input matrix |
| Block Matching | Estimate motion between images or video frames |
 | Block Processing | Repeat user-specified operation on blocks of input matrix |
 | Contrast Adjustment | Adjust image contrast using linear scaling |
 | Deinterlacing | Remove interlacing effect |
| Edge Detection | Find edges of objects in images using Sobel, Prewitt, Roberts, or Canny method |
 | Histogram Equalization | Enhance contrast of images using histogram equalization |
 | Median Filter | Perform 2-D median filtering |
 | Hough Transform | Find lines in images |
 | Hough Lines | Find Cartesian coordinates of lines described by rho and theta pairs |
 | Gaussian Pyramid | Perform Gaussian pyramid decomposition |
 | Write Binary File | Write binary video data to file |
 | Image From File | Read image from file location |
 | Image From Workspace | Import image from MATLAB workspace |
 | Video Viewer | Display images or video frames |
 | From Multimedia File | Read video frames and audio samples from multimedia file |
 | To Multimedia File | Write video frames and audio samples to multimedia file |
 | To Video Display | Display images or video frames |
 | Frame Rate Display | Calculate and display video frame rate |
 | Video To Workspace | Export image or video to MATLAB workspace |
 | Video From Workspace | Import video from MATLAB workspace |
 | Read Binary File | Read video data from binary file |
 | Compositing | Combine two images or apply mask to image |
 | Draw Markers | Draw markers on image |
 | Draw Shapes | Draw rectangles, lines, polygons, or circles on images |
| Image Pad | Pad image by adding rows, columns, or both |
 | Insert Text | Draw text on images or video frames |
| Point Cloud Viewer |
Objets
Simulink.ImageType | Specify image data type |
Rubriques
- 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.
Sélection d՚exemples
Track Marker Using Simulink Images
Track a marker on a whiteboard by using images of the Simulink.ImageType data type.
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.
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.
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.
