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computer vision with simulink -凯发k8网页登录

simulink support for computer vision applications

use computer vision toolbox™ blocks to build models for computer vision applications. perform feature detection, image analysis, fir filtering, frequency and hough transforms, morphology, contrast enhancement, and noise removal.

local features and their descriptors are the building blocks of many computer vision algorithms. their applications include image registration, object detection and classification, tracking, and motion estimation.

motion estimation and tracking are key activities in applications including activity recognition, traffic monitoring, automotive safety, and surveillance.

analysis and enhancement techniques enable you to increase the signal-to-noise ratio and accentuate features.

the function provides details regarding block capabilities, limitations pertaining to code generation, variable-sizing, and supported data types for all computer vision toolbox blocks.

blocks

calculate corner metric matrix and find corners in images
find edges of objects in images using sobel, prewitt, roberts, or canny method
trace object boundaries in binary images
locate a template in an image
estimate geometric transformation from matching point pairs
find local maxima in matrices
locate a template in an image
apply projective or affine transformation
enlarge or shrink entire image or region of interest within image
rotate image by specified angle
shift rows or columns of an image or a video frame by linearly varying offset
translate image in 2-d plane using displacement vector
detect objects using trained deep learning object detector
estimate motion between images or video frames
estimate object velocities
locate a template in an image
2-d autocorrelation of input matrix
compute 2-d correlation of two input matrices
generate histogram from input
compute maximum value of input or sequence of inputs
find 2-d mean of input array
2-d median values of input array
find minimum values in input or sequence of inputs
compute standard deviation of input or sequence of inputs
compute variance of input or sequence of inputs
statistics for labeled regions
find local maxima in matrices
compute peak signal-to-noise ratio (psnr) between images
perform morphological bottom-hat filtering on intensity or binary images
perform morphological closing on binary or intensity images
dilate binary or intensity image by finding local maxima
find local minima in binary or intensity image
label connected components in binary image
perform morphological opening on binary or intensity images
perform morphological top-hat filtering on intensity or binary images
convert intensity image to binary image
downsample or upsample chrominance components of images
convert color space of image
demosaic bayer format images
apply or remove gamma correction to or from image or video stream
compute the complement of pixel values in binary or intensity images
convert and scale input image to specified output data type
pad image by adding rows, columns, or both
pack numeric matrix into a simulink image
unpack numeric matrix from simulink image
output attributes of simulink image signal
compute 2-d discrete convolution of two input matrices
compute two-dimensional fast fourier transform of input
2-d inverse fast fourier transform of input
compute 2-d discrete cosine transform (dct)
compute 2-d inverse discrete cosine transform (idct)
2-d fir filter on input matrix
estimate motion between images or video frames
adjust image contrast using linear scaling
remove interlacing effect
find edges of objects in images using sobel, prewitt, roberts, or canny method
enhance contrast of images using histogram equalization
perform 2-d median filtering
find lines in images
find cartesian coordinates of lines described by rho and theta pairs
perform gaussian pyramid decomposition
write binary video data to file
read image from file location
import image from matlab workspace
display images or video frames
read video frames and audio samples from multimedia file
write video frames and audio samples to multimedia file
display images or video frames
calculate and display video frame rate
export image or video to matlab workspace
import video from matlab workspace
read video data from binary file
combine two images or apply mask to image
draw markers on image
draw rectangles, lines, polygons, or circles on images
pad image by adding rows, columns, or both
draw text on images or video frames
visualize streaming point cloud data sequence

objects

specify image data type

topics


  • video data is a series of images over time.


  • in the computer vision toolbox software, images are real-valued ordered sets of color or intensity data.


  • 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.


  • defines fixed-point concepts and terminology that are helpful to know as you use dsp system toolbox™ software.


  • 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 computer vision toolbox system objects


  • teaches you how to specify fixed-point attributes and parameters in software on both the block and system levels.


  • this example shows how to visualize a streaming point cloud sequence by using a point cloud viewer block.

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