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feature detection and extraction -凯发k8网页登录

image registration, interest point detection, feature descriptor extraction, point feature matching, and image retrieval

local features and their descriptors are the building blocks of many computer vision algorithms. their applications include image registration, object detection and classification, tracking, motion estimation, and content-based image retrieval (cbir). these algorithms use local features to better handle scale changes, rotation, and occlusion. computer vision toolbox™ algorithms include the fast, harris, and shi & tomasi corner detectors, and the sift, surf, kaze, and mser blob detectors. the toolbox includes the sift, surf, freak, brisk, lbp, orb, and hog descriptors. you can mix and match the detectors and the descriptors depending on the requirements of your application.

one item feature matched from a cluttered scene

functions

detect brisk features
detect corners using fast algorithm
detect corners using harris–stephens algorithm
detect kaze features
detect corners using minimum eigenvalue algorithm
detect mser features
detect orb keypoints
detect scale invariant feature transform (sift) features
detect surf features
extractfeaturesextract interest point descriptors
extract local binary pattern (lbp) features
extract histogram of oriented gradients (hog) features
find matching features
find matching features within specified radius
apply geometric transformation to image
estimate 2-d geometric transformation from matching point pairs
estimate 3-d geometric transformation from matching point pairs
combine images, overlay images, or highlight selected pixels
estimate motion between images or video frames
find local maxima in matrices
locate template in image
insert markers in image or video
insert shapes in image or video
display corresponding feature points
display shapes on image, video, or point cloud
annotate truecolor or grayscale image or video stream
insert text in image or video
display image
compare differences between images
apply or remove gamma correction from images or video streams
downsample or upsample chrominance components of images
object for storing binary feature vectors
object for storing brisk interest points
object for storing corner points
object for storing kaze interest points
object for storing mser regions
object for storing orb keypoints
object for storing sift interest points
object for storing surf interest points
2-d rigid geometric transformation
2-d similarity geometric transformation
2-d affine geometric transformation
2-d projective geometric transformation
3-d rigid geometric transformation
3-d similarity geometric transformation

create recognition database

bag of visual words object
search index that maps visual words to images

retrieve images

search image set for similar image
datastore for image data
evaluate image search results

topics

  • local feature detection and extraction

    learn the benefits and applications of local feature detection and extraction.


  • choose functions that return and accept points objects for several types of features.


  • specify pixel indices, spatial coordinates, and 3-d coordinate systems


  • when you specify the type of shape to draw, you must also specify its location on the image.


  • retrieve images from a collection of images similar to a query image using a content-based image retrieval (cbir) system.

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