Feature Detection and Extraction

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.

Functions

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Detect Features

`detectBRISKFeatures`	Detect BRISK features
`detectFASTFeatures`	Detect corners using FAST algorithm
`detectHarrisFeatures`	Detect corners using Harris–Stephens algorithm
`detectKAZEFeatures`	Detect KAZE features
`detectMinEigenFeatures`	Detect corners using minimum eigenvalue algorithm
`detectMSERFeatures`	Detect MSER features
`detectORBFeatures`	Detect ORB keypoints
`detectSIFTFeatures`	Detect scale invariant feature transform (SIFT) features (Since R2021b)
`detectSURFFeatures`	Detect SURF features

Extract Features

`extractFeatures`	Extract interest point descriptors
`extractLBPFeatures`	Extract local binary pattern (LBP) features
`extractHOGFeatures`	Extract histogram of oriented gradients (HOG) features

Match Features

`matchFeatures`	Find matching features
`matchFeaturesInRadius`	Find matching features within specified radius (Since R2021a)

Image Registration

`estgeotform2d`	Estimate 2-D geometric transformation from matching point pairs (Since R2022b)
`estgeotform3d`	Estimate 3-D geometric transformation from matching point pairs (Since R2022b)
`imwarp`	Apply geometric transformation to image
`imblend`	Blend two images (Since R2024b)
`vision.TemplateMatcher`	Locate template in image

Visualization and Display

`insertMarker`	Insert markers in image or video
`insertShape`	Insert shapes in image or video
`showMatchedFeatures`	Display corresponding feature points
`showShape`	Display shapes on image, video, or point cloud (Since R2020b)
`insertObjectAnnotation`	Annotate truecolor or grayscale image or video
`insertObjectKeypoints`	Insert object keypoints in image (Since R2023b)
`insertText`	Insert text in image or video
`imshow`	Display image
`imshowpair`	Compare differences between images
`vision.ChromaResampler`	Downsample or upsample chrominance components of images

Store Features

`binaryFeatures`	Object for storing binary feature vectors
`BRISKPoints`	Object for storing BRISK interest points
`cornerPoints`	Object for storing corner points
`KAZEPoints`	Object for storing KAZE interest points
`MSERRegions`	Object for storing MSER regions
`ORBPoints`	Object for storing ORB keypoints
`SIFTPoints`	Object for storing SIFT interest points (Since R2021b)
`SURFPoints`	Object for storing SURF interest points

Transform Objects

`rigidtform2d`	2-D rigid geometric transformation (Since R2022b)
`simtform2d`	2-D similarity geometric transformation (Since R2022b)
`affinetform2d`	2-D affine geometric transformation (Since R2022b)
`projtform2d`	2-D projective geometric transformation (Since R2022b)
`rigidtform3d`	3-D rigid geometric transformation (Since R2022b)
`simtform3d`	3-D similarity geometric transformation (Since R2022b)

Retrieve Images

Create Recognition Database

`bagOfFeatures`	Bag of visual words object
`invertedImageIndex`	Search index that maps visual words to images

Retrieve Images

`retrieveImages`	Search image set for similar image
`imageDatastore`	Datastore for image data
`evaluateImageRetrieval`	Evaluate image search results

Topics

Local Feature Detection and Extraction
Learn the benefits and applications of local feature detection and extraction.
Point Feature Types
Choose functions that return and accept points objects for several types of features.
Coordinate Systems
Specify pixel Indices, spatial coordinates, and 3-D coordinate systems.
Draw Shapes and Lines
When you specify the type of shape to draw, you must also specify its location on the image.
Image Retrieval with Bag of Visual Words
Retrieve images from a collection of images similar to a query image using a content-based image retrieval (CBIR) system.

Featured Examples

Image Retrieval Using Customized Bag of Features

Create a Content Based Image Retrieval (CBIR) system using a customized bag-of-features workflow.

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Pattern Matching

Use the 2-D normalized cross-correlation for pattern matching and target tracking. The example uses predefined or user specified target and number of similar targets to be tracked. The normalized cross correlation plot shows that when the value exceeds the set threshold, the target is identified.

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Find Object in Cluttered Scene Using Image Point Features

Detect a particular object in a cluttered scene, given a reference image of the object.

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Digit Classification Using HOG Features

Classify digits using HOG features and a multiclass SVM classifier.

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Automatically Find Image Rotation and Scale

Demonstrates how to automatically determine the geometric transformation between two images. Specifically, when one image is distorted in relation to another due to rotation and scaling, the functions detectSURFFeatures and estimateGeometricTransform2D can be employed to identify the rotation angle and scale factor. Subsequently, these parameters can be used to transform the distorted image back to its original appearance.

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Create Panorama

Automatically stitch multiple images into panorama. The procedure for image stitching is an extension of feature based image registration. Instead of registering a single pair of images, multiple image pairs are successively registered relative to each other to form a panorama.

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Stabilize Video Using Image Point Features

Stabilize a video that was captured from a jittery platform. One way to stabilize a video is to track a salient feature in the image and use this as an anchor point to cancel out all perturbations relative to it. This procedure, however, must be bootstrapped with knowledge of where such a salient feature lies in the first video frame. In this example, we explore a method of video stabilization that works without any such a priori knowledge. It instead automatically searches for the "background plane" in a video sequence, and uses the observed distortion to correct for camera motion.

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Object Counting

Use morphological operations to count objects in a video stream.

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Cell Counting

Use a combination of basic morphological operators and blob analysis to extract information from a video stream. In this case, the example counts the number of E. Coli bacteria in each video frame. Note that the cells are of varying brightness, which makes the task of segmentation more challenging.

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