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.

One item feature matched from a cluttered scene

Functions

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Detect Features

`detectBRISKFeatures`	Detect BRISK features and return `BRISKPoints` object
`detectFASTFeatures`	Detect corners using FAST algorithm and return `cornerPoints` object
`detectHarrisFeatures`	Detect corners using Harris–Stephens algorithm and return `cornerPoints` object
`detectKAZEFeatures`	Detect KAZE features and return `KAZEPoints` object
`detectMinEigenFeatures`	Detect corners using minimum eigenvalue algorithm and return `cornerPoints` object
`detectMSERFeatures`	Detect MSER features and return `MSERRegions` object
`detectORBFeatures`	Detect ORB keypoints and return an `ORBPoints` object
`detectSIFTFeatures`	Detect scale invariant feature transform (SIFT) features and return `SIFTPoints` object
`detectSURFFeatures`	Detect SURF features and return `SURFPoints` object

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

Image Retrieval

`imwarp`	Apply geometric transformation to image
`estimateGeometricTransform2D`	Estimate 2-D geometric transformation from matching point pairs
`estimateGeometricTransform3D`	Estimate 3-D geometric transformation from matching point pairs
`vision.AlphaBlender`	Combine images, overlay images, or highlight selected pixels
`vision.BlockMatcher`	Estimate motion between images or video frames
`vision.LocalMaximaFinder`	Find local maxima in matrices
`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
`insertObjectAnnotation`	Annotate truecolor or grayscale image or video stream
`insertText`	Insert text in image or video
`imshow`	Display image
`imshowpair`	Compare differences between images
`vision.GammaCorrector`	Apply or remove gamma correction from images or video streams
`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
`SURFPoints`	Object for storing SURF interest points

Transform Objects

`affine2d`	2-D affine geometric transformation
`affine3d`	3-D affine geometric transformation
`rigid3d`	3-D rigid geometric transformation
`projective2d`	2-D projective geometric transformation

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 it’s 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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Object Detection in a Cluttered Scene Using Point Feature Matching

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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Find Image Rotation and Scale Using Automated Feature Matching

Automatically determine the geometric transformation between a pair of images. When one image is distorted relative to another by rotation and scale, use detectSURFFeatures and estimateGeometricTransform2D to find the rotation angle and scale factor. You can then transform the distorted image to recover the original image.

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Feature Based Panoramic Image Stitching

Automatically create a panorama using feature based image registration techniques.

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Video Stabilization Using Point Feature Matching

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 its 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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