Feature Detection and Extraction

Image registration, interest point detection, extracting feature descriptors, and point feature matching

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. 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 SURF, KAZE, and MSER blob detectors. The toolbox includes the 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. You can also extract features using a pretrained convolutional neural network which applies techniques from the field of deep learning.

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
`detectMinEigenFeatures`	Detect corners using minimum eigenvalue algorithm and return `cornerPoints` object
`detectMSERFeatures`	Detect MSER features and return `MSERRegions` object
`detectORBFeatures`	Detect and store ORB keypoints
`detectSURFFeatures`	Detect SURF features and return `SURFPoints` object
`detectKAZEFeatures`	Detect KAZE 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
`showMatchedFeatures`	Display corresponding feature points

Image Registration

`imwarp`	Apply geometric transformation to image
`estimateGeometricTransform`	Estimate geometric transform 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
`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
`KAZEPoints`	Object for storing KAZE interest points
`cornerPoints`	Object for storing corner points
`SURFPoints`	Object for storing SURF interest points
`MSERRegions`	Object for storing MSER regions
`ORBPoints`	Object for storing ORB keypoints

Transformation Objects

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

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.

Featured Examples

Localize and Read Multiple Barcodes in Image

Detect, decode, and localize 1-D and 2-D barcodes in an image.

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Automatically Detect and Recognize Text in Natural Images

Automatically Detect And Recognize Text In Natural Images.

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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 estimateGeometricTransform 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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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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