Find keypoints in an image:

Highlight keypoints on the image:

Count the number of points of interest in an image:

Keypoints of a grayscale image:

Keypoints of a color image:

Find keypoints in an image:

Return the keypoint descriptor:

Compute multiple properties:

Get properties "Position" and "PixelPosition":

The two corresponding coordinate systems are offset by half a pixel:

Get the "Scale" in pixels:

The scale corresponds to the size of an intrinsic region around the keypoint. Visualize the disc region
around a "SURF" keypoint:

Get the "Orientation" in radians:

Show keypoints with a pattern rotated based on orientation:

Get the "Strength" score:

Display the distribution of the strength of keypoints in an image:

Get the "Descriptor" representing the distribution of pixel values inside a keypoint's neighborhood:

The descriptor is similar to the one of the rotated image:

The "ConstrastSign" is 1 for keypoints lighter than their surroundings:

The sign is -1 for keypoints darker than their surroundings:

Visualize SURF keypoints using their scale, orientation and contrast:

Visualize BRISK keypoints:

Extract local patches of fixed size around detected keypoints:

Extract patches of size proportional to the scale of keypoints:

Use keypoints to crop an image to keep the main features:

Create thumbnails of uniform size:

Object recognition using "bag of words" on a dataset of 5,000 images 32×32 each, belonging to 10 categories:

Compute keypoint descriptors on 256×256 images and create the codebook of visual words:

The codebook contains all image descriptors of length 64:

Find 100 visual codewords using -means clustering:

Image features are defined as the normalized counts of all the codewords:

Construct a classifier trained on extracted image features:

Evaluate the classifier on test data:

Not all properties are supported for all methods:

Missing["NotAvailable"] is returned when a property is not available with the specified method:

"FAST" method does not find contrast sign:

"BRISK" method does not compute oriented descriptors:

"SURF" and "KAZE" descriptors are typically compared using the Euclidean distance:

Distances between the strongest keypoint and each of the next 10 strongest:

"AKAZE", "BRISK" and "ORB" descriptors are typically compared using the Hamming distance:

Distances between the strongest keypoint and each of the next 10 strongest:

Cluster the keypoints based on their descriptors:

ImageCorners may be used as keypoints:

Computer corners using radius 3.5 for similarity to the scale of some keypoint detectors:

Use CornerFilter to get the strength of detected corners:

Top 10 FAST keypoints:

Top 10 AGAST keypoints:

ImageCorrespondingPoints gives the locations for keypoints that have matching descriptors:

Compute keypoints on both images:

Take two keypoints with and without a corresponding point in the second image:

Compute all distances between descriptors for these keypoint to all keypoints in the second image:

The second-to-nearest ratio is typically used to decide whether a keypoint has a corresponding point:

The keypoint with a correspondence is red; the other one is yellow: