Emgu.CV.Features2D Namespace

Wrapped AGAST detector

Wrapped AKAZE detector

Wrapped BFMatcher

Class to compute an image descriptor using the bag of visual words. Such a computation consists of the following steps: 1. Compute descriptors for a given image and its key points set. 2. Find the nearest visual words from the vocabulary for each key point descriptor. 3. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the vocabulary in the given image.

Kmeans-based class to train visual vocabulary using the bag of visual words approach.

BRISK: Binary Robust Invariant Scalable Keypoints

Descriptor matcher

FAST(Features from Accelerated Segment Test) keypoint detector. See Detects corners using FAST algorithm by E. Rosten ("Machine learning for high-speed corner detection, 2006).

The feature 2D base class

Library to invoke Features2D functions

Tools for features 2D

This matcher trains flann::Index_ on a train descriptor collection and calls its nearest search methods to find the best matches. So, this matcher may be faster when matching a large train collection than the brute force matcher.

Wrapping class for feature detection using the goodFeaturesToTrack() function.

Wrapped KAZE detector

MSER detector

Wrapped ORB detector

Wrapped SIFT detector

Simple Blob detector

Parameters for the simple blob detector

Agast feature type

Type of the extracted descriptor

The match distance type

One of the three neighborhoods as defined in the paper

Define the Keypoint draw type

The diffusivity

The score type