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Emgu.CV.Features2D Namespace
Contains classes for 2D feature detection, extraction and matching.
Public classAKAZE
Wrapped AKAZE detector
Public classBFMatcher
Wrapped BFMatcher
Public classBOWImgDescriptorExtractor
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.
Public classBOWKMeansTrainer
Kmeans-based class to train visual vocabulary using the bag of visual words approach.
Public classBrisk
BRISK: Binary Robust Invariant Scalable Keypoints
Public classDescriptorMatcher
Descriptor matcher
Public classFastDetector
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).
Public classFeature2D
The feature 2D base class
Public classFeatures2DToolbox
Tools for features 2D
Public classGFTTDetector
Wrapping class for feature detection using the goodFeaturesToTrack() function.
Public classKAZE
Wrapped KAZE detector
Public classMSERDetector
MSER detector
Public classORBDetector
Wrapped ORB detector
Public classSimpleBlobDetector
Simple Blob detector