Module opencv::features2d [−][src]
Expand description
2D Features Framework
Feature Detection and Description
Descriptor Matchers
Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem. This section is devoted to matching descriptors that are represented as vectors in a multidimensional space. All objects that implement vector descriptor matchers inherit the DescriptorMatcher interface.
Drawing Function of Keypoints and Matches
Object Categorization
This section describes approaches based on local 2D features and used to categorize objects.
Hardware Acceleration Layer
Modules
Structs
Brute-force descriptor matcher.
Class to compute an image descriptor using the bag of visual words.
kmeans -based class to train visual vocabulary using the bag of visual words approach. :
Flann-based descriptor matcher.
A class filters a vector of keypoints.
Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe Lowe04 .
Class for extracting blobs from an image. :
Enums
Constants
Output image matrix will be created (Mat::create), i.e. existing memory of output image may be reused. Two source image, matches and single keypoints will be drawn. For each keypoint only the center point will be drawn (without the circle around keypoint with keypoint size and orientation).
Output image matrix will not be created (Mat::create). Matches will be drawn on existing content of output image.
For each keypoint the circle around keypoint with keypoint size and orientation will be drawn.
Single keypoints will not be drawn.
Traits
Class implementing the AKAZE keypoint detector and descriptor extractor, described in ANB13.
Class for implementing the wrapper which makes detectors and extractors to be affine invariant, described as ASIFT in YM11 .
Wrapping class for feature detection using the AGAST method. :
Brute-force descriptor matcher.
Class to compute an image descriptor using the bag of visual words.
kmeans -based class to train visual vocabulary using the bag of visual words approach. :
Abstract base class for training the bag of visual words vocabulary from a set of descriptors.
Class implementing the BRISK keypoint detector and descriptor extractor, described in LCS11 .
Abstract base class for matching keypoint descriptors.
Wrapping class for feature detection using the FAST method. :
Flann-based descriptor matcher.
Wrapping class for feature detection using the goodFeaturesToTrack function. :
Class implementing the KAZE keypoint detector and descriptor extractor, described in ABD12 .
A class filters a vector of keypoints.
Maximally stable extremal region extractor
Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor
Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe Lowe04 .
Class for extracting blobs from an image. :
Functions
Detects corners using the AGAST algorithm
Detects corners using the AGAST algorithm
Detects corners using the FAST algorithm
Detects corners using the FAST algorithm
Draws keypoints.
Draws the found matches of keypoints from two images.
Draws the found matches of keypoints from two images.
C++ default parameters
Functions to evaluate the feature detectors and [generic] descriptor extractors *
**
Type Definitions
Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem. This section is devoted to computing descriptors represented as vectors in a multidimensional space. All objects that implement the vector descriptor extractors inherit the DescriptorExtractor interface.
Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem. All objects that implement keypoint detectors inherit the FeatureDetector interface.