|
void | setMinArea (int minArea) |
|
int | getMinArea () |
|
void | setMaxAreaRelative (float maxArea) |
|
float | getMaxAreaRelative () |
|
void | setScaleFactor (float scale_factor) |
|
float | getScaleFactor () |
|
void | setNScales (int n_scales) |
|
int | getNScales () |
|
void | detect (Mat image, MatOfKeyPoint keypoints, Mat mask) |
| Detects keypoints in an image (first variant) or image set (second variant).
|
|
void | detect (Mat image, MatOfKeyPoint keypoints) |
| Detects keypoints in an image (first variant) or image set (second variant).
|
|
void | detect (List< Mat > images, List< MatOfKeyPoint > keypoints, List< Mat > masks) |
|
void | detect (List< Mat > images, List< MatOfKeyPoint > keypoints) |
|
void | compute (Mat image, MatOfKeyPoint keypoints, Mat descriptors) |
| Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).
|
|
void | compute (List< Mat > images, List< MatOfKeyPoint > keypoints, List< Mat > descriptors) |
|
void | detectAndCompute (Mat image, Mat mask, MatOfKeyPoint keypoints, Mat descriptors, bool useProvidedKeypoints) |
|
void | detectAndCompute (Mat image, Mat mask, MatOfKeyPoint keypoints, Mat descriptors) |
|
int | descriptorSize () |
|
int | descriptorType () |
|
int | defaultNorm () |
|
void | write (string fileName) |
|
void | read (string fileName) |
|
override bool | empty () |
| Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
|
|
override string | getDefaultName () |
|
IntPtr | getNativeObjAddr () |
|
virtual void | clear () |
| Clears the algorithm state.
|
|
void | save (string filename) |
|
void | Dispose () |
|
void | ThrowIfDisposed () |
|
Class implementing the Tree Based Morse Regions (TBMR) as described in [Najman2014] extended with scaled extraction ability.
min_area prune areas smaller than minArea max_area_relative prune areas bigger than maxArea = max_area_relative * input_image_size scale_factor scale factor for scaled extraction. n_scales number of applications of the scale factor (octaves).
- Note
- This algorithm is based on Component Tree (Min/Max) as well as MSER but uses a Morse-theory approach to extract features.
Features are ellipses (similar to MSER, however a MSER feature can never be a TBMR feature and vice versa).