MRPT
1.9.9
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#include <mrpt/img/TPixelCoord.h>
#include <mrpt/core/round.h>
#include <mrpt/math/KDTreeCapable.h>
#include <mrpt/math/CMatrixTemplate.h>
#include <mrpt/math/CMatrixTemplateNumeric.h>
#include <mrpt/vision/types.h>
#include <functional>
Go to the source code of this file.
Classes | |
struct | mrpt::vision::TSimpleFeature_templ< PIXEL_COORD_TYPE > |
A simple structure for representing one image feature (without descriptor nor patch) - This is the template which allows you to select if pixels are represented as integers or floats. More... | |
struct | mrpt::vision::TSimpleFeatureTraits< FEATURE > |
struct | mrpt::vision::TSimpleFeatureTraits< TSimpleFeature > |
struct | mrpt::vision::TSimpleFeatureTraits< TSimpleFeaturef > |
struct | mrpt::vision::TSimpleFeatureList_templ< FEATURE > |
A list of image features using the structure TSimpleFeature for each feature. More... | |
struct | mrpt::vision::KeypointResponseSorter< FEATURE_LIST > |
A helper struct to sort keypoints by their response: It can be used with these types: More... | |
class | mrpt::vision::CFeatureListKDTree< FEAT > |
Helper class: KD-tree search class for vector<KeyPoint>: Call mark_as_outdated() to force rebuilding the kd-tree after modifying the linked feature list. More... | |
Namespaces | |
mrpt::vision | |
Classes for computer vision, detectors, features, etc. | |
Typedefs | |
using | mrpt::vision::TSimpleFeature = TSimpleFeature_templ< mrpt::img::TPixelCoord > |
A simple structure for representing one image feature (without descriptor nor patch). More... | |
using | mrpt::vision::TSimpleFeaturef = TSimpleFeature_templ< mrpt::img::TPixelCoordf > |
A version of TSimpleFeature with subpixel precision. More... | |
using | mrpt::vision::TSimpleFeatureList = TSimpleFeatureList_templ< TSimpleFeature > |
A list of image features using the structure TSimpleFeature for each feature. More... | |
using | mrpt::vision::TSimpleFeaturefList = TSimpleFeatureList_templ< TSimpleFeaturef > |
A list of image features using the structure TSimpleFeaturef for each feature - capable of KD-tree computations. More... | |
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