SIFT feature detector and descriptor extractor.
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| AFAPI void | sift (features &feat, array &desc, const array &in, const unsigned n_layers=3, const float contrast_thr=0.04f, const float edge_thr=10.f, const float init_sigma=1.6f, const bool double_input=true, const float intensity_scale=0.00390625f, const float feature_ratio=0.05f) |
| | C++ Interface for SIFT feature detector and descriptor. More...
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| AFAPI void | gloh (features &feat, array &desc, const array &in, const unsigned n_layers=3, const float contrast_thr=0.04f, const float edge_thr=10.f, const float init_sigma=1.6f, const bool double_input=true, const float intensity_scale=0.00390625f, const float feature_ratio=0.05f) |
| | C++ Interface for SIFT feature detector and GLOH descriptor. More...
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| AFAPI af_err | af_sift (af_features *feat, af_array *desc, const af_array in, const unsigned n_layers, const float contrast_thr, const float edge_thr, const float init_sigma, const bool double_input, const float intensity_scale, const float feature_ratio) |
| | C++ Interface for SIFT feature detector and descriptor. More...
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| AFAPI af_err | af_gloh (af_features *feat, af_array *desc, const af_array in, const unsigned n_layers, const float contrast_thr, const float edge_thr, const float init_sigma, const bool double_input, const float intensity_scale, const float feature_ratio) |
| | C++ Interface for SIFT feature detector and GLOH descriptor. More...
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SIFT feature detector and descriptor extractor.
Detects features and extract descriptors using the Scale Invariant Feature Transform (SIFT), by David Lowe.
Lowe, D. G., "Distinctive Image Features from Scale-Invariant Keypoints", International Journal of Computer Vision, 60, 2, pp. 91-110, 2004.
◆ af_gloh()
| AFAPI af_err af_gloh |
( |
af_features * |
feat, |
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af_array * |
desc, |
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const af_array |
in, |
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const unsigned |
n_layers, |
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const float |
contrast_thr, |
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const float |
edge_thr, |
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const float |
init_sigma, |
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const bool |
double_input, |
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const float |
intensity_scale, |
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const float |
feature_ratio |
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) |
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C++ Interface for SIFT feature detector and GLOH descriptor.
- Parameters
-
| [out] | feat | af_features object composed of arrays for x and y coordinates, score, orientation and size of selected features |
| [out] | desc | Nx272 array containing extracted GLOH descriptors, where N is the number of features found by SIFT |
| [in] | in | array containing a grayscale image (color images are not supported) |
| [in] | n_layers | number of layers per octave, the number of octaves is computed automatically according to the input image dimensions, the original SIFT paper suggests 3 |
| [in] | contrast_thr | threshold used to filter out features that have low contrast, the original SIFT paper suggests 0.04 |
| [in] | edge_thr | threshold used to filter out features that are too edge-like, the original SIFT paper suggests 10.0 |
| [in] | init_sigma | the sigma value used to filter the input image at the first octave, the original SIFT paper suggests 1.6 |
| [in] | double_input | if true, the input image dimensions will be doubled and the doubled image will be used for the first octave |
| [in] | intensity_scale | the inverse of the difference between the minimum and maximum grayscale intensity value, e.g.: if the ranges are 0-256, the proper intensity_scale value is 1/256, if the ranges are 0-1, the proper intensity-scale value is 1/1 |
| [in] | feature_ratio | maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded |
◆ af_sift()
| AFAPI af_err af_sift |
( |
af_features * |
feat, |
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af_array * |
desc, |
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const af_array |
in, |
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const unsigned |
n_layers, |
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const float |
contrast_thr, |
|
|
const float |
edge_thr, |
|
|
const float |
init_sigma, |
|
|
const bool |
double_input, |
|
|
const float |
intensity_scale, |
|
|
const float |
feature_ratio |
|
) |
| |
C++ Interface for SIFT feature detector and descriptor.
- Parameters
-
| [out] | feat | af_features object composed of arrays for x and y coordinates, score, orientation and size of selected features |
| [out] | desc | Nx128 array containing extracted descriptors, where N is the number of features found by SIFT |
| [in] | in | array containing a grayscale image (color images are not supported) |
| [in] | n_layers | number of layers per octave, the number of octaves is computed automatically according to the input image dimensions, the original SIFT paper suggests 3 |
| [in] | contrast_thr | threshold used to filter out features that have low contrast, the original SIFT paper suggests 0.04 |
| [in] | edge_thr | threshold used to filter out features that are too edge-like, the original SIFT paper suggests 10.0 |
| [in] | init_sigma | the sigma value used to filter the input image at the first octave, the original SIFT paper suggests 1.6 |
| [in] | double_input | if true, the input image dimensions will be doubled and the doubled image will be used for the first octave |
| [in] | intensity_scale | the inverse of the difference between the minimum and maximum grayscale intensity value, e.g.: if the ranges are 0-256, the proper intensity_scale value is 1/256, if the ranges are 0-1, the proper intensity-scale value is 1/1 |
| [in] | feature_ratio | maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded |
◆ gloh()
| AFAPI void gloh |
( |
features & |
feat, |
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|
array & |
desc, |
|
|
const array & |
in, |
|
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const unsigned |
n_layers = 3, |
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const float |
contrast_thr = 0.04f, |
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|
const float |
edge_thr = 10.f, |
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|
const float |
init_sigma = 1.6f, |
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|
const bool |
double_input = true, |
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|
const float |
intensity_scale = 0.00390625f, |
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|
const float |
feature_ratio = 0.05f |
|
) |
| |
C++ Interface for SIFT feature detector and GLOH descriptor.
- Parameters
-
| [out] | feat | features object composed of arrays for x and y coordinates, score, orientation and size of selected features |
| [out] | desc | Nx272 array containing extracted GLOH descriptors, where N is the number of features found by SIFT |
| [in] | in | array containing a grayscale image (color images are not supported) |
| [in] | n_layers | number of layers per octave, the number of octaves is computed automatically according to the input image dimensions, the original SIFT paper suggests 3 |
| [in] | contrast_thr | threshold used to filter out features that have low contrast, the original SIFT paper suggests 0.04 |
| [in] | edge_thr | threshold used to filter out features that are too edge-like, the original SIFT paper suggests 10.0 |
| [in] | init_sigma | the sigma value used to filter the input image at the first octave, the original SIFT paper suggests 1.6 |
| [in] | double_input | if true, the input image dimensions will be doubled and the doubled image will be used for the first octave |
| [in] | intensity_scale | the inverse of the difference between the minimum and maximum grayscale intensity value, e.g.: if the ranges are 0-256, the proper intensity_scale value is 1/256, if the ranges are 0-1, the proper intensity-scale value is 1/1 |
| [in] | feature_ratio | maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded |
◆ sift()
| AFAPI void sift |
( |
features & |
feat, |
|
|
array & |
desc, |
|
|
const array & |
in, |
|
|
const unsigned |
n_layers = 3, |
|
|
const float |
contrast_thr = 0.04f, |
|
|
const float |
edge_thr = 10.f, |
|
|
const float |
init_sigma = 1.6f, |
|
|
const bool |
double_input = true, |
|
|
const float |
intensity_scale = 0.00390625f, |
|
|
const float |
feature_ratio = 0.05f |
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) |
| |
C++ Interface for SIFT feature detector and descriptor.
- Parameters
-
| [out] | feat | features object composed of arrays for x and y coordinates, score, orientation and size of selected features |
| [out] | desc | Nx128 array containing extracted descriptors, where N is the number of features found by SIFT |
| [in] | in | array containing a grayscale image (color images are not supported) |
| [in] | n_layers | number of layers per octave, the number of octaves is computed automatically according to the input image dimensions, the original SIFT paper suggests 3 |
| [in] | contrast_thr | threshold used to filter out features that have low contrast, the original SIFT paper suggests 0.04 |
| [in] | edge_thr | threshold used to filter out features that are too edge-like, the original SIFT paper suggests 10.0 |
| [in] | init_sigma | the sigma value used to filter the input image at the first octave, the original SIFT paper suggests 1.6 |
| [in] | double_input | if true, the input image dimensions will be doubled and the doubled image will be used for the first octave |
| [in] | intensity_scale | the inverse of the difference between the minimum and maximum grayscale intensity value, e.g.: if the ranges are 0-256, the proper intensity_scale value is 1/256, if the ranges are 0-1, the proper intensity-scale value is 1/1 |
| [in] | feature_ratio | maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded |
1.9.5