OrgLion-ML
/
dlib
mirror of https://github.com/davisking/dlib.git
1
0
Fork 0
Code Issues Releases Wiki Activity

updated docs and specs

This commit is contained in:
Davis King 2012-08-26 15:07:28 -04:00
parent 2bee86842a
commit e19f5d65fe
3 changed files with 40 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
dlib/image_processing/scan_image_pyramid_abstract.h
View File

@ -48,6 +48,7 @@ namespace dlib
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing
Natural Scene Categories by Svetlana Lazebnik, Cordelia Schmid,
and Jean Ponce
It also includes the ability to represent movable part models.
@ -88,8 +89,11 @@ namespace dlib
score of the classifier. Note further that each of the movable feature extraction
zones must pass a threshold test for it to be included. That is, if the score that a
movable zone would contribute to the overall score for a sliding window location is not
positive then that zone is not included in the feature vector (i.e. its part of the
feature vector is set to zero. This way the length of the feature vector stays constant).
positive then that zone is not included in the feature vector (i.e. its part of the
feature vector is set to zero. This way the length of the feature vector stays
constant). This movable region construction allows us to represent objects with parts
that move around relative to the object box. For example, a human has hands but they
aren't always in the same place relative to a person's bounding box.
THREAD SAFETY
Concurrent access to an instance of this object is not safe and should be protected

1
docs/docs/imaging.xml
View File

@ -1574,6 +1574,7 @@
Natural Scene Categories by Svetlana Lazebnik, Cordelia Schmid,
and Jean Ponce
</blockquote>
It also includes the ability to represent movable part models.
<br/><br/>
The following feature extractors can be used with the scan_image_pyramid object:

50
examples/object_detector_ex.cpp
View File

@ -121,22 +121,22 @@ int main()
problem you are trying to solve.
2. A detection template. This is a rectangle which defines the shape of a
sliding window (the object_box), as well as a set of rectangles which
envelop it. This set of enveloping rectangles defines the spatial
structure of the overall feature extraction within a sliding window.
In particular, each location of a sliding window has a feature vector
sliding window (i.e. the object_box), as well as a set of rectangular feature
extraction regions inside it. This set of regions defines the spatial
structure of the overall feature extraction within a sliding window. In
particular, each location of a sliding window has a feature vector
associated with it. This feature vector is defined as follows:
- Let N denote the number of enveloping rectangles.
- Let M denote the dimensionality of the vectors output by feature_extractor_type
- Let N denote the number of feature extraction zones.
- Let M denote the dimensionality of the vectors output by Feature_extractor_type
objects.
- Let F(i) == the M dimensional vector which is the sum of all vectors
given by our feature_extractor_type object inside the ith enveloping
rectangle.
given by our Feature_extractor_type object inside the ith feature extraction
zone.
- Then the feature vector for a sliding window is an M*N dimensional vector
[F(1) F(2) F(3) ... F(N)] (i.e. it is a concatenation of the N vectors).
This feature vector can be thought of as a collection of N "bags of features",
each bag coming from a spatial location determined by one of the enveloping
rectangles.
each bag coming from a spatial location determined by one of the rectangular
feature extraction zones.
3. A weight vector and a threshold value. The dot product between the weight
vector and the feature vector for a sliding window location gives the score
@ -145,11 +145,27 @@ int main()
parameters yourself. They are automatically populated by the
structural_object_detection_trainer.
Finally, the sliding window classifiers described above are applied to every level
of an image pyramid. So you need to tell scan_image_pyramid what kind of pyramid
you want to use. In this case we are using pyramid_down which downsamples each
pyramid layer by half (dlib also contains other version of pyramid_down which result
in finer grained pyramids).
The sliding window classifiers described above are applied to every level of an image
pyramid. So you need to tell scan_image_pyramid what kind of pyramid you want to
use. In this case we are using pyramid_down which downsamples each pyramid layer by
half (dlib also contains other version of pyramid_down which result in finer grained
pyramids).
Finally, some of the feature extraction zones are allowed to move freely within the
object box. This means that when we are sliding the classifier over an image, some
feature extraction zones are stationary (i.e. always in the same place relative to
the object box) while others are allowed to move anywhere within the object box. In
particular, the movable regions are placed at the locations that maximize the score
of the classifier. Note further that each of the movable feature extraction zones
must pass a threshold test for it to be included. That is, if the score that a
movable zone would contribute to the overall score for a sliding window location is
not positive then that zone is not included in the feature vector (i.e. its part of
the feature vector is set to zero. This way the length of the feature vector stays
constant). This movable region construction allows us to represent objects with
parts that move around relative to the object box. For example, a human has hands
but they aren't always in the same place relative to a person's bounding box.
However, to keep this example program simple, we will only be using stationary
feature extraction regions.
*/
typedef hashed_feature_image<hog_image<3,3,1,4,hog_signed_gradient,hog_full_interpolation> > feature_extractor_type;
typedef scan_image_pyramid<pyramid_down, feature_extractor_type> image_scanner_type;
@ -167,8 +183,8 @@ int main()
// we only added square detection templates then it would be impossible to detect this non-square
// rectangle. The setup_grid_detection_templates_verbose() routine will take care of this for us by
// looking at the contents of object_locations and automatically picking an appropriate set. Also,
// the final arguments indicate that we want our detection templates to have 4 enveloping rectangles
// laid out in a 2x2 regular grid inside each sliding window.
// the final arguments indicate that we want our detection templates to have 4 feature extraction
// regions laid out in a 2x2 regular grid inside each sliding window.
setup_grid_detection_templates_verbose(scanner, object_locations, 2, 2);