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Merge remote-tracking branch 'origin/2.4' into merge-2.4

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Conflicts:
	modules/core/include/opencv2/core/version.hpp
	modules/core/src/out.cpp
	modules/cudaimgproc/test/test_hough.cpp
	modules/gpu/doc/introduction.rst
	modules/gpu/perf/perf_imgproc.cpp
	modules/gpu/src/generalized_hough.cpp
	modules/nonfree/perf/perf_main.cpp
This commit is contained in:
Roman Donchenko
2014-04-07 14:59:34 +04:00
parent def4f5e2a3 1cee868e88
commit 4f2b126b7b
16 changed files with 43 additions and 38 deletions
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2
modules/contrib/doc/facerec/facerec_api.rst
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@@ -75,7 +75,7 @@ Moreover every :ocv:class:`FaceRecognizer` supports the:
Setting the Thresholds
+++++++++++++++++++++++
Sometimes you run into the situation, when you want to apply a threshold on the prediction. A common scenario in face recognition is to tell, wether a face belongs to the training dataset or if it is unknown. You might wonder, why there's no public API in :ocv:class:`FaceRecognizer` to set the threshold for the prediction, but rest assured: It's supported. It just means there's no generic way in an abstract class to provide an interface for setting/getting the thresholds of *every possible* :ocv:class:`FaceRecognizer` algorithm. The appropriate place to set the thresholds is in the constructor of the specific :ocv:class:`FaceRecognizer` and since every :ocv:class:`FaceRecognizer` is a :ocv:class:`Algorithm` (see above), you can get/set the thresholds at runtime!
Sometimes you run into the situation, when you want to apply a threshold on the prediction. A common scenario in face recognition is to tell, whether a face belongs to the training dataset or if it is unknown. You might wonder, why there's no public API in :ocv:class:`FaceRecognizer` to set the threshold for the prediction, but rest assured: It's supported. It just means there's no generic way in an abstract class to provide an interface for setting/getting the thresholds of *every possible* :ocv:class:`FaceRecognizer` algorithm. The appropriate place to set the thresholds is in the constructor of the specific :ocv:class:`FaceRecognizer` and since every :ocv:class:`FaceRecognizer` is a :ocv:class:`Algorithm` (see above), you can get/set the thresholds at runtime!
Here is an example of setting a threshold for the Eigenfaces method, when creating the model:

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modules/contrib/doc/facerec/tutorial/facerec_gender_classification.rst
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@@ -71,7 +71,7 @@ You really don't want to create the CSV file by hand. And you really don't want
Fisherfaces for Gender Classification
--------------------------------------
If you want to decide wether a person is *male* or *female*, you have to learn the discriminative features of both classes. The Eigenfaces method is based on the Principal Component Analysis, which is an unsupervised statistical model and not suitable for this task. Please see the Face Recognition tutorial for insights into the algorithms. The Fisherfaces instead yields a class-specific linear projection, so it is much better suited for the gender classification task. `http://www.bytefish.de/blog/gender_classification <http://www.bytefish.de/blog/gender_classification>`_ shows the recognition rate of the Fisherfaces method for gender classification.
If you want to decide whether a person is *male* or *female*, you have to learn the discriminative features of both classes. The Eigenfaces method is based on the Principal Component Analysis, which is an unsupervised statistical model and not suitable for this task. Please see the Face Recognition tutorial for insights into the algorithms. The Fisherfaces instead yields a class-specific linear projection, so it is much better suited for the gender classification task. `http://www.bytefish.de/blog/gender_classification <http://www.bytefish.de/blog/gender_classification>`_ shows the recognition rate of the Fisherfaces method for gender classification.
The Fisherfaces method achieves a 98% recognition rate in a subject-independent cross-validation. A subject-independent cross-validation means *images of the person under test are never used for learning the model*. And could you believe it: you can simply use the facerec_fisherfaces demo, that's inlcuded in OpenCV.