Merge pull request #2553 from ilya-lavrenov:typo

doc/tutorials/tutorials.rst

@@ -102,7 +102,7 @@ As always, we would be happy to hear your comments and receive your contribution
    .. cssclass:: toctableopencv
 
    =========== =======================================================
-   |Video|     Look here in order to find use on your video stream algoritms like: motion extraction, feature tracking and foreground extractions.
+   |Video|     Look here in order to find use on your video stream algorithms like: motion extraction, feature tracking and foreground extractions.
 
    =========== =======================================================
 

doc/tutorials/video/table_of_content_video/table_of_content_video.rst

@@ -3,7 +3,7 @@
 *video* module. Video analysis
 -----------------------------------------------------------
 
-Look here in order to find use on your video stream algoritms like: motion extraction, feature tracking and foreground extractions.
+Look here in order to find use on your video stream algorithms like: motion extraction, feature tracking and foreground extractions.
 
 .. include:: ../../definitions/noContent.rst
 

modules/contrib/doc/facerec/facerec_api.rst

@@ -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:
 

modules/contrib/doc/facerec/tutorial/facerec_gender_classification.rst

@@ -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.
 

modules/gpu/doc/introduction.rst

@@ -43,7 +43,7 @@ Utilizing Multiple GPUs
 -----------------------
 
 In the current version, each of the OpenCV GPU algorithms can use only a single GPU. So, to utilize multiple GPUs, you have to manually distribute the work between GPUs.
-Switching active devie can be done using :ocv:func:`gpu::setDevice()` function.  For more details please read Cuda C Programing Guide.
+Switching active devie can be done using :ocv:func:`gpu::setDevice()` function.  For more details please read Cuda C Programming Guide.
 
 While developing algorithms for multiple GPUs, note a data passing overhead. For primitive functions and small images, it can be significant, which may eliminate all the advantages of having multiple GPUs. But for high-level algorithms, consider using multi-GPU acceleration. For example, the Stereo Block Matching algorithm has been successfully parallelized using the following algorithm:
 

modules/legacy/src/blobtrackanalysishist.cpp

@@ -730,12 +730,12 @@ private:
         m_pFViVarRes = (int*)cvAlloc(sizeof(int)*m_Dim);
         m_Sizes = (int*)cvAlloc(sizeof(int)*m_Dim);
 
-        {   /* Create init sparce matrix: */
+        {   /* Create init sparse matrix: */
             int     i;
             for(i=0;i<m_Dim;++i)m_Sizes[i] = m_BinNum;
             m_HistMat.Realloc(m_Dim,m_Sizes,SPARSE);
             m_HistVolumeSaved = 0;
-        } /* Create init sparce matrix. */
+        } /* Create init sparse matrix. */
     } /* AllocData. */
 
     void FreeData()

modules/legacy/src/levmar.cpp

@@ -58,7 +58,7 @@ void cvLevenbergMarquardtOptimization(pointer_LMJac JacobianFunction,
                                     CvMat *X0,CvMat *observRes,CvMat *resultX,
                                     int maxIter,double epsilon)
 {
-    /* This is not sparce method */
+    /* This is not sparse method */
     /* Make optimization using  */
     /* func - function to compute */
     /* uses function to compute jacobian */