fixed a few more typos in the docs.

modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst

@@ -1407,6 +1407,6 @@ The function computes the rectification transformations without knowing intrinsi
 
 .. [BouguetMCT] J.Y.Bouguet. MATLAB calibration tool. http://www.vision.caltech.edu/bouguetj/calib_doc/
 
-.. [Hartley99] Hartley, R.I., “Theory and Practice of Projective Rectification”. IJCV 35 2, pp 115-127 (1999)
+.. [Hartley99] Hartley, R.I., Theory and Practice of Projective Rectification. IJCV 35 2, pp 115-127 (1999)
 
 .. [Zhang2000] Z. Zhang. A Flexible New Technique for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330-1334, 2000.

modules/core/doc/clustering.rst

@@ -58,9 +58,7 @@ partition
 -------------
 Splits an element set into equivalency classes.
 
-.. ocv:function:: template<typename _Tp, class _EqPredicate> int
-
-.. ocv:function:: partition( const vector<_Tp>& vec, vector<int>& labels, _EqPredicate predicate=_EqPredicate())
+.. ocv:function:: template<typename _Tp, class _EqPredicate> int partition( const vector<_Tp>& vec, vector<int>& labels, _EqPredicate predicate=_EqPredicate())
 
     :param vec: Set of elements stored as a vector.
 
@@ -76,4 +74,4 @@ http://en.wikipedia.org/wiki/Disjoint-set_data_structure
 . The function
 returns the number of equivalency classes.
 
-.. [Arthur2007] Arthur and S. Vassilvitskii “k-means++: the advantages of careful seeding”, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 2007
+.. [Arthur2007] Arthur and S. Vassilvitskii. k-means++: the advantages of careful seeding, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 2007

modules/gpu/doc/object_detection.rst

@@ -67,12 +67,7 @@ Interfaces of all methods are kept similar to the ``CPU HOG`` descriptor and det
 
 gpu::HOGDescriptor::HOGDescriptor
 -------------------------------------
-.. ocv:function:: gpu::HOGDescriptor::HOGDescriptor(Size win_size=Size(64, 128),
-   Size block_size=Size(16, 16), Size block_stride=Size(8, 8),
-   Size cell_size=Size(8, 8), int nbins=9,
-   double win_sigma=DEFAULT_WIN_SIGMA,
-   double threshold_L2hys=0.2, bool gamma_correction=true,
-   int nlevels=DEFAULT_NLEVELS)??check the output??
+.. ocv:function:: gpu::HOGDescriptor::HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16), Size block_stride=Size(8, 8), Size cell_size=Size(8, 8), int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA, double threshold_L2hys=0.2, bool gamma_correction=true, int nlevels=DEFAULT_NLEVELS)
 
     Creates the ``HOG`` descriptor and detector.
 
@@ -114,7 +109,7 @@ gpu::HOGDescriptor::getBlockHistogramSize
 
 gpu::HOGDescriptor::setSVMDetector
 --------------------------------------
-.. ocv:function:: void gpu::HOGDescriptor::setSVMDetector(const vector<float>\& detector)
+.. ocv:function:: void gpu::HOGDescriptor::setSVMDetector(const vector<float>& detector)
 
     Sets coefficients for the linear SVM classifier.
 
@@ -146,9 +141,7 @@ gpu::HOGDescriptor::getPeopleDetector64x128
 
 gpu::HOGDescriptor::detect
 ------------------------------
-.. ocv:function:: void gpu::HOGDescriptor::detect(const GpuMat\& img,
-   vector<Point>\& found_locations, double hit_threshold=0,
-   Size win_stride=Size(), Size padding=Size())??see output??
+.. ocv:function:: void gpu::HOGDescriptor::detect(const GpuMat& img, vector<Point>& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size())
 
     Performs object detection without a multi-scale window.
 
@@ -166,10 +159,7 @@ gpu::HOGDescriptor::detect
 
 gpu::HOGDescriptor::detectMultiScale
 ----------------------------------------
-.. ocv:function:: void gpu::HOGDescriptor::detectMultiScale(const GpuMat\& img,
-   vector<Rect>\& found_locations, double hit_threshold=0,
-   Size win_stride=Size(), Size padding=Size(),
-   double scale0=1.05, int group_threshold=2)??the same??
+.. ocv:function:: void gpu::HOGDescriptor::detectMultiScale(const GpuMat& img, vector<Rect>& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size(), double scale0=1.05, int group_threshold=2)
 
     Performs object detection with a multi-scale window.
 
@@ -191,9 +181,7 @@ gpu::HOGDescriptor::detectMultiScale
 
 gpu::HOGDescriptor::getDescriptors
 --------------------------------------
-.. ocv:function:: void gpu::HOGDescriptor::getDescriptors(const GpuMat\& img,
-   Size win_stride, GpuMat\& descriptors,
-   int descr_format=DESCR_FORMAT_COL_BY_COL)?? the same??
+.. ocv:function:: void gpu::HOGDescriptor::getDescriptors(const GpuMat& img, Size win_stride, GpuMat& descriptors, int descr_format=DESCR_FORMAT_COL_BY_COL)
 
     Returns block descriptors computed for the whole image. The function is mainly used to learn the classifier.
 
@@ -247,7 +235,7 @@ Cascade classifier class used for object detection.
 
 gpu::CascadeClassifier_GPU::CascadeClassifier_GPU
 -----------------------------------------------------
-.. ocv:function:: gpu::CascadeClassifier_GPU(const string\& filename)
+.. ocv:function:: gpu::CascadeClassifier_GPU(const string& filename)
 
     Loads the classifier from a file.
 
@@ -269,7 +257,7 @@ gpu::CascadeClassifier_GPU::empty
 
 gpu::CascadeClassifier_GPU::load
 ------------------------------------
-.. ocv:function:: bool gpu::CascadeClassifier_GPU::load(const string\& filename)
+.. ocv:function:: bool gpu::CascadeClassifier_GPU::load(const string& filename)
 
     Loads the classifier from a file. The previous content is destroyed.
 
@@ -287,7 +275,7 @@ gpu::CascadeClassifier_GPU::release
 
 gpu::CascadeClassifier_GPU::detectMultiScale
 ------------------------------------------------
-.. ocv:function:: int gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat\& image, GpuMat\& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size())
+.. ocv:function:: int gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size())
 
     Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
 

modules/highgui/doc/qt_new_functions.rst

@@ -230,9 +230,9 @@ createOpenGLCallback
 ------------------------
 Creates a callback function called to draw OpenGL on top the the image display by ``windowname``.
 
-.. ocv:function:: void createOpenGLCallback( const string& window_name, OpenGLCallback callbackOpenGL, void* userdata CV_DEFAULT(NULL), double angle CV_DEFAULT(-1), double zmin CV_DEFAULT(-1), double zmax CV_DEFAULT(-1)
+.. ocv:function:: void createOpenGLCallback( const string& window_name, OpenGLCallback callbackOpenGL, void* userdata =NULL, double angle=-1, double zmin=-1, double zmax=-1)
 
-.. ocv:cfunction:: void cvCreateOpenGLCallback( const char* windowName, CvOpenGLCallback callbackOpenGL, void* userdata=NULL, double angle=-1, double zmin=-1, double zmax=-1
+.. ocv:cfunction:: void cvCreateOpenGLCallback( const char* windowName, CvOpenGLCallback callbackOpenGL, void* userdata=NULL, double angle=-1, double zmin=-1, double zmax=-1 )
 
     :param window_name: Name of the window.
 
@@ -308,9 +308,9 @@ createButton
 ----------------
 Attaches a button to the control panel.
 
-.. ocv:function:: createButton( const string& button_name CV_DEFAULT(NULL),ButtonCallback on_change CV_DEFAULT(NULL), void* userdata CV_DEFAULT(NULL), int button_type CV_DEFAULT(CV_PUSH_BUTTON), int initial_button_state CV_DEFAULT(0))
+.. ocv:function:: createButton( const string& button_name=NULL, ButtonCallback on_change=NULL, void* userdata=NULL, int button_type=CV_PUSH_BUTTON, int initial_button_state=0 )
 
-.. ocv:cfunction:: cvCreateButton( const char* buttonName=NULL, CvButtonCallback onChange=NULL, void* userdata=NULL, int buttonType=CV_PUSH_BUTTON, int initialButtonState=0
+.. ocv:cfunction:: cvCreateButton( const char* buttonName=NULL, CvButtonCallback onChange=NULL, void* userdata=NULL, int buttonType=CV_PUSH_BUTTON, int initialButtonState=0 )
 
     :param  button_name: Name of the button.
 

modules/highgui/doc/reading_and_writing_images_and_video.rst

@@ -266,6 +266,7 @@ VideoCapture::read
 Grabs, decodes and returns the next video frame.
 
 .. ocv:function:: VideoCapture& VideoCapture::operator >> (Mat& image)
+
 .. ocv:function:: bool VideoCapture::read(Mat& image)
 
 .. ocv:pyfunction:: cv2.VideoCapture.read([image]) -> successFlag, image
@@ -288,6 +289,7 @@ Returns the specified ``VideoCapture`` property
 .. ocv:pyfunction:: cv2.VideoCapture.get(propId) -> retval
 
 .. ocv:cfunction:: double cvGetCaptureProperty( CvCapture* capture, int propId )
+
 .. ocv:pyoldfunction:: cv.GetCaptureProperty(capture, propId)->double
 
 
@@ -343,6 +345,7 @@ Sets a property in the ``VideoCapture``.
 .. ocv:pyfunction:: cv2.VideoCapture.set(propId, value) -> retval
 
 .. ocv:cfunction:: int cvSetCaptureProperty( CvCapture* capture, int propId, double value )
+
 .. ocv:pyoldfunction:: cv.SetCaptureProperty(capture, propId, value)->None
 
     :param propId: Property identifier. It can be one of the following:
@@ -402,6 +405,7 @@ VideoWriter::VideoWriter
 VideoWriter constructors
 
 .. ocv:function:: VideoWriter::VideoWriter()
+
 .. ocv:function:: VideoWriter::VideoWriter(const string& filename, int fourcc, double fps, Size frameSize, bool isColor=true)
 
 .. ocv:pyfunction:: cv2.VideoWriter([filename, fourcc, fps, frameSize[, isColor]]) -> <VideoWriter object>
@@ -461,6 +465,7 @@ VideoWriter::write
 Writes the next video frame
 
 .. ocv:function:: VideoWriter& VideoWriter::operator << (const Mat& image)
+
 .. ocv:function:: void VideoWriter::write(const Mat& image)
 
 .. ocv:pyfunction:: cv2.VideoWriter.write(image) -> None

modules/imgproc/doc/feature_detection.rst

@@ -222,7 +222,7 @@ Determines strong corners on an image.
 
 .. ocv:pyfunction:: cv2.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) -> corners
 
-.. ocv:cfunction:: void cvGoodFeaturesToTrack( const CvArr* image, CvArr* eigImage, CvArr* tempImage CvPoint2D32f* corners, int* cornerCount, double qualityLevel, double minDistance, const CvArr* mask=NULL, int blockSize=3, int useHarris=0, double k=0.04 )
+.. ocv:cfunction:: void cvGoodFeaturesToTrack( const CvArr* image, CvArr* eigImage, CvArr* tempImage, CvPoint2D32f* corners, int* cornerCount, double qualityLevel, double minDistance, const CvArr* mask=NULL, int blockSize=3, int useHarris=0, double k=0.04 )
 
 .. ocv:pyoldfunction:: cv.GoodFeaturesToTrack(image, eigImage, tempImage, cornerCount, qualityLevel, minDistance, mask=None, blockSize=3, useHarris=0, k=0.04)-> corners
 

modules/ml/doc/boosting.rst

@@ -59,9 +59,9 @@ To reduce computation time for boosted models without substantially losing accur
 **all**
 training examples are recomputed at each training iteration. Examples deleted at a particular iteration may be used again for learning some of the weak classifiers further [FHT98]_.
 
-.. _HTF01: [HTF01] Hastie, T., Tibshirani, R., Friedman, J. H. *The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics*. 2001.
+.. [HTF01] Hastie, T., Tibshirani, R., Friedman, J. H. *The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics*. 2001.
 
-.. _FHT98: [FHT98] Friedman, J. H., Hastie, T. and Tibshirani, R. Additive Logistic Regression: a Statistical View of Boosting. Technical Report, Dept. of Statistics*, Stanford University, 1998.
+.. [FHT98] Friedman, J. H., Hastie, T. and Tibshirani, R. Additive Logistic Regression: a Statistical View of Boosting. Technical Report, Dept. of Statistics*, Stanford University, 1998.
 
 CvBoostParams
 -------------