FIx misc. source and comment typos

Found via `codespell -q 3 -S ./3rdparty,./modules -L amin,ang,atleast,dof,endwhile,hist,uint`

backporting of commit: 32aba5e64b

doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown

@@ -253,8 +253,8 @@ Here is explained in detail the code for the real time application:
     @code{.cpp}
     RobustMatcher rmatcher;                                                          // instantiate RobustMatcher
 
-    cv::FeatureDetector * detector = new cv::OrbFeatureDetector(numKeyPoints);       // instatiate ORB feature detector
-    cv::DescriptorExtractor * extractor = new cv::OrbDescriptorExtractor();          // instatiate ORB descriptor extractor
+    cv::FeatureDetector * detector = new cv::OrbFeatureDetector(numKeyPoints);       // instantiate ORB feature detector
+    cv::DescriptorExtractor * extractor = new cv::OrbDescriptorExtractor();          // instantiate ORB descriptor extractor
 
     rmatcher.setFeatureDetector(detector);                                           // set feature detector
     rmatcher.setDescriptorExtractor(extractor);                                      // set descriptor extractor

doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.markdown

@@ -29,8 +29,8 @@ This distance is equivalent to count the number of different elements for binary
 
 To filter the matches, Lowe proposed in @cite Lowe:2004:DIF:993451.996342 to use a distance ratio test to try to eliminate false matches.
 The distance ratio between the two nearest matches of a considered keypoint is computed and it is a good match when this value is below
-a thresold. Indeed, this ratio allows helping to discriminate between ambiguous matches (distance ratio between the two nearest neighbors is
-close to one) and well discriminated matches. The figure below from the SIFT paper illustrates the probability that a match is correct
+a threshold. Indeed, this ratio allows helping to discriminate between ambiguous matches (distance ratio between the two nearest neighbors
+is close to one) and well discriminated matches. The figure below from the SIFT paper illustrates the probability that a match is correct
 based on the nearest-neighbor distance ratio test.
 
 ![](images/Feature_FlannMatcher_Lowe_ratio_test.png)

doc/tutorials/imgcodecs/raster-gdal/raster_io_gdal.markdown

@@ -15,7 +15,7 @@ The primary objectives for this tutorial:
 
 -   How to use OpenCV [imread](@ref imread) to load satellite imagery.
 -   How to use OpenCV [imread](@ref imread) to load SRTM Digital Elevation Models
--   Given the corner coordinates of both the image and DEM, correllate the elevation data to the
+-   Given the corner coordinates of both the image and DEM, correlate the elevation data to the
     image to find elevations for each pixel.
 -   Show a basic, easy-to-implement example of a terrain heat map.
 -   Show a basic use of DEM data coupled with ortho-rectified imagery.

doc/tutorials/imgproc/gausian_median_blur_bilateral_filter/gausian_median_blur_bilateral_filter.markdown

@@ -157,7 +157,7 @@ already known by now.
     -   *src*: Source image
     -   *dst*: Destination image
     -   *Size(w, h)*: The size of the kernel to be used (the neighbors to be considered). \f$w\f$ and
-        \f$h\f$ have to be odd and positive numbers otherwise thi size will be calculated using the
+        \f$h\f$ have to be odd and positive numbers otherwise the size will be calculated using the
         \f$\sigma_{x}\f$ and \f$\sigma_{y}\f$ arguments.
     -   \f$\sigma_{x}\f$: The standard deviation in x. Writing \f$0\f$ implies that \f$\sigma_{x}\f$ is
         calculated using kernel size.

doc/tutorials/imgproc/morph_lines_detection/morph_lines_detection.md

@@ -30,7 +30,7 @@ Two of the most basic morphological operations are dilation and erosion. Dilatio
 
     ![Dilation on a Grayscale Image](images/morph6.gif)
 
--   __Erosion__: The vise versa applies for the erosion operation. The value of the output pixel is the <b><em>minimum</em></b> value of all the pixels that fall within the structuring element's size and shape. Look the at the example figures below:
+-   __Erosion__: The vice versa applies for the erosion operation. The value of the output pixel is the <b><em>minimum</em></b> value of all the pixels that fall within the structuring element's size and shape. Look the at the example figures below:
 
     ![Erosion on a Binary Image](images/morph211.png)
 

doc/tutorials/introduction/transition_guide/transition_guide.markdown

@@ -191,7 +191,7 @@ brief->compute(gray, query_kpts, query_desc); //Compute brief descriptors at eac
 
 OpenCL {#tutorial_transition_hints_opencl}
 ------
-All specialized `ocl` implemetations has been hidden behind general C++ algorithm interface. Now the function execution path can be selected dynamically at runtime: CPU or OpenCL; this mechanism is also called "Transparent API".
+All specialized `ocl` implementations has been hidden behind general C++ algorithm interface. Now the function execution path can be selected dynamically at runtime: CPU or OpenCL; this mechanism is also called "Transparent API".
 
 New class cv::UMat is intended to hide data exchange with OpenCL device in a convenient way.
 

doc/tutorials/ios/video_processing/video_processing.markdown

@@ -101,7 +101,7 @@ using namespace cv;
 }
 @endcode
 In this case, we initialize the camera and provide the imageView as a target for rendering each
-frame. CvVideoCamera is basically a wrapper around AVFoundation, so we provie as properties some of
+frame. CvVideoCamera is basically a wrapper around AVFoundation, so we provide as properties some of
 the AVFoundation camera options. For example we want to use the front camera, set the video size to
 352x288 and a video orientation (the video camera normally outputs in landscape mode, which results
 in transposed data when you design a portrait application).

doc/tutorials/ml/non_linear_svms/non_linear_svms.markdown

@@ -13,7 +13,7 @@ In this tutorial you will learn how to:
 Motivation
 ----------
 
-Why is it interesting to extend the SVM optimation problem in order to handle non-linearly separable
+Why is it interesting to extend the SVM optimization problem in order to handle non-linearly separable
 training data? Most of the applications in which SVMs are used in computer vision require a more
 powerful tool than a simple linear classifier. This stems from the fact that in these tasks __the
 training data can be rarely separated using an hyperplane__.

doc/tutorials/objdetect/cascade_classifier/cascade_classifier.markdown

@@ -113,7 +113,7 @@ This tutorial code's is shown lines below. You can also download it from
 Result
 ------
 
--#  Here is the result of running the code above and using as input the video stream of a build-in
+-#  Here is the result of running the code above and using as input the video stream of a built-in
     webcam:
 
     ![](images/Cascade_Classifier_Tutorial_Result_Haar.jpg)

doc/tutorials/video/optical_flow/optical_flow.markdown

@@ -15,7 +15,7 @@ Optical Flow
 ------------
 
 Optical flow is the pattern of apparent motion of image objects between two consecutive frames
-caused by the movemement of object or camera. It is 2D vector field where each vector is a
+caused by the movement of object or camera. It is 2D vector field where each vector is a
 displacement vector showing the movement of points from first frame to second. Consider the image
 below (Image Courtesy: [Wikipedia article on Optical Flow](http://en.wikipedia.org/wiki/Optical_flow)).