Merge pull request #11305 from tomoaki0705:typoNVIDIA

cmake/templates/cvconfig.h.in

@@ -49,13 +49,13 @@
 /* C= */
 #cmakedefine HAVE_CSTRIPES
 
-/* NVidia Cuda Basic Linear Algebra Subprograms (BLAS) API*/
+/* NVIDIA CUDA Basic Linear Algebra Subprograms (BLAS) API*/
 #cmakedefine HAVE_CUBLAS
 
-/* NVidia Cuda Runtime API*/
+/* NVIDIA CUDA Runtime API*/
 #cmakedefine HAVE_CUDA
 
-/* NVidia Cuda Fast Fourier Transform (FFT) API*/
+/* NVIDIA CUDA Fast Fourier Transform (FFT) API*/
 #cmakedefine HAVE_CUFFT
 
 /* IEEE1394 capturing support */
@@ -127,10 +127,10 @@
 /* Microsoft Media Foundation Capture library */
 #cmakedefine HAVE_MSMF
 
-/* NVidia Video Decoding API*/
+/* NVIDIA Video Decoding API*/
 #cmakedefine HAVE_NVCUVID
 
-/* NVidia Video Encoding API*/
+/* NVIDIA Video Encoding API*/
 #cmakedefine HAVE_NVCUVENC
 
 /* OpenCL Support */

doc/tutorials/gpu/gpu-basics-similarity/gpu_basics_similarity.markdown

@@ -8,7 +8,7 @@ Goal
 In the @ref tutorial_video_input_psnr_ssim tutorial I already presented the PSNR and SSIM methods for checking
 the similarity between the two images. And as you could see, the execution process takes quite some
 time , especially in the case of the SSIM. However, if the performance numbers of an OpenCV
-implementation for the CPU do not satisfy you and you happen to have an NVidia CUDA GPU device in
+implementation for the CPU do not satisfy you and you happen to have an NVIDIA CUDA GPU device in
 your system, all is not lost. You may try to port or write your owm algorithm for the video card.
 
 This tutorial will give a good grasp on how to approach coding by using the GPU module of OpenCV. As
@@ -187,7 +187,7 @@ introduce asynchronous OpenCV GPU calls too with the help of the @ref cv::cuda::
 Result and conclusion
 ---------------------
 
-On an Intel P8700 laptop CPU paired with a low end NVidia GT220M, here are the performance numbers:
+On an Intel P8700 laptop CPU paired with a low end NVIDIA GT220M, here are the performance numbers:
 @code
 Time of PSNR CPU (averaged for 10 runs): 41.4122 milliseconds. With result of: 19.2506
 Time of PSNR GPU (averaged for 10 runs): 158.977 milliseconds. With result of: 19.2506

modules/dnn/src/caffe/opencv-caffe.proto

@@ -50,7 +50,7 @@ syntax = "proto2";
 
 package opencv_caffe;
 
-// NVidia's Caffe feature is used to store fp16 weights, https://github.com/NVIDIA/caffe:
+// NVIDIA's Caffe feature is used to store fp16 weights, https://github.com/NVIDIA/caffe:
 // Math and storage types
 enum Type {
   DOUBLE = 0;
@@ -72,10 +72,10 @@ message BlobProto {
   repeated double double_data = 8 [packed = true];
   repeated double double_diff = 9 [packed = true];
 
-  // NVidia's Caffe fields begin.
+  // NVIDIA's Caffe fields begin.
   optional Type raw_data_type = 10;
   optional bytes raw_data = 12 [packed = false];
-  // NVidia's Caffe fields end.
+  // NVIDIA's Caffe fields end.
 
   // 4D dimensions -- deprecated.  Use "shape" instead.
   optional int32 num = 1 [default = 0];

modules/imgproc/src/deriv.cpp

@@ -547,7 +547,7 @@ static bool ocl_Laplacian5(InputArray _src, OutputArray _dst,
     size_t src_step = _src.step(), src_offset = _src.offset();
     const size_t tileSizeYmax = wgs / tileSizeX;
 
-    // workaround for Nvidia: 3 channel vector type takes 4*elem_size in local memory
+    // workaround for NVIDIA: 3 channel vector type takes 4*elem_size in local memory
     int loc_mem_cn = dev.vendorID() == ocl::Device::VENDOR_NVIDIA && cn == 3 ? 4 : cn;
 
     if (((src_offset % src_step) % esz == 0) &&