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Merge pull request #11305 from tomoaki0705:typoNVIDIA

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Alexander Alekhin
2018-04-13 12:56:41 +00:00
parent 46d85fb558 a40354d16f
commit a2d6ee2d31
4 changed files with 11 additions and 11 deletions
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doc/tutorials/gpu/gpu-basics-similarity/gpu_basics_similarity.markdown
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@@ -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