How to Build OpenCV 2.2 with GPU (CUDA) on Windows 7

OpenCV version 2.2 was released in December last year with GPU support. This GPU moduimagele was written in CUDA which means it’s hardware dependent (only NVIDIA CUDA enabled GPUs can make use of this module). It has opened the gateways of GPU accelerated Image Processing and Computer Vision available right in OpenCV. Using it can be a nightmare for most of you so I decided to log my way of making it work which is not very much different from what’s on the documentation with some added steps. 

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Internet Explorer is now GPU Accelerated

The time is fast approaching when every significant app will make use of the vast parallel resource pool of the GPUs. This stampede towards GPU-accelerated-computing just got a boost from the release of the all new Internet Explorer 9. NVIDIA worked with Microsoft since almost the start of IE9 development cycle to make sure every possible GPU resource is utilized. IE9 is definitely a leap forward from it’s predecessors in that it opens up a new avenue for the future browsers: GPUs.

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EVGA announces first dual-Fermi graphics card

EVGA has announced GTX 460 2Win, the first dual-Fermi graphics card featuring 662 CUDA cores (at 700 MHz) and 2GB of DDR5 memory (3600 MHz effective). According to the company, this combination of two low end Fermi chips will beat the 3D Mark score of the NVIDIA GTX 580. That’s not a biggie, as GTX 580 has only 512 CUDA core, but the better news is that GTX 460 2Win will cost less than GTX 580, says EVGA.

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How to Run CUDA In Visual Studio 2010

Microsoft has given a complete facelift to the new Visual Studio 2010. A whole bunch of new features and removal of a couple of them. With the new VS 2010, you cannot define custom build rules with .rules file as it was done before. Now there’s a whole new bunch of modules that need to imagebe specified in order to make a set of custom build rules to work with Visual Studio 2010 project. This blog explains the steps involved in making CUDA work on the new Visual Studio 2010.

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How to Apply Filters to Images Using CUDA

Image filtering is one of the most basic utility of image processing and computer vision. Any image processing application, like feature detection, is composed of applying a series of filters to the image. After reading this guide, you’ll be able to efficiently apply filters to images using shared memory of CUDA architecture. Here’s a step by step guide to write your own filter of any type and size. For simplicity I’ll use a 16 bit unsigned grey scale image in this tutorial.
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How to Use OpenCV With CUVI

From day 1 we have focused on making CUVI compatible with the existing Vision and Imaging libraries and what comes to mind right away is: OpenCV. We have been asked a many times whether CUVI functions work with OpenCV. Here’s a little tutorial on how you can use OpenCV’s Image reading and writing functions with CUVI functions.

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Profiling CUDA Applications on Windows with NVIDIA Compute Visual Profiler

Writing applications that use the massive parallel compute power of the CUDA capable GPUs has been made even more simpler with the release of CUDA Toolkit 3.2 RC. What’s more exciting is that it comes with an improved CUDA Visual Profiler which lets you profile every minute aspect of your application. Today I am going to walk you through the simple process of profiling your CUDA application.

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