Nsight Eclipse Edition is a full-featured IDE, powered by the Eclipse platform that provides a complete integrated development environment to edit, build, debug and profile CUDA C/C++ applications on MAC and Linux platforms. The combination of CUDA aware source editor and powerful debugging and profiling tools make Nsight Eclipse Edition the ultimate development platform for heterogeneous computing.
A preview of CUDA toolkit 5 is already available for Registered developers and NVIDIA is expected to roll out the production release soon. Besides habitual addition of more image processing functionality, the new toolkit offers some great features including:
- Dynamic parallelism
- GPUDirect for clusters (RDMA)
- GPU object linking
- NVIDIA Nsight, Eclipse Edition
It’s one thing to compare GPU code performance with CPU code performance. If the algorithm is parallel, GPU would beat CPU any day. In our case, CUVI beats the best (performance wise) CPU primitives library on the planet, Intel(r) IPP. Take a look at the performance figures.
CUVILib has finally came out of Beta. We have added a lot more functionality and made sure that it runs smooth on mission-critical applications. Its simple API, magnitudes better performance than competing solutions and cross-platform support provides you a complete Imaging package. Before we get into what’s new in version 1.2 here are some useful links worth checking out:
Ever wanted to add color and edit really, really old movies? Our grand parents did not have HD cameras which means today, we have hundreds of thousands of hours of video recorded a few decades ago. Movies, documentaries and family videos gathering dust in some archive or a shelf in your home. With the new recoloring and digitizing technology, it’s now possible to digitize (meaning you can open them in video editing tools on your computer) and recolor old, shaky videos. Some very interesting work is being done in Sweden by a company which provides software for this. Film studios use this software to restore old movies. It’s called AgiScan.
The era of the next-generation graphics is finally upon us. If you’ve been hankering after a graphics card upgrade lately and wanted to see what NVIDIA’s reply to AMD’s Radeon HD 7970 is, wait no further. The green squad has taken the cloaks off of their GeForce GTX 680, a new piece of graphics silicon targeted at consumers and the enthusiast mob based on its latest Kepler architecture. NVIDIA claims that its new GTX 680 is the fastest and most power-efficient GPU ever made offering significant performance enhancements over its rivals.
We are pleased to announce the new version of CUVI lib. Our team has been working hard on this release to bring significant improvements over the previous version. You can download the latest release from this link.
Changes from version 0.5:
- Pyramidal Optical Flow (Lucas-Kanade)
- Feature selection (KLT | Harris | Peter)
- Feature tracking (KLT)
- Lightning Adjustment
- Bayer to RGB conversion
CUVI library comes with a lot of image processing building blocks that can be used to build countless applications. For example the Feature Detection & Tracking module of CUVI can be used for motion detection in a live video stream, intrusion detection and tracking an object of interest throughout a video stream or series of cameras. The processing pipeline for motion detection goes as follows:
The next release of CUVI library is due within next 30 days and we are pleased to announce that it’ll be having lots of functions from Image Enchantments domain. Our filter module just got better and now support dozens of predefined filters as well as the option to add your own custom taps and anchor position. One particular function that I’m excited about in the new release is adjust which is equivalent to MATLAB’s imadjust function.
CUVI version 0.6 is under development. One of its new features is the histogram equalization of images.
Histogram Equalization is an image processing technique for contrast enhancement of low contrast images. Our implementation is 3 to 6 times faster than the one offered by OpenCV 2.2, with same output. The speed depends on image size as well as image intensity values. Currently, only 8 bit greyscale images are supported, and we hope to add support for other image types in future releases of CUVI.