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 Continue reading “CUVI Version 0.8 Released”
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.
With CUVI version 0.5 release, significant changes were made to make sure utilizing the power of GPUs for your Imaging and Vision applications is even more easier. In version 0.5, CuviImage object is introduced – it holds image on the GPU so you know whenever you create a CuviImage, the image always resides on the GPU, saving you complexity of creating and managing device memories yourself. In addition to just creating and holding the image on the GPU, CuviImage also maintains the basic information of the image like rows, columns (matrix style), width step, pixel depth, number of channels etc.
This guide explains how to run OpenCL applications in MSVS using AMD Accelerated Parallel Processing SDK formerly known as ATI Stream SDK. I have used the following software for this guide:
- MSVS 2008 Professional Edition
- AMD APP SDK v2.4 [Download Link]
CUVI version 0.5 is cooked in our labs and we are doing testing and documentation at the moment. The new release will be out anytime in the coming week. We have been working for almost six months on the new framework that couldn’t get any simpler and easy to use. In this release we are also enabling our premium feature detectors that are 10 times faster than OpenCV 2.2
OpenCV version 2.2 was released in December last year with GPU support. This GPU module 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.
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.