February 2nd, 2014
November 4th, 2013
OpenCLIPP is a library providing processing primitives (image processing primitives in the first version) implemented with OpenCL for fast execution on dedicated computing devices like GPUs. Two interfaces are provided: C (similar to the Intel IPP and NVIDIA NPP libraries) and C++. OpenCLIPP is free for personal and commercial use. It can be downloaded from GitHub.
M. Akhloufi, A. Campagna, “OpenCLIPP: OpenCL Integrated Performance Primitives library for computer vision applications”, Proc. SPIE Electronic Imaging 2014, Intelligent Robots and Computer Vision XXXI: Algorithms and Techniques, P. 9025-31, February 2014.
October 7th, 2013
A free webinar on accelerating face-in-the-crowd recognition with GPU technology will be held on November 5th. It teaches how GPUs can be used to accelerate face detection and recognition of people in the crowd. The presentation will also cover the speakers’ use of ROS, OpenCV, OpenMP, and Armadillo libraries to develop fast reliable distributed video processing code. To register follow the link: https://www2.gotomeeting.com/register/292953058
July 23rd, 2013
This paper presents an accelerated version of copy-move image forgery detection scheme on the Graphics Processing Units or GPUs. With the replacement of analog cameras with their digital counterparts and availability of powerful image processing software packages, authentication of digital images has gained importance in the recent past. This paper focuses on improving the performance of a copy-move forgery detection scheme based on radix sort by porting it onto the GPUs. This scheme has enhanced performance and is much more efficient compared to other methods without degradation of detection results. The CPU version of the radix-sort based detection scheme was developed in Matlab and critical sections of the CPU version were coded in C-language using Matlab’s Mex interface to get the maximum performance. The GPU version was developed using Jacket GPU Engine for Matlab and performs over twelve times faster than its optimized CPU variant. The contribution this paper makes towards blind image forensics is the use of integral images for computing feature vectors of overlapping blocks in block-matching technique and acceleration of the entire copy-move forgery detection scheme on the GPUs, not found in literature.
(Jaideep Singh and Balasubramanian Raman, “A High Performance Copy-Move Image Forgery Detection Scheme on GPU”, Advances in Intelligent and Soft Computing Volume 131, 2012, pp 239-246, Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011). [DOI])
July 14th, 2013
Anatoly Baksheev, OpenCV GPU Module Team Leader at Itseez will demonstrate how to obtain and build OpenCV, its GPU module, and the sample programs. You will learn how to use the OpenCV GPU module and create your own custom GPU functions for OpenCV. Register for the July 30th webinar: http://goo.gl/5V3eA
May 31st, 2013
From a recent press release:
AMD’s APP SDK is an essential resource for developers who wish to leverage the processing power of heterogeneous computing. OpenCL™ is the primary mechanism for achieving this today, but AMD’s goal is to enable developers to accelerate applications with the programming paradigm of their choice. Toward that end, AMD has added support for heterogeneous libraries such as the newly released Bolt open source C++ template library and OpenCV computer vision library which now includes heterogeneous acceleration.
New to APP SDK 2.8.1:
Bolt: With the recent launch of Bolt 1.0, AMD has added several samples to the APP SDK to demonstrate Bolt 1.0 features. These showcase the usage of Bolt APIs such as scan, sort, reduce and transform. Other new samples highlight the ease of porting from STL and the performance benefits achieved over equivalent STL implementations. We’ve also included samples to demonstrate the different fallback options available in Bolt 1.0 when no GPU is available which ensure your code runs correctly on any platform.
OpenCV: AMD has been working closely with the OpenCV open source community to add heterogeneous acceleration capability to the world’s most popular computer vision library. These changes are already integrated into OpenCV and are readily available for developers who want to improve performance and efficiency of their computer vision applications. AMD has included samples to illustrate these improvements and highlight how simple it is to include them in your app.
GCN: AMD recently launched its new Graphics Core Next (GCN) architecture on several AMD products. GCN is based on a scalar architecture vs. the VLIW vector architecture of prior generations, so hand-tuned vectorization to optimize hardware utilization is no longer needed. We’ve modified several samples in AMD APP SDK 2.8.1 to show the ease of writing scalar code as compared to vectorization.
For more information, see developer.amd.com.
March 12th, 2013
This 1-hour webinar (June 11, 10am-11am PST) introduces the powerful OpenCV library, shows how this library has been accelerated using CUDA on NVIDIA GPUs, and demonstrates how to use the OpenCV GPU library to create lightning-fast applications. Free registration: http://bit.ly/11eqoaJ
May 17th, 2012
Recently, general-purpose computing on graphics processing units (GPGPU) has been enabled on mobile devices thanks to the emerging heterogeneous programming models such as OpenCL. The capability of GPGPU on mobile devices opens a new era for mobile computing and can enable many computationally demanding computer vision algorithms on mobile devices. As a case study, this paper proposes to accelerate an exemplar-based inpainting algorithm for object removal on a mobile GPU using OpenCL. We discuss the methodology of exploring the parallelism in the algorithm as well as several optimization techniques. Experimental results demonstrate that our optimization strategies for mobile GPUs have significantly reduced the processing time and make computationally intensive computer vision algorithms feasible for a mobile device. To the best of the authors’ knowledge, this work is the first published implementation of general-purpose computing using OpenCL on mobile GPUs.
(Guohui Wang, Yingen Xiong, Jay Yun and Joseph R. Cavallaro: “Accelerating Computer Vision Algorithms Using OpenCL on the Mobile GPU – A Case Study”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, May 2013, to appear. [PDF])
March 18th, 2012
TunaCode has released CUVILib v1.2, a library to accelerate imaging and computer vision applications. CUVILib adds acceleration to Imaging applications from Medical, Industrial and Defense domains. It delivers very high performance and supports both CUDA and OpenCL. Modules include color operations (demosaic, conversions, correction etc), linear/non-linear filtering, feature extraction & tracking, motion estimation, image transforms and image statistics.
More information, including a free trial version: http://www.cuvilib.com/
May 29th, 2011
Modern GPUs are well suited for performing image processing tasks. We utilize their high computational performance and memory bandwidth for image segmentation purposes. We segment cardiac MRI data by means of numerical solution of an anisotropic partial differential equation of the Allen-Cahn type. We implement two different algorithms for solving the equation on the CUDA architecture. One of them is based on the Runge-Kutta-Merson method for the approximation of solutions of ordinary differential equations, the other uses the GMRES method for the numerical solution of systems of linear equations. In our experiments, the CUDA implementations of both algorithms are about 3–9 times faster than corresponding 12-threaded OpenMP implementations.
(Oberhuber T., Suzuki A., Vacata J., Žabka V., “Image segmentation using CUDA implementations of the Runge-Kutta-Merson and GMRES methods“, Journal of Math-for-Industry, 2011, vol. 3, pp. 73–79 [PDF])
Many image processing applications use the histogramming algorithm, which fills a set of bins according to the frequency of occurrence of pixel values taken from an input image. Histogramming has been mapped on a GPU prior to this work. Although significant research effort has been spent in optimizing the mapping, we show that the performance and performance predictability of existing methods can still be improved.
In this paper, we present two novel histogramming methods, both achieving a higher performance and predictability than existing methods. We discuss performance limitations for both novel methods by exploring algorithm trade-offs.
The first novel method gives an average performance increase of 33% over existing methods for non-synthetic benchmarks. The second novel method gives an average performance increase of 56% over existing methods and guarantees to be fully data independent. While the second method is specifically designed for Fermi GPU architectures, the first method is also suitable for older architectures.
(Cedric Nugteren, Gert-Jan van den Braak, Henk Corporaal, Bart Mesman: “High performance predictable histogramming on GPUs: exploring and evaluating algorithm trade-offs”, GPGPU-4: Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units. [DOI] [Paper and Source Code])