TunaCode has announced the release of CUVI Lib v0.3 (Beta version) for Windows 32 and 64 Systems. A copy can be downloaded from http://www.cuvilib.com/downloads.
CUVI Lib (CUDA for Vision and Imaging Lib) is an add-on library for NPP (NVIDIA Performance Primitives) and includes several advanced computer vision and image processing functions presently not available in NPP. This version of CUVI Lib supports, among others:
Taking inspiration from genetic screening techniques, researchers from MIT and Harvard have demonstrated a way to build better artificial visual systems with the help of low-cost, high-performance gaming hardware.
The neural processing involved in visually recognizing even the simplest object in a natural environment is profound — and profoundly difficult to mimic. Neuroscientists have made broad advances in understanding the visual system, but much of the inner workings of biologically based systems remain a mystery.
Using Graphics Processing Units (GPUs) — the same technology video game designers use to render life-like graphics — MIT and Harvard researchers are now making progress faster than ever before. “We made a powerful computing system that delivers over hundred fold speed-ups relative to conventional methods,” said Nicolas Pinto, a PhD candidate in James DiCarlo’s lab at the McGovern Institute for Brain Research at MIT. “With this extra computational power, we can discover new vision models that traditional methods miss.” Pinto co-authored the PLoS study with David Cox of the Visual Neuroscience Group at the Rowland Institute at Harvard.
This paper by Wojek et al. presents a fast object class localization framework from TU Darmstadt implemented on a data parallel architecture currently available in recent computers. Our case study, the implementation of Histograms of Oriented Gradients (HOG) descriptors, shows that just by using this recent programming model we can easily speed up an original CPU-only implementation by a factor of 34 (with disk IO) / 109 (processing only), making it unnecessary to use early rejection cascades that sacrifice classification performance, even in real-time conditions. Using recent techniques to program the Graphics Processing Unit (GPU) allows our method to scale up to the latest, as well as to future improvements of the hardware.(Sliding-Windows for Rapid Object Class Localization: a Parallel Technique. C. Wojek, G. Dorko, A. Schulz, B. Schiele.30th DAGM Symposium (DAGM 2008), pp. 71-81, Munich, Germany)
GPU4Vision is a project founded by the Institute for Computer Graphics and Vision, Graz University of Technology dealing with fast computer vision algorithms for tasks like basic image processing, segmentation, motion, stereo etc. On the GPU4Vision website you can take a look at the project’s latest scientific publications, watch demo videos of algorithms and even download and evaluate some of them on your own PC. (GPU4Vision – Website)
This work describes the implementation of a real-time visual tracker that targets the position and 3D pose of objects (specifically faces) in video sequences. The use of GPUs for the computation and efficient sparse-template-based particle filtering allows real-time processing even when tracking multiple faces simultaneously in high-resolution video frames. Using a GPU and the NVIDIA CUDA technology, performance improvements as large as ten times compared to a similar CPU-only tracker are achieved. (Real-time Visual Tracker by Stream Processing. Oscar Mateo Lozano, and Kazuhiro Otsuka. Journal of Signal Processing Systems.)
This paper by Cabido et al. presents a real-time object tracking algorithm, based on the hybridization of particle filtering (PF) and a multi-scale local search (MSLS) algorithm, for both CPU and GPU architectures. The developed system provides successful results in precise tracking of single and multiple targets in monocular video, operating in real-time at 70 frames per second for 640 × 480 video resolutions on the GPU, up to 1100% faster than the CPU version of the algorithm. (Multiscale and local search methods for real time region tracking with particle filters: local search driven by adaptive scale estimation on GPUs. Raul Cabido, Antonio S. Montemayor, Juan Jose Pantrigo, and Bryson R. Payne. Machine Vision and Applications, Springer, 2008.)
A multi-view stereo evaluation has been proposed by Steve Seitz et al. The challenge involves recovering 3D reconstructions of complete objects from a large number of views. Among the reported techniques, two out of nine make an intensive usage of GPUs, both yielding large speedups: the work by Pons, Keriven and Labatut that took part in the original competition at CVPR06, and the work by Hornung and Kobbelt. Running times, accuracy and completeness of the methods are reported here. (Steve Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), New York, 2006.)
The computational power of GPU-based algorithms is receiving increased attention in research on Computer Vision and 3D stereo reconstruction from images. In this context one of the most important ingredients for any 3D stereo reconstruction technique is the estimation of photo consistency. This ECCV06 paper presents a new, illumination invariant photo consistency measure for high quality, volumetric 3D reconstruction from calibrated images. In contrast to current standard methods such as normalized cross-correlation it supports unconstrained camera setups and non-planar surface approximations. The paper shows how this measure as well as the other important stages of the volumetric reconstruction pipeline can be implemented in a highly efficient way by exploiting current graphics processors. The authors’ GPU implementation achieves speedups up to a factor of 85 in comparison to CPU-based algorithms, and allows reconstruction of high quality models with computation times of only a few seconds to minutes, even for large numbers of cameras and high volumetric resolutions. (Robust and Efficient Photo-Consistency Estimation for Volumetric 3D Reconstruction.Alexander Hornung and Leif Kobbelt. European Conference on Computer Vision (ECCV 2006), LNCS, vol. 3952, Springer, 179-190.)
GPU_KLT is an implementation (using OpenGL/Cg) of the popular KLT feature tracker which runs primarily on the graphics processing unit (GPU). The GPU-based implementation emulates Stan Birchfield’s KLT implementation of the original algorithm proposed by Kanade, Lucas and Tomasi (1991). GPU_KLT tracks approximately 1000 feature points within 1024×768 resolution video at 30 Hz on an ATI 1900 XT and at 25 Hz on a Nvidia Geforce 7900 GTX. It can be used for real-time computer vision systems involving object detection, structure from motion, robot navigation and video surveillance. Source code is available for research use on the GPU_KLT webpage (Sudipta N Sinha, Jan-Michael Frahm, Marc Pollefeys and Yakup Genc, “Feature Tracking and Matching in Video Using Programmable Graphics Hardware”,
submitted to Machine Vision and Applications, July 2006.)
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