Media & Computer Vision
Media organizations, internet companies, and technology development enterprises are finding more and more needs for technical computing in research and development departments. The pervasiveness of GPUs across enterprises and the need for better, faster, and cheaper ways of performing modeling, simulation, and analytics make ArrayFire a perfect solution for industries looking for high performance applications.
Companies such as Google and Adobe have found that ArrayFire provides a unique way to leverage the power and flexibility of GPU computing without the heavy investment in development time and resources.
Example Applications
ArrayFire can help educate students and support research in many application areas including:
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Fast Computer Vision with OpenCV and ArrayFire OpenCV Blogger |
Speedup: 10X |
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Fast Computer Vision with OpenCV and ArrayFire
Authors: OpenCV Blogger The OpenCV library is the defacto standard for doing computer vision and image processing research projects. OpenCV includes several hundreds of computer vision algorithms, aimed for use in realtime vision applications. This case study shows how to use both libraries together. There is a simple example application that demonstrates using OpenCV for webcam access and ArrayFire for some basic processing routines and displaying results. Last Updated: 24 Aug 2011 |
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Video Processing |
Speedup: 10X - 20X |
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Video Processing
Authors: Google and Stanford University Video content analysis is the basis for categorizing videos and enabling search by content. Growing interest in using sparse-coding methods to extract motion features in video in support of video content analysis led to the application of AccelerEyes software and GPUs to improve performance by substantially accelerating the solution of the L1-regularized least-squares optimization problem. Last Updated: 13 Jan 2010 |
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Action Recognition with Independent Subspace Analysis Stanford University |
Speedup: 4.4X |
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Action Recognition with Independent Subspace Analysis
Authors: Quoc Le, Will Zou, Serena Yeung, Andrew Ng, Stanford University In a paper at this year's CVPR 2011, entitled "Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis", the authors explain how their unsupervised feature learning algorithm competes with other algorithms that are hand crafted or use learned features. For their training purposes, they used a multi-layered stacked convolutional ISA (Independent subspace analysis) network. An ISA is used for learning features from image patches without supervision. Last Updated: 19 Aug 2011 |
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Music Beat Analysis Georgia Tech |
Speedup: 15X |
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Music Beat Analysis
Authors: Vidhur Vohra - Georgia Tech Did you ever wonder how the music visualizer in your media player works? Watching it pulsate in synchrony with the beats of the song is almost as entertaining as listening to the song itself! Researchers have been attempting to detect beats in audio signals for many years, and there are many techniques available, from the simplest (and least accurate) to more complicated algorithms that are highly accurate. All algorithms, though, perform some form of signal processing and frequency analysis, applications highly suited to GPU Computing. Last Updated: 11 Aug 2011 |
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Optimization methods for deep learning Stanford Artificial Intelligence Laboratory |
Speedup: Improved Accuracy |
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Optimization methods for deep learning
Authors: Stanford Artificial Intelligence Laboratory Researchers at SAIL (Stanford Artificial Intelligence Laboratory), have done it again. They have successfully used AccelerEyes software to speed up the training part of Deep Learning algorithms. In their paper titled .On Optimization Methods for Deep Learning., they experiment with some of the well known training algorithms and demostrate their scalability across parallel architectures (GPUs as well as multi-machine networks). The algorithms include SGDs (Stochastic Gradient Descent) L-BFGS (Limited BFGS used for solving non-linear problems), CG (Conjugate Gradient). Last Updated: 20 Sep 2011 |
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Feature Learning on Images Stanford University |
Speedup: Hours of runtime reduction |
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Feature Learning Architectures with GPU-acceleration
Authors: Andrew Ng, Stanford University Stanford researchers in Andrew Ng’s group used AccelerEyes software to speed up their work on Feature Learning Architectures. They decided to use GPUs and AccelerEyes software for this study because of the need to quickly evaluate many architectures on thousands of images. Last Updated: 9 Apr 2011 |
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Digital Holography for Imaging National University of Ireland, Maynooth |
Speedup: 17X |
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Digital Holography
Authors: Nitesh Pandey, Damien Kelly, Bryan Hennelly and
Thomas Naughton from the National University of Ireland,
Maynooth
Digital holography is a powerful imaging technique with many new
applications like true 3D display. It allows the capture of both
amplitude and phase information of the light reflected off the
surface of 3D objects. Researchers at the National University of
Ireland, Maynooth are developing techniques based on digital
holography for 3D display applications. |