Paper
25 January 2011 Accelerating image recognition on mobile devices using GPGPU
Miguel Bordallo López, Henri Nykänen, Jari Hannuksela, Olli Silvén, Markku Vehviläinen
Author Affiliations +
Proceedings Volume 7872, Parallel Processing for Imaging Applications; 78720R (2011) https://doi.org/10.1117/12.872860
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States
Abstract
The future multi-modal user interfaces of battery-powered mobile devices are expected to require computationally costly image analysis techniques. The use of Graphic Processing Units for computing is very well suited for parallel processing and the addition of programmable stages and high precision arithmetic provide for opportunities to implement energy-efficient complete algorithms. At the moment the first mobile graphics accelerators with programmable pipelines are available, enabling the GPGPU implementation of several image processing algorithms. In this context, we consider a face tracking approach that uses efficient gray-scale invariant texture features and boosting. The solution is based on the Local Binary Pattern (LBP) features and makes use of the GPU on the pre-processing and feature extraction phase. We have implemented a series of image processing techniques in the shader language of OpenGL ES 2.0, compiled them for a mobile graphics processing unit and performed tests on a mobile application processor platform (OMAP3530). In our contribution, we describe the challenges of designing on a mobile platform, present the performance achieved and provide measurement results for the actual power consumption in comparison to using the CPU (ARM) on the same platform.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Miguel Bordallo López, Henri Nykänen, Jari Hannuksela, Olli Silvén, and Markku Vehviläinen "Accelerating image recognition on mobile devices using GPGPU", Proc. SPIE 7872, Parallel Processing for Imaging Applications, 78720R (25 January 2011); https://doi.org/10.1117/12.872860
Lens.org Logo
CITATIONS
Cited by 39 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
OpenGL

Image processing

Visualization

Mobile devices

Binary data

Detection and tracking algorithms

Cameras

Back to Top