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Proceedings Paper

Large object extraction for binary images on the GPU
Author(s): Gregory Huchet
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Paper Abstract

Object filtering by size is a basic task in computer vision. A common way to extract large objects in a binary image is to run the connected-component labeling (CCL) algorithm and to compute the area of each component. Selecting the components with large areas is then straightforward. Several CCL algorithms for the GPU have already been implemented but few of them compute the component area. This extra step can be critical for real-time applications such as real-time video segmentation. The aim of this paper is to present a new approach for the extraction of visually large objects in a binary image that works in real-time. It is implemented using CUDA (Compute Unified Device Architecture), a parallel computing architecture developed by NVIDIA.

Paper Details

Date Published: 19 February 2013
PDF: 6 pages
Proc. SPIE 8656, Real-Time Image and Video Processing 2013, 865608 (19 February 2013); doi: 10.1117/12.2002557
Show Author Affiliations
Gregory Huchet, Samsung Information Systems America, Inc. (United States)

Published in SPIE Proceedings Vol. 8656:
Real-Time Image and Video Processing 2013
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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