19 February 2013 Large object extraction for binary images on the GPU
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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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gregory Huchet, Gregory Huchet, } "Large object extraction for binary images on the GPU", Proc. SPIE 8656, Real-Time Image and Video Processing 2013, 865608 (19 February 2013); doi: 10.1117/12.2002557; https://doi.org/10.1117/12.2002557

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