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.