A Parallel Approach to Object Identification in Large-scale Images

As the computing power of processors is being drastically improved, the sizes of image data for various applications are also increasing. One of the most basic operations on image data is to identify objects within the image, and the connected component labeling (CCL) is the most frequently used strategy for this problem. However, CCL cannot be easily implemented in a parallel fashion because the connected pixels can be found basically only by graph traversal. In this paper, we propose a GPU-based efficient algorithm for object identifi- cation in large-scale images and the performance of the proposed method is compared with that of the most commonly used method implemented with OpenCV libraries. The method was implemented and tested on computing environments with commodity CPUs and GPUs. The experimental results show that the proposed method outperforms the reference method when the pixel density is below 0.7. Object identification in image data is the fundamental operation and rapid computation is highly requested as the sizes of the currently available image data rapidly increase. The experimental results show the proposed method can be a good solution to the object identification in large-scale image data.