Title: GPU-BASED OBJECT IDENTIFICATION IN LARGE-SCALE IMAGES FOR REAL-TIME RADAR SIGNAL ANALYSIS

Issue Number: Vol. 6, No. 4
Year of Publication: 2016
Page Numbers: 140-149
Authors: Isamu Shioya , Tomoya Yamamoto, Takahiro Suzuki
Journal Name: International Journal of New Computer Architectures and their Applications (IJNCAA)
- Hong Kong
DOI:  http://dx.doi.org/10.17781/P002225

Abstract:


As the computing power of processors is being drastically improved, the sizes of image data for various applications are also increasing. 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 identification 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 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 method proposed in this paper is applied to the analysis of radar signals. Since the radar signals contains complex and large data, the efficiency of the method is crucial when realtime analysis is required. The experimental results show the proposed method can be a good solution to the object identification in largescale image data such as radar signals.