An intensity-density associative clustering method for big millimeter-wave image data

Abstract

The three-dimensional (3D) Millimeter-Wave (MMW) image is a big data with redundant information that not only blurs the image but also increases the computational load on the denoising procedure. To address this problem, we propose an intensity-density associative clustering method which mainly consists of two phases. Specifically, an intensity–based clustering algorithm, e.g., K-Means, is firstly applied on the amplitude of the image data and initially achieves denoising and data compression. Then, a density-based spatial clustering algorithm, e.g., DBSCAN, is used to further extract object information. Due to the label transferring from the retained amplitude, only valid image data can forward spatial information to DBSCAN, and as a result, the computational load on DBSCAN decreases. Also, a multi-threaded parallel computing framework is developed to exploit the distributed multicore processing for its implementation. Therefore, the proposed method can be well-adapted for 3D MMW image data in a computational efficient way. Simulations and experimental results confirm the effectiveness of our method that has good efficiency on big MMW image data with respect to the noise suppression and object information extraction.