Ant_ViBe: Improved ViBe Algorithm Based on Ant Colony Clustering under Dynamic Background

Foreground target detection algorithm (FTDA) is a fundamental preprocessing step in computer vision and video processing. A universal background subtraction algorithm for video sequences (ViBe) is a fast, simple, efficient and with optimal sample attenuation FTDA based on background modeling. However, the traditional ViBe has three limitations: (1) the noise problem under dynamic background; (2) the ghost problem; and (3) the target adhesion problem. In order to solve the three problems above, ant colony clustering is introduced and Ant_ViBe is proposed in this paper to improve the background modeling mechanism of the traditional ViBe, from the aspects of initial sample modeling, pheromone and ant colony update mechanism, and foreground segmentation criterion. Experimental results show that the Ant_ViBe greatly improved the noise resistance under dynamic background, eased the ghost and targets adhesion problem, and surpassed the typical algorithms and their fusion algorithms in most evaluation indexes.

Ant Colony Clustering for ROI Identification in Functional Magnetic Resonance Imaging

Brain network analysis using functional magnetic resonance imaging (fMRI) is a widely used technique. The first step of brain network analysis in fMRI is to detect regions of interest (ROIs). The signals from these ROIs are then used to evaluate neural networks and quantify neuronal dynamics. The two main methods to identify ROIs are based on brain atlas registration and clustering. This work proposes a bioinspired method that combines both paradigms. The method, dubbed HAnt, consists of an anatomical clustering of the signal followed by an ant clustering step. The method is evaluated empirically in both in silico and in vivo experiments. The results show a significantly better performance of the proposed approach compared to other brain parcellations obtained using purely clustering-based strategies or atlas-based parcellations.