Over the years, resting state functional magnetic resonance imaging (rsfMRI) has been a preferred design tool to analyze human brain functions and brain parcellations. Several different statistical methods have been proposed to study functional connectivity and generate various parcellation atlases based on corresponding connectivity maps. In this study, we employ a sliding window correlation method to generate accurate individual voxel-wise dynamic functional connectivity maps, based on which the brain can be parcellated into highly homogeneous functional parcels. Because there is no ground truth for functional brain parcellation, we accomplish parcellation via k-means clustering to compare with other available parcellations. With temporal characteristics of functional connectivity taken into consideration, high homogeneity can be observed in high resolution parcellation of human brain.