This paper proposes a novel image processing method based on a percolation model. The percolation model is used to represent the natural phenomenon of the permeation of liquid. The percolation takes into account the connectivity among the neighborhoods. In the proposed method, a cluster formation by the percolation process is performed first. Then, feature extraction from the cluster is carried out. Therefore, this method is a type of scalable window processing for realizing a robust and flexible feature extraction. The effectiveness of proposed method was verified by experiments on crack detection, noise reduction, and edge detection.

Flooding is usually utilized to find a multi-hop route toward a node which is not within transmission range. However, existing flooding schemes deteriorate the performance of network because of periodic message exchanges, frequent occurrence of collisions, and redundant packet transmission. To resolve the problem, a lightweight and novel flooding scheme is proposed in this paper. The scheme employs ongoing packets for constructing a cluster architecture as the existing on-demand clustering scheme. Unlike to the existing schemes, it makes use of unicast packet transmission to reduce the number of collisions and to find the flooding candidates easily. As a result, the proposed scheme yields fewer flooding nodes than other schemes. Simulation results prove that it causes fewer packet transmissions and fewer collisions than those of two other schemes.