Focusing on the issue that edges extracted by state-of-the-art exist some deficiencies including non-continuity, incompleteness, incline, jitter and notches etc., an edge extraction method based on graph theory was proposed, which considered the image as an undirected graph by regarding each pixel as a node and connecting two adjacent nodes in horizontal or vertical direction to constitute a side. The proposed method included three phases:in pixels similarity calculation phase, the weights were given to sides in undirected graph, which represented pixels similarity; in threshold determination phase, the mean of all the weights (the similarity of the whole image) was determined as a threshold; in edge determination phase, when weights on horizontal or vertical sides were smaller than the threshold, the left nodes of horizontal side and the upper nodes of vertical side were retained to constitute edges of the image. The experimental results show that the proposed edge extraction method based on graph theory is suitable for the images with obvious target and background, and can overcome deficiencies including non-continuity, incompleteness, incline, jitter and notches etc., and has anti-noise ability to Speckle noise and Gaussian noise.

Opportunistic network routing algorithm based on geographic location information in DIrection based Geographic routing scheme (DIG) has the problems of large delay and low success rate, which is due to that DIG algorithm makes the waiting time of the data in the cache too long and cannot guarantee the data-carrying node move to the destination node. To solve these problems, Geographic Routing algorithm based on Directional Data Transmission (GRDDT) was proposed. The algorithm used a new data forwarding mechanism and a more effective use of the neighbor list information, effectively avoiding the appearance of the above circumstances, so as to reduce data packet transmission delay and to improve the success rate. OPNET simulation results show that, the performance of transmission delay and success rate of GRDDDT algorithm are improved compared with DIG.