Abstract
Edge detection is a very important application in the conventional image processing area. In real life we may need to face this critical technique, for example, understanding how to use wireless sensors to detect the correct extended range of greasiness on the sea. In the distributed wireless sensor network environment, we utilize the technique of Cellular Neural Network to reach the goal of edge detection. Using this technique, we can make each sensor only receive the information of adjacent sensors to identify itself as edge sensor or not.First of all, we propose an algorithm, that is, according to the detecting edge results of sensors under different communication abilities, we determine the most efficient communication range of sensors and make the simulations in different communication qualities.Moreover, we discuss the other two edge detection methods which are also used in the distributed wireless sensor network: one is the statistical-based approach and the other is the filter-based approach applied in the image processing. Comparing the edge detecting simulation results of this two methods and the method of Cellular Neural Network, we find that in the low SNR environment, the results of Cellular Neural Network we gained might be better than the ones of the other two.Third, we discuss in advance the mathematical model combined by the Cellular Neural Network and wireless sensor network, and then, utilize the numerical recipes to obtain the edge detection's approximate analytical results, the probability of detection, and the probability of false alarm.Finally, we compare the analytical results' probability of detection and false alarm with the ones of simulation results which belong to the edge detection found by the technique of the combination of wireless sensor network and Cellular Neural Network.