The objects with inhomogeneous intensity or multi-gray intensity by using active contour, a supervised active contour algorithm named KSOAC was proposed based on Kernel Self-Organizing Map (KSOM). Firstly, prior examples extracted from foreground and background were input into KSOM for training respectively, and two topographic maps of input patterns were obtained to characterize their distribution and get the synaptic weight vector. Secondly, the average training error of unit pixel of two maps were computed and added to energy function to modify the contour evolution; meanwhile, the controlling parameter of energy item was obtained by the area ratio of foreground and background. Finally, supervised active contour energy function and iterative equation integrated with synaptic weight vectors were deduced, and simulation experiments were conducted on multiple images using Matlab 7.11.0. Experimental results and simulation data show that the map obtained by KSOM is closer to prior example distribution in comparison with Self-Organizing Map (SOM) active contour (SOAC), and the fitting error is smaller. The Precision, Recall and F-measure metrics of KSOAC are higher than 0.9, and the segmentation results are closer to the target; while the time consumption of KSOAC is similar to that of SOAC. Theoretical analysis and simulation results show that KSOAC can improve segmentation effectiveness and reduce target leak in segmenting images with inhomogeneous intensity and objects characterized by many different intensities, especially in segmenting unknown probability distribution images.

In order to meet the needs of modern society for weather forecast and early warning service, a real-time weather forecast and abnormal weather early warning system was researched and implemented in the Android system. Based on the decision tree algorithm of C4.5 algorithm, the warning classification problem was resolved. By means of extracting the attributes with maximum gain rate as the features of training sample, a decision tree was built. A model of decision tree was got by the pruning weather warning evaluation and analysis and application were made on this model. The experimental results show that this method has advantages in the assessment of classification accuracy, with correct classification rate up to 85.8%.