Since the low security of sensors, poor detection area and resource limitation in Wireless Sensor Network (WSN) cause outlier data collected by nodes, an algorithm of the outlier node detection in WSN based on graph signal processing was proposed. Firstly, according to the sensor position features, a K-Nearest Neighbors ( KNN) graph signal model was established. Secondly, the statistical test quantity was built based on the smoothness ratio of the graph signal before and after low-pass filtering. Finally, the judgement of the existence of outlier nodes was realized through the statistical test quantity and decision threshold. Experiments on the public temperature dataset and PM2.5 dataset demonstrate that compared with algorithm of outlier node detection based on graph frequency domain, the proposed algorithm has the detection rate increased by 7% under the condition of single outlier node and has the detection rate of 98% under the condition of multiple outlier nodes, and keep high detection rate under the condition of outlier node with small deviation value.

To solve the problem that GrabCut algorithm is sensitive to local noise, time consuming and edge extraction is not ideal, the paper put forward a new algorithm of improving image segmentation based on GrabCut. Multi-scale watershed was used for gradient image smoothing and denoising. Watershed operation was proposed again for the new gradient image, which not only enhanced image edge points, but also reduced the computation cost of the subsequent processing. Then the entropy penalty factor was used to optimize the segmentation energy function to prevent target information loss. The experimental results show that the error rate of the proposed algorithm is reduced, Kappa coefficient is increased and the efficiency is improved compared with the traditional algorithm. In addition, the edge extraction is more complete and smooth. The improved algorithm is applicable to different types of image segmentation.