Concerning the problem of insufficient accuracy of the traditional image segmentation algorithm in segmentation of blue-green alga images, a new network structure named Deep Generative Adversarial Net (DGAN) based on Deep Neural Network (DNN) and Generative Adversarial Net (GAN) was proposed. Firstly, based on Fully Convolutional neural Network (FCN), a 12-layer FCN was constructed as the Generater ( G), which was used to study the distribution of data and generate the segmentation result of blue-green alga images ( Fake). Secondly, a 5-layer Convolutional Neural Network (CNN) was constructed as the Discriminator ( D), which was used to distinguish the segmentation result generated by the generated network ( Fake) and the true segmentation result with manual annotation ( Label), G tried to generate Fake and deceive D, D tried to find out Fake and punish G. Finally, through the adversarial training of two networks, a better segmentation result was obtained because Fake generated by G could cheat D. The training and test results on image sets with 3075 blue-green alga images show that, the proposed DGAN is far ahead of the iterative threshold segmentation algorithm in precision, recall and F ₁ score, which are increased by more than 4 percentage points than other DNN algorithms such as FCNNet (SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):640-651) and Deeplab (CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs. Computer Science, 2014(4):357-361). The proposed DGAN has obtained more accurate segmentation results. In the aspect of segmentation speed, the DGAN needs 0.63 s per image, which is slightly slower than the traditional FCNNet with 0.46 s, but much faster than Deeplab with 1.31 s. The balanced segmentation accuracy and speed of DGAN can provide a feasible technical scheme for image-based semantic segmentation of blue-green algae.

In order to create novel artistic effects, a period-dynamic-image model was proposed, in which each element is a periodic function. Instead of using an array of color pixels to represent a digital image, a Fourier model was used to represent a periodic dynamic image as an array of functional pixels, and the output of each pixel was computed by a Fourier synthesis process. Then three applications with three rendering styles were put forward, including dynamic painting, dynamic distortion effects and dynamic speech balloons, to visually display the periodic dynamic images. A prototype system was constructed and a series of experiments were performed. The results demonstrate that the proposed method can effectively explore the novel artistic effects of periodic dynamic images, and it can be used as a new art media.

For the shortage of big data and incremental data processing in static algorithm, the dynamic rule extraction algorithm based on rough-decision tree was constructed to diagnose rotating machinery faults. Through the combination of rough set with decision tree, the sample selections were made by the method of incremental sampling. Through dynamic reduction, decision tree construction, rules extraction and selection, matching, four steps of loop iteration process, dynamic rule extraction was achieved, which improved the credibility of the extracted rules. Meanwhile, by applying the algorithm to the dynamic problem: rotating machinery fault diagnosis, the effectiveness of the algorithm was verified. Finally, the efficiency of the algorithm was compared with static algorithm and incremental dynamic algorithm. The result demonstrates that the proposed algorithm can obtain more implied information in the most streamlined way.