Low-poly is a popular style in the art design field recently. In order to improve the quality of image and video low-poly stylization, an image and video low-poly rendering method based on edge features and superpixel segmentation was proposed. Firstly, the intersection points of adjacent superpixels and the uniform sampling points of the difference set between feature edges and superpixel boundaries were extracted as the vertices of the triangle mesh, and Delaunay triangulation was performed to generate the initial triangle mesh. Then, the constrained quadric error metric method was used to simplify the generated mesh in order to generate the final triangle mesh. Finally, the triangle mesh was filled with color to obtain the image with low-poly style. For video low-poly rendering, the temporally consistent superpixels were used to track the same part of the object across frames to establish associations between the video frames, reducing the jitter after video rendering. In addition, the video segmentation method was used to segment the moving objects in the video, so as to obtain sampling points with different densities between the moving objects and the background, and the local stylized effect of the video was obtained by rendering the moving objects. Experimental results show that the proposed method can generate low-poly rendering results with better visual effects.

Speech segmentation is an indispensable basic work in speech recognition and speech synthesis, and its quality has a great impact on the following system. Although manual segmentation and labeling is of high accuracy, it is quite time-consuming and laborious, and requires domain experts to deal with. As a result, automatic speech segmentation has become a research hotspot in speech processing. Firstly, aiming at current progress of automatic speech segmentation, several different classification methods of speech segmentation were explained. The alignment-based methods and boundary detection-based methods were introduced respectively, and the neural network speech segmentation methods, which can be applied in the above two frameworks, were expounded in detail. Then, some new speech segmentation technologies based on the methods such as bio-inspiration signal and game theory were introduced, and the performance evaluation metrics widely used in the speech segmentation field were given, and these evaluation metrics were compared and analyzed. Finally, the above contents were summarized and the future important research directions of speech segmentation were put forward.