Realistic facial animation can enhance the immersion characteristic of 3D games. This paper proposes a 3D morphing target method based on GPU for real-time animation of facial expressions. We employ texture mapping to obtain a realistic appearance, and design a fast morphing process to achieve a relatively high FPS by modifying the morph data structure and implementing the morphing algorithm in the shader. The algorithm can be realized and divided into seven steps including creating morph target expressions, evaluating difference vector, initializing morph data structure, loading data into VBO, linking attributes to shader and rendering facial expressions. The rendering system also gives us convenient interaction with the digital character and realizes quick expressions shift for practical application. Experiment results show that the proposed approach yields realistic real-time expressions animation.

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.