In order to reconstruct 3D objects with relief-texture quickly, a modeling method based on real-time interaction for 3D objects was proposed. Firstly, the input object or image that needed to generate relief texture was converted into an initial depth map, then the depth map was converted into a gradient image. The obtained gradient image was compressed and filtered, then the continuous relief depth map was reconstructed by solving the linear equation. Secondly, using the proposed relief-texture-mapping algorithm based on mesh intersection, the optimized relief depth image was pasted on the goal object surface by real-time interaction including translation, rotation and scale under 3D scene directly. Finally, an object with relief-texture was achieved through re-triangulation of the goal model. The experimental results show that the proposed method can quickly generate concave or convex, especially multiple reliefs on the goal object, the final obtained model can be directly applied to 3D printing without any other processing.

The mouse behaviors cannot be accurately recognized by the existing computer-based automatic analysis system, and the ground truth is generally achieved from experts’ annotation on a massive number of video images. However, to some extent, subjective misjudgments are unavoidable. To solve these problems, a human computation-based mouse behavior recognition method was proposed in this paper. Because of the superiority of human visual perception, and the decentralization and cooperation of the internet, human brains were treated as processors in a distributed system. Firstly, every mouse behavior frames were distributed to on-line individuals randomly, and each behavior frame was classified by a large number of on-line individuals. Secondly, all the effective classifications from the on-line individuals were collected, analyzed and processed by computer system, realizing the final mouse behavior classification based on these frame sequences. The experimental results show that the proposed method is effective to improve the correct recognition rate of mouse behaviors with limited cost.