Random needle embroidery(RNE) is a graceful art enrolled in the world intangible cultural heritage. In this paper, we study the stitch layout problem and propose a controllable stitch layout strategy for RNE. Using our method, a user can easily change the layout styles by adjusting several high-level layout parameters. This approach has three main features: firstly, a stitch layout rule containing low-level stitch attributes and high-level layout parameters is designed; secondly, a stitch neighborhood graph is built for each region to model the spatial relationship among stitches; thirdly, different stitch attributes(orientations, lengths, and colors) are controlled using different reaction-diffusion processes based on a stitch neighborhood graph. Moreover, our method supports the user in changing the stitch orientation layout by drawing guide curves interactively. The experimental results show its capability for reflecting various stitch layout styles and flexibility for user interaction.
This paper presents a synthesis method for 3D models using Petri net. Feature structure units from the example model are extracted, along with their constraints, through structure analysis, to create a new model using an inference method based on Petri net. Our method has two main advantages: first, 3D model pieces are delineated as the feature structure units and Petri net is used to record their shape features and their constraints in order to outline the model, including extending and deforming operations; second, a construction space generating algorithm is presented to convert the curve drawn by the user into local shape controlling parameters, and the free form deformation (FFD) algorithm is used in the inference process to deform the feature structure units. Experimental results showed that the proposed method can create large-scale complex scenes or models and allow users to effectively control the model result.
We propose a 3D model feature line extraction method using templates for guidance. The 3D model is first projected into a depth map, and a set of candidate feature points are extracted. Then, a conditional random fields (CRF) model is established to match the sketch points and the candidate feature points. Using sketch strokes, the candidate feature points can then be connected to obtain the feature lines, and using a CRF-matching model, the 2D image shape similarity features and 3D model geometric features can be effectively integrated. Finally, a relational metric based on shape and topological similarity is proposed to evaluate the matching results, and an iterative matching process is applied to obtain the globally optimized model feature lines. Experimental results showed that the proposed method can extract sound 3D model feature lines which correspond to the initial sketch template.
