相對於傳統電腦圖學專注於以物理光學為基礎的計算模型，近十年來，有一股新的研究方向專注於利用計算模型去模擬人類創作藝術作品的方式、流程與媒介，希望藉此提供更多元的電腦繪圖影像，並透過對於人類識覺的瞭解，產生能傳遞豐富資訊的影像以及提供良好的視覺化資訊，這個類別的研究方向稱之為非相片擬真電腦繪圖(Non-Photorealistic Rendering)。本論文透過三個不同面向提供風格化電腦繪圖所需的演算架構。首先，針對風格化立體繪圖，提供了一個以點為基礎的階層式架構，並結合顏色區塊的抽象概念於階層式資料結構上，配合不同的筆觸色調貼圖，達成即時互動的繪圖效能以及各種不同程度抽象化和筆觸風格化的繪圖效果。其次，對於筆觸風格轉移的問題，提出了一套擷取畫作中重要筆觸的流程，並將其轉換成一個在空間與色調上均保持連續性的三維貼圖，藉以轉移二維影像中的筆觸風格到三維模型上，並且維持在不同視角和不同光源變化下良好的連續性。最後，在繪畫風格之外，根據心理認知科學的發現，提出了一個以流線為基礎的排列演算法，使得運動視錯覺影像的產生可以不再受限於既有的排列方式和有限的顏色組合，提供在廣告和海報設計上全新的發展空間。

Non-photorealistic rendering (NPR) brings art and science together and focus on the communication and stylization content of an image. NPR is a powerful technique for generating images in the manner of artistic styles such as painting, stippling, and hatching. This thesis investigates the pattern placement problem in NPR to provide the coherent rendering in the temporal and tonal dimension, and explores three topics of the computational stylization.

First, we proposed a novel computational framework for interactive, three-dimensional, stylized, and abstract painterly rendering. In this framework, the input models are first represented using 3D point sets and then this point-based representation is used to build a multiresolution bounding sphere hierarchy. From the leaf to root nodes, spheres of various sizes are rendered into multiple size strokes on the canvas. The proposed sphere hierarchy is developed using multi-scale region segmentation. This segmentation task assembles spheres with similar attribute regularities into a meaningful region hierarchy. These attributes include colors, positions and curvatures. This hierarchy is very useful in the following respects: 1) ensurence the screen-space stroke density, 2) controlling of different abstraction from input model, 3) maintaining of region structures such as the edges/boundaries at different scales and 4) rendering models interactively. By choosing suitable abstractions, brush stroke, and lighting parameters, we can interactively generate various painterly styles. We also propose a novel scheme that reduces the popping effect in animation sequences.

Next, a painterly style transfer technique was proposed based on the point based framework. Our technique can automatically transfer brush stroke textures and color changes to 3D models by taking a painted image as a sample. Therefore, the eneration of stylized images/animation in the style of a given artwork can be achieved. This new approach works articularly well for a rich variety of brush strokes ranging from simple 1D and 2D line-art strokes to very complicated ones with significant variations in stroke characteristics. During the rendering/animation process, the coherence of stroke textures and color changes over 3D surfaces can be well maintained. Various stylized images can be generated using the proposed framework.

Finally, We also provide a new NPR stylization to generate the motion illusion from daily images. Illusory motion in a still image is a fascinating research topic in the study of human motion perception. Physiologists and psychologists have attempted to understand this phenomenon by constructing simple, color repeated asymmetric patterns (RAP) and have found several useful rules to enhance the strength of illusory motion. Based on their knowledge, we propose a computational method to generate self-animating images. First, we present an optimized RAP placement on streamlines to generate illusory motion for a given static vector field. Next, a general coloring scheme for RAP is proposed to render streamlines. Furthermore, to enhance the strength of illusion and respect the shape of the region, a smooth vector field with opposite directional flow is automatically generated given an input image. Examples generated by our method are shown as evidence of the illusory effect and the potential applications for advertisement and design purposes.

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