Descriptive and generative models for 2D vector pattern design

2D矢量图案（2DVP），也称为矢量纹理，无论是静态的还是动态的，在许多计算机图形学和多媒体应用中都无处不在，例如艺术或科学插图、制图、数据可视化、网页设计、视频游戏和动画中的视觉效果。所有这些信息技术领域在加拿大都具有高度重要性和强大的工业影响力。与栅格纹理相比，2DVP具有计算机辅助设计所需的几个特性：分辨率独立，防止任何混叠工件；紧凑的结构，减少内存存储；基于对象的表示，允许新颖而强大的编辑过程。尽管最近取得了进展，但用于帮助用户设计此类图像的专用计算机工具是有限的。其中一个原因是缺乏对象空间组织的直接模型，这是将它们与光栅图像和其他类型的矢量图像明显区分开来的一个方面。另一部分解释来自于风格化效果，如颜色和几何效果，这些效果通常与空间组织相关。我们项目的总体研究目标是继续进行基础和应用研究，以开发新的直观表示和2DVP设计算法。我们将首先对2DVP的手工设计过程进行彻底的用户研究。我们将收集在线和离线数据，以了解这些图像所涉及的时间和空间结构。使用机器学习技术进行分析，本研究将为我们提供见解，以根据应用程序提出忠实的模型。在用户研究的基础上，我们将首先提出一个新的2DVP描述模型。当前的方法大多受到光栅模型的启发，没有充分利用这种模式基于对象的特性。我们将研究空间组织与基于感知的风格特征相结合的统计描述性模型。这个描述性模型将在图像检索环境中进行评估：矢量模式的网络照片库存包含数百万张2DVP，供计算机图形设计师使用。标签和关键字是昂贵的，主观的，不适合抽象的图像，如矢量纹理。我们还将提出一种完整的生成方法，允许2DVP内对象的相关风格化。这里将采用强有力的以用户为中心的交互式方法。最后，本课程将探讨协助设计师制作2DVP动画的技术。我们计划提出一种可编程的方法来设计仿射变换效果。

2D vector patterns (2DVP), also called vector textures, whether they be static or dynamic, are ubiquitous in many computer graphics and multimedia applications such as artistic or scientific illustration, cartography, data visualization, web design, video games, and visual effects in animation. All these information technology domains are of high importance and of strong industrial impact in Canada. Compared to raster textures, 2DVP hold several desirable properties for computer assisted design: a resolution independence that prevents any aliasing artifacts, a compact structure that reduces memory storage, and an object-based representation that allows novel and powerful editing procedures. Despite recent progress, dedicated computer tools to assist the user in the design of such images are limited. One reason is the lack of a straightforward model for the objects' spatial organisation, an aspect that clearly distinguish them from their raster counterparts and other types of vector images. Another part of the explanation comes from stylization effects such as color and geometry effects which are often correlated to the spatial organisation. The overall research objective of our program is to continue fundamental and applied research for the development of novel intuitive representations and design algorithms of 2DVP. We will first conduct a thorough user study of the manual design process of 2DVP. We will collect online and offline data to understand both the temporal and spatial structures involved in such images. Using machine learning techniques to conduct our analysis, this study will give us insights to propose faithful models depending on the application. Based on the user study, we will propose first a new descriptive model of 2DVP. Current approaches are mostly inspired from raster models, not taking full advantage of the object-based nature of such patterns. We will investigate statistical descriptive models of the spatial organisation combined to perceptually-based style features. This descriptive model will be evaluated in an image retrieval context: web photo stocks of vector patterns contain millions of 2DVP for computer graphic designers. Tags and keywords are expensive, subjective, and not adapted to abstract images such as vector textures. We will also propose a complete generative approach that allows the correlated stylization of objects within 2DVP. A strong user-centered and interactive approach will be adopted here. Finally, this program will investigate techniques to assist designers in the production of animated 2DVP. We plan to propose a programmable approach to design affine transformation effects.