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，在许多计算机图形和多媒体应用程序中普遍存在，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非常重要，具有很强的工业影响。与栅格纹理相比，2DVP具有计算机辅助设计所需的几个特性：防止任何混叠伪影的分辨率无关性，减少内存存储的紧凑结构，以及允许新颖而强大的编辑过程的基于对象的表示。尽管最近取得了进展，但帮助用户设计此类图像的专用计算机工具是有限的。一个原因是缺乏一个直观的模型来描述物体的空间组织，这是一个明显区别于它们的栅格对应图像和其他类型的矢量图像的方面。另一部分解释来自风格化效果，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，为开发新型的直观表示和设计算法提供2DVP。我们将首先对2DVP的手动设计过程进行彻底的用户研究。我们将收集线上和线下数据，以了解此类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，这项研究将给我们提供洞察力，根据应用情况提出忠实的模型。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。当前的方法大多受栅格模型的启发，没有充分利用这种模式的基于对象的性质。我们将研究结合基于感知的风格特征的空间组织的统计描述性模型。这个描述性模型将在图像检索上下文中进行评估：网络照片库中的矢量图案包含数百万个供计算机图形设计人员使用的2DVP。标签和关键字是昂贵的、主观的，并且不适用于诸如矢量纹理之类的抽象图像。我们还将提出一种完整的生成性方法，该方法允许在2DVP内对对象进行相关样式化。这里将采取强烈的以用户为中心的互动方式。最后，这项计划将调查技术，以协助设计人员在动画制作的2DVP。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.