Hierarchical Methods in Graphics and Image Processing

图形和图像处理中的分层方法

• 批准号: 105708-2007
• 负责人: Yang, XueDong
• 金额: $ 1.38万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=362305
• 关键词: Hierarchical; Methods; Graphics; Image; Processing

Hierarchical multi-resolution models are powerful tools for robust and effective computation in computer graphics and image processing, in an adaptive and progressive style. I will focus on the following specific topics:- To apply the hybrid hierarchical modeling method developed for hair rendering to a dynamic water simulation problem. Examples include a waterfall's dynamic shape as influenced by the features of the cliff face and wind factors, or large waves splashing onto rocks. In this work, our goal is to develop a unified framework for water state transition between liquid, droplets, and mist.- To extend the mathematical formulation of an emerging region portioning method, achieved through the mean shift algorithm, by introducing a variation factor, and to explore this method in scale-space resulting in a hierarchical representation and two novel applications in the areas of texture analysis and no-reference image metrics.- To investigate an adaptive partitioning algorithm for edge detection in images that groups edge points hierarchically according to their natural spatial clustering. An extension of this work will result in a novel sparse matrix representation for the high-dimensional accumulators used by the Hough Transform in handling curves.My long-term goals are:- To generalize my hybrid hierarchical modeling method into a framework that can handle a variety of complex natural phenomena for computer graphics.- To develop a general framework for extracting and organizing object boundaries from complex images as piecewise lines and arc segments for the general purpose of object recognition.- To develop a content-based, no-reference image and video-quality metric.

分层多分辨率模型是计算机图形学和图像处理中鲁棒和有效计算的有力工具，具有自适应和渐进的特点。我将重点关注以下具体主题：-将为头发渲染开发的混合分层建模方法应用于动态水模拟问题。例子包括受悬崖表面特征和风因素影响的瀑布的动态形状，或溅到岩石上的大浪。在这项工作中，我们的目标是为液体，水滴和雾之间的水状态转换开发一个统一的框架。-通过引入变异因子，扩展一种新兴的区域分割方法的数学公式，该方法是通过mean shift算法实现的，并在尺度空间中探索这种方法，从而产生层次表示和纹理分析和无参考图像度量领域的两个新应用。-研究一种用于图像边缘检测的自适应分区算法，该算法根据边缘点的自然空间聚类分层分组。对这一工作的扩展将产生一种新的稀疏矩阵表示，用于霍夫变换在处理曲线时使用的高维累加器。我的长期目标是：-将我的混合分层建模方法推广到一个可以处理计算机图形学各种复杂自然现象的框架中。-开发从复杂图像中提取和组织物体边界的一般框架，作为分段线和弧段，用于物体识别的一般目的。-制订一套以内容为本、无参考的图像及视频质素指标。