Career: Non-parametric Multi-scale Statistical Models for Natural Signals and Images

职业：自然信号和图像的非参数多尺度统计模型

• 批准号: 9875866
• 负责人: Paul Viola
• 金额: --
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-07-01 至 2003-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9875866&HistoricalAwards=false
• 关键词: Career; Non; parametric; Multi; scale

This CAREER award supports the study of image processing and vision. Vision is perhaps the most complex and powerful capability that we possess. Intriguingly vision develops gradually as we grow from infancy to adulthood. This research focuses on two related questions: What is the fundamental structure of visual images that allow this type of learning? What algorithms can be used to learn this structure? Answers to these questions have bearing on the recognition of complex visual patterns, the generation of computer graphics images, and the study of human perception. The technical emphasis of this effort is based on the observation that the distribution of natural images is far from uniform. On the contrary, real images have complex and important statistical structure that can be exploited for image processing, recognition and analysis. Based on these insights, the investigator has developed a non-parametric multi-scale statistical model for images that can be used for recognition, image processing and in a ``generative mode'' to synthesize high quality textures. In this effort the theoretical underpinnings of these algorithms as well as the application to other processing problems are studied. In addition, the award is supporting the development of a class and textbook on the statistical structure of natural images. While emphasizing fundamentals, the course draws together advances in computer vision, image processing, digital television, image compression, the psychology of vision, and the neural basis of vision.

这一职业奖支持图像处理和视觉的学习。视觉可能是我们拥有的最复杂和最强大的能力。耐人寻味的是，随着我们从婴儿期到成年期，视力逐渐发展。这项研究集中在两个相关的问题上：什么是视觉图像的基本结构，使这种类型的学习？什么算法可以用来学习这种结构？这些问题的答案关系到复杂视觉模式的识别、计算机图形图像的生成和人类感知的研究。这项工作的技术重点是基于观察到的自然图像的分布远非均匀。相反，真实图像具有复杂而重要的统计结构，可以用于图像处理、识别和分析。基于这些见解，研究人员开发了一种用于图像的非参数多尺度统计模型，该模型可用于识别、图像处理和以生成模式合成高质量的纹理。在这一努力中，研究了这些算法的理论基础以及在其他处理问题中的应用。此外，该奖项还支持开发一门关于自然图像统计结构的课程和教科书。在强调基础的同时，本课程汇集了计算机视觉、图像处理、数字电视、图像压缩、视觉心理学和视觉的神经基础等方面的进展。