CAREER: A New Statistical Framework for Natural Images with Applications in Vision

职业：一种新的自然图像统计框架及其在视觉中的应用

• 批准号: 0953373
• 负责人: Siwei Lyu
• 金额: $ 49.96万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953373&HistoricalAwards=false
• 关键词: CAREER; New; Statistical; Framework; Natural

This project studies natural image statistics, and their applications in diverse fields such as computational neuroscience, image processing, computer vision, and graphics. The centerpiece of this project is a new image representation based on a simple nonlinear transform that is statistically justified and biologically inspired. This representation provides a new language to describe image signals, and forms the basis to build statistical models to more effectively capture statistical properties of natural image. Built upon this new image representation, this project explores new paradigms to model and interpret visual neural responses and high-level perceptual properties, and provides new tools for image restoration, analysis and synthesis. On the other hand, by applying natural image statistics to the forensic analysis of digital images, this project facilitates forensic practitioners in criminal investigations, and contributes to national security and public safety. Moreover, this project contributes to education by making the learning of Computer Science fun and useful for undergraduate students, promoting the participation of women and undergraduate students in research, and improving the early learning of mathematics and sciences for local high school students.

该项目研究了自然图像统计数据及其在各种领域的应用，例如计算神经科学，图像处理，计算机视觉和图形。该项目的核心是基于一个简单的非线性变换的新图像表示，该变换具有统计学上的合理性和具有生物学启发的。该表示形式提供了一种描述图像信号的新语言，并构成了建立统计模型以更有效地捕获自然图像的统计属性的基础。该项目建立在这种新图像表示的基础上，探索了新的范式，以建模和解释视觉神经反应和高级感知属性，并为图像恢复，分析和综合提供了新的工具。另一方面，通过将自然图像统计数据应用于数字图像的法医分析，该项目促进了法医从业者进行刑事调查，并为国家安全和公共安全做出了贡献。此外，该项目通过使计算机科学的学习变得有趣且对本科生有用，促进妇女和本科生研究的参与以及改善当地高中生数学和科学的早期学习，从而有助于教育。