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.

该项目研究自然图像统计及其在不同领域的应用，如计算神经科学、图像处理、计算机视觉和图形学。这个项目的核心是一种新的图像表示，它基于一种简单的非线性变换，从统计学上讲是合理的，并受到生物学的启发。这种表示提供了一种新的语言来描述图像信号，并为建立统计模型以更有效地捕捉自然图像的统计特性奠定了基础。在这种新的图像表示的基础上，这个项目探索了新的范例来建模和解释视觉神经反应和高级感知属性，并为图像恢复、分析和合成提供了新的工具。另一方面，通过将自然图像统计应用于数字图像的法医分析，该项目为刑事调查中的法医从业人员提供便利，并有助于国家安全和公共安全。此外，该项目还通过使本科生学习计算机科学变得有趣和有用，促进女性和本科生参与研究，以及改善当地高中生早期学习数学和科学，为教育做出贡献。