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

这个职业奖支持图像处理和视觉的研究。视觉可能是我们拥有的最复杂和最强大的能力。有趣的是，从婴儿到成年，我们的视力是逐渐发展的。本研究集中在两个相关的问题：什么是视觉图像的基本结构，允许这种类型的学习？什么样的算法可以用来学习这个结构？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以及人类感知的研究。这项工作的技术重点是基于观察到自然图像的分布远非均匀。相反，真实的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非参数多尺度图像统计模型，可用于识别，图像处理和“生成模式”，以合成高质量的纹理。在这项工作中，这些算法的理论基础以及其他处理问题的应用进行了研究。此外，该奖项还支持开发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、视觉心理学和视觉神经基础方面的进展。

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.