Learning Probability Models for Surface Appearance and Shape by Minimax Entropy Principle

摘要IIS-9877127朱，宋-春俄亥俄州立大学，115,872-12美元。用极小极大熵原理学习表面外观和形状的概率模型这是连续三年的奖项的第一年资助。本课题的研究针对视觉学习中的三个重要问题。1)。研究视觉学习的数学理论。2)。学习纹理、物体形状、杂乱和纹理流的真实概率模型，这些都是构建自然场景的重要组成部分。3)。开发用于模型推理和模型验证的快速算法。这项研究的核心是最小极大熵学习理论，该理论由PI、共同PI和Mumford于1997年提出。极小极大熵学习理论能够探索观察到的图像和模式背后的内在结构(或规律)。这一学习理论的结果是纹理、物体形状、杂乱和纹理流的视觉模式的统计模型。学习的模型可以在贝叶斯框架中用作似然函数和先验概率，这对于贝叶斯图像分析范例在图像分割、感知组织和目标识别等应用中的成功至关重要。PI和Co-PI已经取得了良好的初步结果，他们共同组成了一个有效的跨学科团队来进行研究。

Abstract IIS-9877127Zhu, Song-ChunOhio State University$115,872 - 12 mos.Learning Probability Models for Surface Appearance and Shape by Minimax Entropy PrincipleThis is the first year funding of a three year continuing award. The research in this project is aimed at three important problems in visual learning. 1). Studying a mathematical theory for visual learning. 2). Learning realistic probability models for textures, object shapes, clutter, and texton flows, which are essential constructing components of natural scenes. 3). Developing fast algorithms for model inference and model verification. Central to the research is the minimax entropy learning theory, proposed by the PI, the co-PI, and Mumford in 1997. The minimax entropy learning theory is capable of exploring the intrinsic structures (or regularities) underlying the observed imagery and patterns. The outcomes of this learning theory are statistical models of the visual patterns for textures, object shapes, clutter, and texton flows. The learned models can be used in the Bayesian framework as likelihood functions and prior probabilities, which are essential for the success of Bayesian image analysis paradigm in applications such as image segmentation, perceptual organization, and object recognition. The PI and Co-PI have achieved good preliminary results, and together, they form an effective interdisciplinary team to conduct the research.