Hierarchal Perceptual Organization with the Center-Surround Algorithm

机器人和计算机视觉计划摘要建议#：0329156标题：层次感知组织与中心-周围的mPI：Siskind，杰弗里普渡大学这项研究的目的是开发一个通用的分析和算法框架，多维上下文无关的语法（PCFG），可用于模拟图像和其他多维数据集的层次结构。该框架将PCFG的概念从1D单词串扩展到2D图像数据，并且类似地扩展内部-外部算法以支持使用这些扩展的PCFG对2D图像数据进行训练、分类和解析。扩展的框架被称为空间随机树（SRT）和扩展的算法的中心环绕算法。该框架是既健全和有效的，因为一个新的概念，选区，限制了允许的方式来划分一个父段到子段在解析。这项研究具有很大的智力价值，因为它形成了一个基本的基础：从低层次的图像属性，如边缘显着性和区域形状，颜色，纹理和相对位置推断语义有意义的层次结构。从未标记的训练数据中以无监督的方式发现自然图像集合共享的共同层次结构。基于全局层次结构而不是局部低层特征区分不同的自然图像场景类。这项研究将通过解决各种技术领域的广泛应用中共享的问题来实现广泛的影响。我们尤其会：扩展SRT框架，使其可以用于精确地建模层次结构中成分之间的几何关系。这将增强SRT在图像高级建模中的价值。开发用于组合SRT模型以及将SRT模型与其他可用数据模型合并的工具。这将为提高方法的速度和准确性提供一个总体框架。探索使用SRT作为对高维数据进行分类的距离度量。这为潜在的应用程序（如Web集群）打开了技术的大门。 开发一种统一的方法来组合视频的空间和时间解析。这些新方法可以同时支持视频索引和监控任务。 开发解析和识别图像的新方法。这在打印信息分析、监控视频监控或医学图像分析等应用中非常有用。研究团队包括研究人员，他们为我们的项目带来了各种不同领域的专业知识，包括计算语言学、机器视觉、逆问题、随机过程和自然语言处理。这种广泛的背景使团队能够共同利用来自多个领域的想法，并以一种没有这种合作就不可能的方式对这些领域产生广泛的影响。

Robotics and Computer Vision ProgramABSTRACTProposal #: 0329156Title: Hierarchal Perceptual Organization with the Center-Surround AlgorithmPI: Siskind, JefferyPurdue UniversityThe objective of this research is to develop a general analytic and algorithmic framework for multidimensional context-free grammars (PCFG) that can be used to model the hierarchical structure of images and other multidimensional data sets. This framework extends the notions of PCFGs from 1D word strings to 2D image data and similarly extends the inside-outside algorithm to support training, classification, and parsing on 2D image data with these extended PCFGs. The extended framework is called spatial random trees (SRTs) and the extended algorithm the center-surround algorithm. The framework is both sound and efficient because of a novel notion of constituency that constrains the allowable ways to partition a parent segment into child subsegments during parsing. This research has great intellectual merit because it forms a fundamental basis for: Inferring semantically meaningful hierarchal structure from low-level image properties such as edge saliency and region shape, color, texture, and relative position. Discovering the common hierarchal structure shared by a collection of natural images in an unsupervised fashion from unlabeled training data. Distinguishing between different natural image-scene classes on the basis of global hierarchal structure, rather than local low-level features. This research will achieve broad impact by addressing a problem that is shared among a wide array of applications in a variety of technical fields. In particular, we will: Extend the SRT framework so that it can be used to accurately model the geometric relations between constituents in hierarchal structures. This will enhance the value of SRTs in high-level modeling of images. Develop tools for combining SRT models and merging SRT models with other available data models. This will provide a general framework for both improved speed and accuracy of the methods. Explore the use of SRTs as a distance metric for classifying high-dimensional data. This opens the techniques to potential applications such as Web clustering. Develop a unified approach to combined spatial and temporal parsing of video. These new methods can support both video indexing and surveillance tasks. Develop novel approaches for the parsing and recognition of images. This can be useful in applications such as the analysis of printed information, the monitoring of surveillance video, or the analysis of medical imagery.The research team includes researchers who bring to our project, expertise from a wide variety of different fields including computational linguistics, machine vision, inverse problems, stochastic processes, and natural language processing. This broad background allows the team to collectively leverage ideas from multiple fields and have broad impact on these fields in a way that would not be possible without such collaboration.