CAREER: Object Recognition with Hierarchical Models

职业：使用分层模型进行物体识别

• 批准号: 1215812
• 负责人: Pedro Felzenszwalb
• 金额: $ 16.65万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1215812&HistoricalAwards=false
• 关键词: CAREER; Object; Recognition; Hierarchical; Models

AbstractTitle: CAREER: Object Recognition with Hierarchical ModelsPI: Pedro FelzenszwalbInstitution: University of ChicagoCAREER: Object Recognition with Hierarchical ModelsObject recognition is one of the most important problems in computer vision. While researchers have worked on this problem for over thirty years, vision systems are still unable to recognize many common objects in cluttered images. The PI proposes to address this problem by developing new hierarchical models and efficient search algorithms for recognition.Hierarchical models represent objects using parts which are themselves defined in terms of subparts. Moreover, the subparts may be recursively defined in terms of smaller components. This hierarchical organization can efficiently encode important relationships among the components that make up an object. Another important property of hierarchical models is that components can be shared among different object models. This is useful for being able to quickly recognize which of many possible objects are present in an image. It is also important for learning models from small datasets. Finally, in the most general types of models the structure of an object may be specified by a grammar instead of being fixed in advance. The number of parts that make up an object may be variable and there may be choice among different parts that can go in a particular place. All of these aspects make hierarchical models incredibly expressive.Algorithms for object recognition typically search over large spaces encoding the pose of an object, or over correspondences between model features and features extracted from an image. The PI will develop efficient optimization algorithms for solving these problems. This will be accomplished by exploiting the structure of the search spaces defined by general classes of hierarchical models.Broader significance and importance: Object recognition has many important practical applications, including in robotics, human-computer interaction, image retrieval, security systems and medical image analysis. Research in object recognition can also play an important role in our understanding of human perception and intelligence. The proposed research will draw upon ideas from diverse areas such as computer vision, theoretical computer science, natural language understanding and mathematics.URL: http://people.cs.uchicago.edu/~pff/hierarchical

AbstractTitle：职业：具有分层模型的对象识别：Pedro Felzenszwalbinstitution：Chicagocareer大学：具有分层模型的对象识别对象识别对象识别是计算机视觉中最重要的问题之一。 尽管研究人员已经解决了这个问题已有30多年了，但视觉系统仍无法识别混乱的图像中的许多常见对象。 PI建议通过开发新的层次模型和有效的搜索算法来解决此问题。层次结构模型表示对象使用本身定义的零件，这些零件本身是根据子部分定义的。 此外，可以根据较小的组件递归定义该子部分。 这个层次组织可以有效地编码组成对象的组件之间的重要关系。 分层模型的另一个重要属性是，可以在不同对象模型之间共享组件。 这对于能够快速识别图像中存在许多可能的对象中的哪些很有用。 这对于从小型数据集学习模型也很重要。 最后，在最通用的模型中，对象的结构可以由语法指定，而不是预先固定。 组成对象的零件数量可能是可变的，并且可以在特定位置进行的不同零件之间有选择。 所有这些方面使层次模型令人难以置信的表达性。对象识别的算法通常在编码对象姿势的大空间上搜索，或者在模型特征和从图像中提取的特征之间的对应关系。 PI将开发有效的优化算法来解决这些问题。 这将通过利用由层次模型的一般类别定义的搜索空间的结构来实现。BOADER的意义和重要性：对象识别具有许多重要的实际应用，包括在机器人技术，人体计算机交互，图像检索，安全系统和医疗图像分析中。 对象识别的研究也可以在我们对人类感知和智力的理解中发挥重要作用。 拟议的研究将借鉴来自计算机视觉，理论计算机科学，自然语言理解和数学等不同领域的想法：http：//people.cs.uchicago.edu/~pff/hierarchical