Collaborative Research: Randomized Invariant Features for Recognition

协作研究：用于识别的随机不变特征

• 批准号: 0222516
• 负责人: Carlo Tomasi
• 金额: $ 30.6万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-15 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0222516&HistoricalAwards=false
• 关键词: Collaborative; Research; Randomized; Invariant; Features

The goal of this collaborative proposal between the University of California at Santa Cruz and Duke University is the definition of features for recognition and classification of objects in imagery. Feature vectors are enriched and made invariant at the same time by an efficient, probabilistic coding of the feature-space manifolds that are obtained when features undergo all possible transformations in a predefined class. This coding is designed to circumvent the curse of dimensionality by reducing the representation requirements to the two properties that are essential for recognition and classification, that is, uniqueness and continuity. Uniqueness requires different manifolds to be coded differently, while continuity requires similar codings for similar manifolds.Theoretical and empirical investigations will study optimal feature design, the performance of these features with various classification and recognition methods, and the trade-off of computational efficiency and performance. Misclassification rates for families of features and transformations will also be measured, both by theoretical bounds and empirical tests. The behavior of the proposed representations will be examined in the presence of clutter and data corruptions such as occlusions. These concepts will be tested through their application to the automatic diagnosis of colon cancer from computerized tomography scans, and to the interpretation of American sign language from video sequences.

圣克鲁斯的加州大学和杜克大学之间的合作建议的目标是识别和分类的图像中的对象的功能的定义。通过对特征空间流形进行有效的概率编码，特征向量被丰富，同时变得不变，当特征在预定义的类中经历所有可能的变换时获得特征空间流形。这种编码的目的是规避灾难的维数减少表示要求的两个属性是必不可少的识别和分类，即，唯一性和连续性。统一性要求不同的流形以不同的方式编码，而连续性要求相似的流形相似的codings.Theoretical和实证调查将研究最佳的特征设计，这些功能与各种分类和识别方法的性能，以及计算效率和性能的权衡。还将通过理论界限和经验测试来测量特征和变换家族的误分类率。在存在杂波和数据损坏（如遮挡）的情况下，将检查所提出的表示的行为。这些概念将通过应用于计算机断层扫描结肠癌的自动诊断和从视频序列中解读美国手语来进行测试。