ITR: Invariant Detection and Interpretation of Specific Objects in Image Data

ITR：图像数据中特定对象的不变检测和解释

• 批准号: 0219016
• 负责人: Donald Geman
• 金额: $ 45万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-10-01 至 2005-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0219016&HistoricalAwards=false
• 关键词: ITR; Invariant; Detection; Interpretation; Specific

AbstractPI: Donald J GemanTitle: ITR SMALL: Invariant Detection and Interpretation of Specific Objects in Image DataThe area of investigation is automated scene analysis. The main objective is to detect the appearance in image data of objects from a small repertoire. Two key liabilities in current methods are insufficient invariance, both photometric and geometric, and inefficient computation. To confront these difficulties, a unified statistical and computational framework is proposed which is based on a coarse-to-fine sequence of approximations to a full Bayesian model. Research topics include both algorithmic and mathematical issues arising in coarse-to-fine search, model selection and deformable shape analysis. The interpretation of natural scenes is effortless for human beings but is the main challenge of artificial vision. This "semantic gap" has largely resisted any satisfying solution and impedes scientific and technological advances in many areas, including automated medical diagnosis, industrial automation, and effective security and surveillance. The general objective of this project is to design computer algorithms for detecting and interpreting certain objects appearing in still pictures in order to relieve humans of wearisome visual search tasks in medical imaging, law enforcement, industrial inspection and everyday life

摘要PI：Donald J GemanTitle：ITR SMALL：图像数据中特定对象的不变检测和解释研究领域是自动场景分析。主要目的是检测来自一个小曲目的对象在图像数据中的外观。当前方法的两个主要缺陷是光度学和几何学的不变性不足，以及计算效率低下。为了应对这些困难，提出了一个统一的统计和计算框架，该框架基于对完整贝叶斯模型的从粗到精的近似序列。研究主题包括从粗到精的搜索、模型选择和变形形状分析中出现的算法和数学问题。对自然场景的解释对人类来说是毫不费力的，但这是人工视觉的主要挑战。这种“语义鸿沟”在很大程度上阻碍了任何令人满意的解决方案，并阻碍了许多领域的科技进步，包括自动化医疗诊断、工业自动化以及有效的安全和监控。这个项目的总体目标是设计计算机算法来检测和解释出现在静止图像中的特定对象，以便将人类从医学成像、执法、工业检查和日常生活中令人厌烦的视觉搜索任务中解放出来