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

摘要PI：Donald J Geman标题：ITR小：图像数据中特定对象的不变性检测和解释研究领域是自动场景分析。其主要目的是从一个小的剧目检测对象的图像数据中的外观。现有方法的两个主要缺点是光度和几何不变性不足以及计算效率低。为了面对这些困难，提出了一个统一的统计和计算框架，它是基于一个粗到细的序列的近似一个完整的贝叶斯模型。研究课题包括从粗到精的搜索、模型选择和可变形形状分析中出现的算法和数学问题。自然景物的解释对人类来说毫不费力，但却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学和技术进步，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，用于检测和解释静止图像中出现的某些对象，以减轻人类在医学成像、执法、工业检查和日常生活中的视觉搜索任务

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