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

摘要：Donald J . GemanTitle: ITR SMALL：图像数据中特定目标的不变性检测和解释，研究的领域是自动场景分析。主要目标是从一个小的库中检测图像数据中物体的外观。现有方法的两个主要缺点是光度和几何不变性不足，以及计算效率低下。为了克服这些困难，提出了一个统一的统计和计算框架，该框架基于对全贝叶斯模型的从粗到精的近似序列。研究课题包括从粗到精搜索、模型选择和可变形形状分析中出现的算法和数学问题。对自然场景的解读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。该项目的总体目标是设计计算机算法来检测和解释静态图像中出现的某些物体，以减轻人类在医学成像，执法，工业检查和日常生活中令人厌倦的视觉搜索任务

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