ITR: Learning and recognition of objects in sensory data.

人类可以用他们的感官来识别物体和场景。学习大量物体的外观，将它们组织成类别，并在稍后快速识别它们的能力是生存的一项重要技能。在机器上复制这种能力将在大量的科学和工业应用中非常有用，如医学图像数据库的自动探索，工业工厂的诊断和质量控制，网络上图像和声音的自动分类。本研究的目的是发展一种识别理论，适用于任何类型的感官数据，并且不需要监督学习和分类。方法是概率的：对象类别由零件外观和对象形状的概率密度函数建模。检测和识别被描述为统计推理问题。使用最大似然法来实现对象类别的无监督学习。为了激励和测试这一理论，研究人员将从事三个应用：从图像数据库中自动分类和检索物体，从电影中自动分类和检索人类行为，以及与感知任务相关的神经信号。

Humans can recognize objects and scenes using their senses. The ability of learning the appearance of a great number of objects, organizing them into categories, and quickly recognizing them later is an important skill for survival. Replicating such ability in machines would be extremely useful in a great number of scientific and industrial applications such as automatic exploration of databases of medical images, diagnostics and quality control in industrial plants, automatic classification of images and sounds on the web.The aim of this study is to develop a theory of recognition that is applicable any type of sensory data and where no supervision is required for learning and categorization.The approach is probabilistic: object categories are modeled by probability density functions on part appearance and object shape. Detection and recognition are formulated as statistical inference problems. Unsupervised learning of object categories is approached using maximum likelihood. In order to motivate and test the theory the investigators will engage in three applications: automatic classification and retrieval of objects from image databases, of human actions from movies, and of neuronal signals associated with perceptual tasks.