ITR: Learning and recognition of objects in sensory data.

人类可以用感官识别物体和场景。学习大量物体的外观，将它们组织成类别，并在以后迅速识别它们的能力是一项重要的生存技能。在机器中复制这种能力将在大量的科学和工业应用中非常有用，例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，自动分类的图像和声音在网络上。这项研究的目的是开发一种理论的认识，适用于任何类型的感官数据，其中没有监督是需要学习和该方法是概率性的：对象类别由部件外观和对象形状上的概率密度函数建模。检测和识别被公式化为统计推断问题。对象类别的无监督学习使用最大似然法。为了激发和测试的理论，研究人员将从事三个应用程序：自动分类和检索的对象从图像数据库，从电影中的人类行为，和神经元信号与知觉任务。

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.