Dynamic Visual Scene Gist Recognition using a Probabilistic Inference Framework

我们怎么能看到森林而不刻意寻找树木呢？在一条忙碌的高速公路上，如果不识别车辆或测量它们的速度，我们怎么能知道交通是否顺畅呢？这些问题激发了这个研究项目。人类被赋予了从一个单一的眼神中抓住“要点”或复杂视觉场景的整体意义的能力，而不关注细节。本计画的目的是发展新的计算视觉模型，结合联合收割机生物视觉处理与机率推论来进行要点辨识。开发的模型将能够通过快速分析复杂的动态场景并对其语义类别进行分类来模仿人类视觉，而无需识别单个对象。

How can we see the forest without intentionally looking for the trees? How can we tell traffic is flowing smoothly on a busy highway without identifying vehicles or measuring their speed? These are the questions that inspire this research project. Humans are endowed with the ability to grasp the ‘gist’ or overall meaning of a complex visual scene from a single glance and without attention to details. The aim of this project is to develop new computational vision models that combine biological visual processing with probabilistic inference for gist recognition. The developed models will be able to mimic human vision by analysing a complex dynamic scene rapidly and classifying its semantic categories, without identifying individual objects.