CAREER: Categorization and Identification of Visual Scenes

职业生涯：视觉场景的分类和识别视觉识别的一个显著方面是，人类能够在1/20秒内识别出复杂视觉场景的含义(或“主旨”)，而与场景中物体的数量无关。在观看电视广告中的快速序列和现代电影预告片中的快速剪辑时，可以体验到这种快速理解现象。这一非凡的壮举是如何实现的？在过去的十年中，研究已经在理解单个物体识别的机制方面取得了实质性的进展，但在理解场景识别方面进展较少。例如，在需要识别场景主旨的任务中，计算机系统远远达不到人类的表现。Aude Oliva博士采用了一种新的方法来解决这个具有挑战性的问题，他研究了本质上是全局的分析机制，专注于描述场景的空间布局(例如，其体积、视角、杂乱程度)而不仅仅是其组成部分(例如，场景中的对象)的统计稳健特征。在美国国家科学基金会的支持下，奥德·奥利瓦博士将进行一项为期五年的职业奖项研究，以考察全球图像分析方法如何解释人类识别场景和物体的非凡能力。此外，她还将使用这种方法来定义机器视觉系统的操作策略。这项研究计划将结合许多方法，包括行为实验(心理物理学、眼球跟踪)、认知神经科学方法(事件相关电位)和计算建模。这项工作的应用可能包括帮助驾驶员的场景和空间识别系统、可以提供大型图像数据库内容的语义描述的自动系统，以及帮助视障人士在视觉空间中导航的计算机辅助系统。奥利瓦博士提出的教育任务包括对研究生和本科生进行场景理解的认知和计算方法的实验室培训，以及一门关于计算视觉认知的新课程，以及冬季辅导和年度研讨会，这两门课程都是关于场景理解的。

CAREER: Categorization and Identification of Visual ScenesPI: Aude OlivaOne remarkable aspect of visual recognition is that humans are able to recognize the meaning (or "gist") of complex visual scenes within 1/20 of a second, independently of the quantity of objects in the scene. This rapid understanding phenomenon can be experienced while looking at rapid sequences in television advertisements and quick cuts in modern movie trailers. How is this remarkable feat accomplished? Research over the last decade has made substantial progress toward understanding the mechanisms underlying single object recognition, but less progress has been made toward understanding scene recognition. For example, computer systems fall well short of human performance in tasks that require recognizing the gist of a scene. Dr. Aude Oliva has undertaken a novel approach to this challenging question by studying mechanisms of analysis that are global in nature, focusing on statistically robust features describing the spatial layout of the scene (e.g. its volume, its perspective, its level of clutter) and not merely its components (e.g., the objects in a scene). With National Science Foundation support, Dr. Aude Oliva will conduct a five-year CAREER award study to examine how a global approach to image analysis can explain humans remarkable ability to recognize scenes and objects. Moreover she will use this approach to define operational strategies for machine vision systems. This program of research will combine a number of methodologies, including behavioral experiments (psychophysics, eye tracking), cognitive neuroscience methods (event-related potentials), and computational modeling. Applications of this work might include scene and space recognition systems to assist drivers, automatic systems that could provide semantic descriptions of the contents of large image databases, and computer assisted systems to aid the visually-impaired in navigating through visual space. The educational mission proposed by Dr. Oliva includes laboratory training of graduate and undergraduate students in the cognitive and computational methods of scene understanding, as well as a new course on computational visual cognition, and a winter tutorial together with an annual symposium both on scene understanding.