RI: Small: Collaborative Research: Detecting Abnormalities in Images

近年来，计算机对图像的解释取得了长足的进步，但即使是最现代的算法也无法与人类在简单视觉任务中的能力相提并论。例如，在看一幅图像时，人们条件反射地根据对象所属的类别--人、动物、工具和其他重要类别--对其中的对象进行分类。这使我们能够理解物体在图像中的含义，例如，理解一个有许多食物的场景可能是一张餐桌。因为即使是现代的计算机视觉系统也不能做出这样的分类，它们不能自动检测出场景中的对象何时不属于，即它相对于场景中存在的类别是异常的。检测这种“古怪”或不典型的物体对于理解视觉场景至关重要，因为不属于的物体往往扮演着最重要的角色，需要立即采取行动(就像餐桌上的猫)。对人类受试者的研究表明，人类确实特别擅长检测非典型项目，这些项目往往在我们意识到之前就吸引了我们的视觉注意。该项目旨在开发算法技术，赋予计算机视觉系统同样的能力。通过采用现代视觉技术来模仿人类观察者对视觉异常进行分类的方式，研究人员将开发能够检查图像并自动检测异常物体的计算机系统，以及识别异常的性质和量化异常的程度。该项目涉及多所大学和多个科学专业的研究人员之间的合作，包括计算机视觉和人类视觉。其结果将是一类新的和有用的计算机视觉技术，可以在许多情况下应用于视觉图像理解。

Computer interpretation of images has taken huge strides in recent years, but even the most modern algorithms can't come close to matching human capabilities on simple visual tasks. For example, in a brief glance at an image, people reflexively classify the objects in it in terms of the categories they belong to--people, animals, tools, and other significant classes. This allows us to understand the objects' meaning in the image, for example understanding that a scene with many pieces of food might be a dinner table. Because even modern computer vision systems can't make such a classification, they can't automatically detect when an object in a scene doesn't belong, that is, when it is abnormal relative to the categories present in the scene. Detecting such "oddball" or atypical objects is essential to understanding visual scenes, because objects that don't belong are often the ones that play the most important role and require immediate action (like a cat on the dinner table). Studies of human subjects have shown that humans are indeed especially adept at detecting atypical items, which often draw our visual attention even before we become consciously aware of them.This project aims at developing algorithmic techniques to endow computer visions systems with the same ability. By adapting modern vision techniques to mimic the way human observers classify visual atypicality, researchers will develop computer systems that can examine an image and automatically detect abnormal objects, as well as identifying the nature of the abnormality and quantifying the degree of abnormality. The project involves a collaboration among researchers at multiple universities and multiple scientific specialties, including both computer vision and human vision. The result will be a new and useful class of computer vision techniques that can be applied to visual image understanding in many contexts.