CAREER: Ground Truth Dataset and Benchmarks for Mid-Level Vision

大卫河机器视觉系统现在可以做令人惊讶的事情：阅读虹膜和人脸，帮助在真实的环境中驾驶自动汽车，在医学扫描中定位和测量解剖结构--这些只是近年来出现的能力的几个例子。特殊用途领域仍然标志着我们成功的极限，然而，人类的目标-水平的机器视觉仍然遥不可及，因为这些问题的解决方案并不要求机器理解视觉信息的丰富结构。视觉感知的问题。 该项目的主要目标是构建地面实况图像注释的数据集，该数据集在表面，对象和基本3D场景几何体的级别上提供场景的感知。 这个数据集将是前所未有的丰富和详细，精确地提供了为机器视觉系统带来通用功能所需的信息和表示。 该项目的第二个目标是创建相关的基准和方法，用于评估机器系统的地面实况数据。智力优势：该提案的核心是设计一个用于中级视觉的数据集。 在这个项目中提出的视觉信息的中级表示是至关重要的，因为目前还没有可行的机器视觉中级表示。 一个好的中级表示既可以从图像计算，也可以用于更高级别的任务。 一个通用的，具体的，可测试的中级表示可能是最重要的交付拟议的项目。更广泛的影响：拟议的项目将对机器视觉和人类视觉研究社区产生广泛的影响。 机器视觉模型需要复杂的地面真实数据进行训练和基准评估，而心理物理学建模者则面临来自自然图像的数据的挑战。 此外，该项目将向研究界免费提供其所有数据和工具。 总的来说，这是机器视觉的一个激动人心的时刻。 我们正处于建造具有人类视觉感知能力的机器的门槛，这将极大地改变人与机器之间的关系。 有了针对战略研究问题的数据集，我们就可以取得重大进展。网址：http://vision.bc.edu/~dmartin/MidLevel

David R. MartinAbstractMachine vision systems can now do amazing things: Reading irises and faces, helping to drive autonomous cars in real environments, locating and measuring anatomical structures in medical scans -- these are just a few examples of capabilities that have emerged in recent years.Special-purpose domains still mark the limit of our success, however.The goal of human-level machine vision is still out of reach because the solutions found to these problems do not require the machine to understand the rich structure of visual information.It is essential to take an empirical approach to the problem of visual perception. The primary goal of this project is to build a dataset of ground truth image annotations that provides the perception of scenes at the level of surfaces, objects, and basic 3D scene geometry. This dataset will be unprecedentedly rich and detailed, providing precisely the information and representations needed to bring general purpose capabilities to machine vision systems. A secondary goal of this project is to create the associated benchmarks and methodologies for evaluating machine systems with respect to the ground truth data.Intellectual Merit: At the heart of this proposal is the design of a dataset for mid-level vision. The mid-level representation of visual information proposed in this project is of fundamental importance, because there is currently no viable mid-level representation in machine vision. A good mid-level representation is both computable from images as well as useful for higher level tasks. A generic, concrete, and testable mid-level representation is perhaps the most important deliverable of the proposed project.Broader Impact: The proposed project will have broad impact on the machine vision and human vision research communities. Machine vision models require complex ground truth data for training and benchmarks for evaluation, while psychophysics modelers face the challenge of data from natural images. Additionally, this project will make all its data and tools freely available to the research community. In general, this is an exciting time for machine vision. We are at the threshold of building machines that attain human-level visual perception, which would dramatically alter the relationship between people and machines. With datasets targeting the strategic research problems, significant progress is at hand.URL: http://vision.bc.edu/~dmartin/MidLevel