CAREER: Combinatorial Inference and Learning for Fusing Recognition and Perceptual Grouping

当面对一幅新图像时，人类通常在轮廓、表面、连接点和它们之间的关系方面提供一致的场景解释方面几乎没有问题。这种感知组织的过程与对熟悉的形状和材料的识别密切相关。感知组织可以通过将混乱场景的复杂性降低到少量候选表面来帮助识别，而识别可以帮助解决基于局部图像线索分组的模糊性。该项目正在开发一种计算框架，将自上而下的识别信息与自下而上的感知组织融合在一起，以自动产生连贯的场景解释。本研究包括(1)识别局部图像特征，为分组和图形-背景提供线索；(2)开发可组合检测器库，捕获物体、部分及其空间关系的外观；(3)设计模型和有效的推理例程，明确推理遮挡和图像区域和轮廓与物体形状的绑定。分组和识别的集成模型对扩展必须在复杂、杂乱的环境中运行的机器人和辅助技术的计算机视觉能力具有直接意义。正在开发的框架还应用于自动化生物图像分析，其中自上而下的形状信息在解决噪声局部测量中很有用。该项目开发的计算工具以及传播和教育工作旨在形成生物成像和前沿计算机视觉研究之间的跨学科桥梁。

When presented with a novel image, humans typically have little problem providing a consistent interpretation of the scene in terms of contours, surfaces, junctions, and the relations between them. This process of perceptual organization is closely coupled with recognition of familiar shapes and materials. Perceptual organization can aid recognition by reducing the complexity of a cluttered scene to a small number of candidate surfaces while recognition can help resolve ambiguities in grouping based on local image cues. This project is developing a computational framework that fuses top-down information provided by recognition with bottom-up perceptual organization in order to automatically produce a coherent scene interpretation. This research includes (1) identifying local image features that provide cues to grouping and figure-ground, (2) developing libraries of composable detectors that capture the appearance of objects, parts and their spatial relations, and (3) designing models and efficient inference routines that explicitly reason about occlusion and the binding of image regions and contours into object shapes.Integrated models of grouping and recognition have direct significance to expand the computer vision capabilities of robotics and assistive technologies that must operate in complex, cluttered environments. The framework being developed also has applications in automating biological image analysis where top-down shape information are useful in resolving noisy local measurements. The computational tools developed by the project along with dissemination and educational efforts are aimed at forming an interdisciplinary bridge between biological imaging and cutting-edge computer vision research.