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

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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.