Minimally Supervised Acquisition of 3D Recognition Models from Large Visual Corpora

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Abstract IIS-9977206Nelson, RandalUniversity of Rochester$99,133 - 12 mos.Minimally Supervised Acquisition of 3D Recognition Models from Large Visual CorporaThis is the first year award of a three-year continuing grant. This research concerns with training 3D object recognition systems from imagery, which will allow the recognition of objects without constructing a 3D geometric model, and the recognition of objects where current geometric recognition technology does not apply. In various forms, image-based methods are currently the most successful general approach. Two issues that have been little explored are: (1) whether such systems can be trained from imagery that is unlabeled, and (2) whether they can be trained from imagery that is not trivially segmentable. These questions are the focus of this research proposal. Using a 3D recognition system the PI has developed, and possibly others, the project will investigate training, updating, and refining 3D representations for recognition from unstructured or semi-structured imagery and demonstrate the technology through working prototypes.Issues connected with training include basic algorithm design, controlling growth of the representation dealing with clutter, and correcting mistakes. Issues associated with the training corpus include quantifying the required density of salient images, allowable clutter, and required seed information. Issues connected to the recognition system include handling clutter, space requirements, and new capabilities such as dealing with hierarchical representations or flexible objects. Formal models for the various stages of the process will be developed.