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This research project is a study of the role of fixation and visual attention in object recognition. In this project, we build an active vision system which can recognize a target object in a cluttered scene efficiently and reliably. Our system integrates visual cues like color and stereo to perform figure/ground separation, yielding candidate regions on which to focus attention. Within each image region, we use stereo to extract features that lie within a narrow disparity range about the fixation position. These selected features are then used as input to an Alignment-style recognition system. We show that visual attention and fixation significantly reduce the complexity and the false identifications in model-based recognition using Alignment methods. We also demonstrate that stereo can be used effectively as a figure/ground separator without the need for accurate camera calibration.
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Perceptual modeling in the problem of active object recognition in visual scenes
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Familiarity based unified visual attention model for fast and robust object recognition
Even though visual attention models using bottom-up saliency can speed up object recognition by predicting object locations, in the presence of multiple salient objects, saliency alone cannot discern target objects from the clutter in a scene. Using a ...
An autonomous visual perception model for robots using object-based attention mechanism
ROBIO'09: Proceedings of the 2009 international conference on Robotics and biomimeticsThe object-based attention theory has shown that perception processes only select one object of interest from the world at a time which is then represented for action. This paper therefore presents an autonomous visual perception model for robots by ...