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Training gesture recognizers with synthetic data generated from real gestures is a well known and powerful technique that can significantly improve recognition accuracy. In this paper we introduce a novel technique called <i>gesture path stochastic resampling</i> (GPSR) that is computationally efficient, has minimal coding overhead, and yet despite its(More)
Visual pattern recognition and classification is a challenging computer vision problem. Genetic programming has been applied towards automatic visual pattern recognition. One of the main factors in evolving effective classifiers is the suitability of the GP language for defining expressions for feature extraction and classification. This research presents a(More)
—In this paper, we propose a framework to enable an autonomous robot to manipulate objects in cluttered scenes. Manipulation of objects in a complex cluttered scene demands an extremely precise pose estimation system. In order to precisely estimate object poses, a database of the objects should be acquired from earlier encounters. Hence, in addition to the(More)
Despite decades of research, there is yet no general rapid prototyping recognizer for dynamic gestures that can be trained with few samples, work with continuous data, and achieve high accuracy that is also modality-agnostic. To begin to solve this problem, we describe a small suite of accessible techniques that we collectively refer to as the Jackknife(More)
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