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We propose a geometric method for visual tracking, in which the 2-D affine motion of a given object template is estimated in a video sequence by means of coordinate-invariant particle filtering on the 2-D affine group Aff(2). Tracking performance is further enhanced through a geometrically defined optimal importance function, obtained explicitly via Taylor expansion of a principal component analysis...
Recent studies have shown that the perception of natural movements - in the sense of being "humanlike" - depends on both joint and task space characteristics of the movement. This paper proposes a movement generation framework that merges two established techniques from gesture recognition and motion generation - hidden Markov models (HMMs) and principal components - into an efficient and...
This paper proposes a hidden Markov model (HMM) based approach to generate human-like movements for humanoid robots. Given human motion capture data for a class of movements, principal components are extracted for each class, and used as basis elements that in turn represent more general movements within each class. A HMM is also designed and trained for each movement class using the movement data...
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