Search

The recognition of transitive, goal-directed actions requires a sensible balance between the representation of specific shape details of effector and goal object and robustness with respect to image transformations. We present a biologically-inspired architecture for the recognition of transitive actions from video sequences that integrates an appearance-based recognition approach with a simple neural mechanism for the representation of the effector-object relationship. A large degree of position invariance is obtained by nonlinear pooling in combination with an explicit representation of the relative positions of object and effector using neural population codes. The approach was tested on real videos, demonstrating successful invariant recognition of grip types on unsegmented video sequences. In addition, the algorithm reproduces and predicts the behavior of action-selective neurons in parietal and prefrontal cortex. [ABSTRACT FROM AUTHOR]

Mirror neurons in monkey premotor cortex of are active during the motor planning and the visual observation of actions. These neurons have recently received a vast amount of interest in cognitive neuroscience and have been discussed as potential basis of imitation learning and the understanding of actions. We present a model that explains visual properties of mirror neurons without a reconstruction of the three-dimensional structure of action and object. The proposed model is based on a small number of physiologically well-established principles. In addition, it postulates novel neural mechanisms for the integration of information about object and effector movement, which can be tested in electrophysiological experiments. [ABSTRACT FROM AUTHOR]