Determining Three-Dimensional Shape from Orientation and Spatial Frequency Disparities
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Neural Coding in the Dorsal Visual Stream
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
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Within the framework of a model of vision-based robotic grasping inspired on neuroscience data, we deal with the problem of object orientation estimation by analyzing human psychophysical data in order to reproduce them in an artificial setup. A set of ANN is implemented which, on the one hand, allows to replicate some neuroscientific findings and, on the other hand, constitutes a tool for slant estimation that can improve the reliability of artificial vision systems, namely those dedicated to analyze visual data inherent to the interaction robot-environment, such as in grasping actions. The implementation confirms the hypothesis that integration of monocular and binocular data for the extraction of action-related object properties can provide an artificial system with improved pose estimation capabilities.