Determining Attitude of Object From Needle Map Using Extended Gaussian Image
Determining Attitude of Object From Needle Map Using Extended Gaussian Image
Structured descriptions of complex curved objects for recognition and visual memory.
Structured descriptions of complex curved objects for recognition and visual memory.
Performance measurement and analysis of certain search algorithms.
Performance measurement and analysis of certain search algorithms.
On the Recognition of Curved Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Decision trees for geometric models
SCG '93 Proceedings of the ninth annual symposium on Computational geometry
Bounds on Shape Recognition Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
The complexity of perceptual search tasks
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
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The problem of recognizing what objects are where in the workspace of a robot can be cast as one of searching for a consistent matching between sensory data elements and equivalent model elements. In principle, this search space is enormous, and to control the potential combinatorial explosion, constraints between the data and model elements are needed. A set of constraints for sparse sensory data that are applicable to a wide variety of sensors are derived, and their characteristics are examined. Known bounds on the complexity of constraint satisfaction problems are used, together with explicit estimates of the effectiveness of the constraints derived for the case of sparse, noisy, three-dimensional sensory data, to obtain general theoretical bounds on the number of interpretations expected to be consistent with the data. It is shown that these bounds are consistent with empirical results reported previously. The results are used to demonstrate the graceful degradation of the recognition technique with the presence of noise in the data, and to predict the number of data points needed, in general, to uniquely determine the object being sensed.