The society of mind
Generating and generalizing models of visual objects
Artificial Intelligence
Similarity, typicality, and categorization
Similarity and analogical reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Function-based generic recognition for multiple object categories
CVGIP: Image Understanding
Machine Learning
3D Model Retrieval with Spherical Harmonics and Moments
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
FOCUS: a generalized method for object discovery for robots that observe and interact with humans
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Learning function-based object classification from 3D imagery
Computer Vision and Image Understanding
Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Humans have abstract models for object classes which helps recognize previously unseen instances, despite large intra-class variations. Also objects are grouped into classes based on their purpose. Studies in cognitive science show that humans maintain abstractions and certain specific features from the instances they observe. In this paper, we address the challenging task of creating a system which can learn such canonical models in a uniform manner for different classes. Using just a few examples the system creates a canonical model (COMPAS) per class, which is used to recognize classes with large intra-class variation (chairs, benches, sofas all belong to sitting class). We propose a robust representation and automatic scheme for abstraction and generalization. We quantitatively demonstrate improved recognition and classification accuracy over state-of-art 3D shape matching/classification method and discuss advantages over rule based systems.