Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
C4.5: programs for machine learning
C4.5: programs for machine learning
Function-based generic recognition for multiple object categories
CVGIP: Image Understanding
Mind Bugs: The Origins of Procedural Misconceptions
Mind Bugs: The Origins of Procedural Misconceptions
Extracting a Valid Boundary Representation from a Segmented Range Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Explanation-Based Generalization: A Unifying View
Machine Learning
A New Approach to Fuzzy Classifier Systems
Proceedings of the 5th International Conference on Genetic Algorithms
Improved Rooftop Detection in Aerial Images with Machine Learning
Machine Learning
Automatic Object Recognition within an Office Environment
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
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
Efficient search and verification for function based classification from real range images
Computer Vision and Image Understanding
Learning function-based object classification from 3D imagery
Computer Vision and Image Understanding
Function-based classification from 3D data via generic and symbolic models
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
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Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. Such systems naturally associate a "measure of goodness" or "membership value" with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of diffierent properties of the object's shape. A membership function is used to compute the membership value when evaluating a primitive of a particular physical property of an object. In previous versions of a recognition system known as GRUFF, the membership function for each of the primitive evaluations was hand-crafted by the system designer. In this paper, we provide a learning component for the GRUFF system, called OMLET, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally applicable to any problem in which low-level membership values are combined through an and-or tree structure to give a final overall membership value.