Intelligent spatial reasoning
First Claim
1. A computerized automatic spatial reasoning method to create spatial reasoning rules to characterize subtle physical, structural or geometrical conditions comprising the steps of:
- a) Inputting a plurality of image object sets, each object contains its associated image pixels;
b) Designating one of the image object sets as target object set and another object set as condition object set;
c) Calculating spatial mapping features for the target object set from transformed images of the target object set and the condition object set;
d) Performing spatial mapping feature learning using the spatial mapping features to create at least one salient spatial mapping feature output;
e) Performing spatial reasoning rule learning by a supervised learning method using the at least one spatial mapping feature to create at least one spatial reasoning rule output;
f) Using the spatial reasoning rule output to characterize spatial relations of multiple sets of objects for applications such as geographical information systems, cell image informatics, semiconductor or electronic automatic defect classification or military automatic target classification applications.
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Abstract
An intelligent spatial reasoning method receives a plurality of object sets. A spatial mapping feature learning method uses the plurality of object sets to create at least one salient spatial mapping feature output. It performs spatial reasoning rule learning using the at least one spatial mapping feature to create at least one spatial reasoning rule output. The spatial mapping feature learning method performs a spatial mapping feature set generation step followed by a feature learning step. The spatial mapping feature set is generated by repeated application of spatial correlation between two object sets. The feature learning method consists of a feature selection step and a feature transformation step and the spatial reasoning rule learning method uses the supervised learning method.
The spatial reasoning approach of this invention automatically characterizes spatial relations of multiple sets of objects by comprehensive collections of spatial mapping features. Some of the features have clearly understandable physical, structural, or geometrical meanings. Others are statistical characterizations, which may not have clear physical, structural or geometrical meanings when considered individually. A combination of these features, however, could characterize subtle physical, structural or geometrical conditions under practical situations. One key advantage of this invention is the ability to characterize subtle differences numerically using a comprehensive feature set.
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Citations
33 Claims
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1. A computerized automatic spatial reasoning method to create spatial reasoning rules to characterize subtle physical, structural or geometrical conditions comprising the steps of:
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a) Inputting a plurality of image object sets, each object contains its associated image pixels; b) Designating one of the image object sets as target object set and another object set as condition object set; c) Calculating spatial mapping features for the target object set from transformed images of the target object set and the condition object set; d) Performing spatial mapping feature learning using the spatial mapping features to create at least one salient spatial mapping feature output; e) Performing spatial reasoning rule learning by a supervised learning method using the at least one spatial mapping feature to create at least one spatial reasoning rule output; f) Using the spatial reasoning rule output to characterize spatial relations of multiple sets of objects for applications such as geographical information systems, cell image informatics, semiconductor or electronic automatic defect classification or military automatic target classification applications. - View Dependent Claims (2, 3, 4)
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5. A computerized automatic two image object set spatial mapping feature generation method to create spatial mapping features that can be used to characterize subtle physical, structural or geometrical conditions comprises the steps of:
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a) Inputting a first image object set containing its associated image pixels, designated as target image object set; b) Inputting a second image object set containing its associated image pixels, designated as condition image object set; c) Performing label propagation operation assigning labels to image pixels on the target image object set and the condition image object set to create target image object set transformed data and condition image object set transformed data output; d) Performing a spatial mapping features calculation using the target image object set transformed data and the condition image object set transformed data to create an image spatial mapping features output; e) Using the image spatial mapping features output to characterize spatial relations of two image object sets for applications such as geographical information systems, cell image informatics, semiconductor or electronic automatic defect classification or military automatic target classification applications. - View Dependent Claims (6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31)
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32. A computerized automatic multiple image object set spatial mapping feature generation method to create spatial mapping features that can be used to characterize subtle physical, structural or geometrical conditions comprising the steps of:
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a) Inputting a first image object set containing its associated image pixels, designated as target image object set; b) Inputting a plurality of additional image object sets, each object contains its associated image pixels; c) Designating each of the plurality of additional image object sets as condition image object set; d) Calculating spatial mapping features for the target image object set from transformed images of the target image object set and the condition image object set; e) Performing contrast feature extraction using spatial mapping features for each dual of the object set pairs to create a contrast features output; f) Using the contrast features output to characterize spatial relations of multiple sets of objects for applications such as geographical information systems, cell image informatics, semiconductor or electronic automatic defect classification or military automatic target classification applications. - View Dependent Claims (33)
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Specification