National Instruments Corporation

Bruce Smyth

Dinesh R Nair

Nicolas F Vazquez

Matthew S Fisher

Satish V Kumar

Subramanian Ramamoorthy

Robert J B Giesen

Joshua B Keeler

System and method for characterizing vision systems. A multi-dimensional condition space is provided, each dimension representing a respective condition axis, where each point in the condition space specifies a set of conditions under which a vision system may operate. An image is provided. The condition space is sampled according to a pseudo-random sequence, e.g., a low-discrepancy sequence, to determine a plurality of test conditions usable to characterize the vision system, where each test condition corresponds to a respective set of conditions. A plurality of test images corresponding to the plurality of test conditions are generated based on the image, e.g., by applying image processing functions to the image that simulate the test conditions. A vision inspection is performed on each of the plurality of test images to generate respective test results, and the test results are analyzed to determine conditions under which the vision system operates correctly.

Characterizing vision systems

A geometric pattern matching method to locate instances of a template image in a target image using a plurality of models with different feature combinations. A learning phase involves learning geometric information about the template image and creating a model for each of a plurality of different respective parent features. Each model comprises a relationship tree from the respective parent feature to one or more other child features, and hence each model may have different feature combinations. In a matching phase, the method may examine a plurality of different models to determine matches in the target image being analyzed. The matching phase may select different models dynamically based on prior matching results.

Geometric pattern matching using dynamic feature combinations

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Hollis, NH, United States of America

Bruce Smyth