Cyberoptics Corporation

General Electric Company

Datacube, Inc.

Swaminathan Manickam

Steven K Case

David W Duquette

Thomas W Bushman

Kevin George Harding

Eric P Rudd

Timothy A Skunes

Christopher Allen Nafis

Gil Abramovich

Meena Ganesh

John P Konicek

Qingying Hu

Scott D Roth

This invention provides a method for contactless fingerprint detection and verification comprising illuminating a fingerprint and directing a reflected light through an imaging system using liquid crystal panels and birefringent elements to polarize the light. A plurality of polarized images are captured and used to calculate the depth of structural features on the fingertip. A means to generate a two-dimensional rolled equivalent image of the fingerprint is also provided which may then be used for verification and authentication. The invention also provides an imaging system for carrying out the method.

Method and system for contactless fingerprint detection and verification

The present invention includes a method of determining a location of a component on a workpiece. A before-placement standard image is acquired of an intended placement location on a standard workpiece. Then, a standard component is placed upon the standard workpiece and the placement is verified. An after-placement standard image is acquired and a standard difference image is created from the before and after standard images. Then, a before-placement test image is acquired of an intended placement location on the workpiece. A component is then placed upon the workpiece, and after-placement test image is acquired. A test difference image is created from the before and after test images. A first offset is calculated between the before standard difference image and the before test image. Then, the test difference is transformed based on the first offset to generate a difference test image (DTR) that is registered to the standard difference image. The standard difference image is correlated to the registered difference test image (DTR) to generate a registration offset indicative of placement efficacy.

Image analysis for pick and place machines with in situ component placement inspection

A pick and place machine includes a sensor disposed to acquire an image of a nozzle before a pick operation, and one or more images after the pick operation. Image analytics based upon these images reveal important characteristics that can be used to classify the pick operation. In some embodiments, a plurality of after-pick images are acquired at different poses (angular orientations).

Pick and place machine with improved component pick image processing

A system for digital image recognition which combines sparse correlation with image pyramiding to reduce the number of pixels used in correlation provides effective recognition of a reference image template without exhaustive correlation of all pixels in the reference image template. An optimal sparse pixel set is selected from the pixels of the reference image template by correlating the reference image template against a search image scene which is to be searched. Such a sparse pixel set includes those pixels which are optimal in defining the correlation sensitive features of the reference image template. By terminating the accumulation of sparse pixels at an optimal point, performance is maximized without compromising accuracy of recognition. The resultant optimal sparse pixel set is then correlated against the pixels in the search image scene through a series of transformations to find a match of the reference image template within the search image scene.

Rotation and scale invariant image finder

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Bellingham, MA, United States of America

Swaminathan Manickam