3m Innovative Properties Company

Michigan State University

Muhammad Jamal Afridi

Robert W Shannon

Gautam Singh

Nicholas Andrew Asendorf

Caroline M Ylitalo

Guruprasad Somasundaram

Kui Chen-Ho

Britton G Billingsley

Glenn E Casner

Jennifer Frances Schumacher

Steven H Kong

Michael A McCoy

Jonathan Dale Gandrud

Travis L Potts

James B Snyder

Nathan J Anderson

Timothy J Gardner

Golshan Golnari

Anne C Gold

Jonathan T Kahl

Silvia Geciova-Borovova Guttmann

Catherine L Aune

Matthew T Scholz

G Marco Bommarito

James W Howard

Himanshu Nayar

Robert D Lorentz

Shannon D Scott

Arash Sangari

Xiaoming Liu

Claire R Donoghue

Arun Ross

Elisa J Collins

Erik M Shapiro

Mojtaba Kadkhodaie Elyaderani

Optical articles including a spatially defined arrangement of a plurality of data rich retroreflective elements, wherein the plurality of retroreflective elements comprise retroreflective elements having at least two different retroreflective properties and at least two different optical contrasts with respect to a background substrate when observed within an ultraviolet spectrum, a visible spectrum, a near-infrared spectrum, or a combination thereof.

Optical articles and systems interacting with the same

A product authentication system includes one or more image capture devices that provide video data representative of an enhanced safety environment. The product authentication system can determine the continuous presence of a designated product during a time period, for example, during performance of a task in an enhanced safety environment. The video data can be spatiotemporally sliced according to multiple orientations with respect to the time axis and the two spatial axes. A plurality of data streams are created where the slices of each respective data stream have the same orientation. The data streams are processed according to one or more machine learning models, such as convolutional neural networks trained to determine whether the designated product is continuously present in the video data. An indicator of the continuous presence of the designated product can be provided to a downstream system and used to indicate whether functions of the downstream system are to be performed.

Video based continuous product detection

Systems and methods for authenticating material samples are provided. Digital images of the samples are processed to extract computer-vision features, which are used to train a classification algorithm along with location and optional time information. The extracted features/information of a test sample are evaluated by the trained classification algorithm to identify the test sample. The results of the evaluation are used to track and locate counterfeits or authentic products.

Image based counterfeit detection

Systems and methods for detecting/identifying novel material samples are provided. A test sample image is processed with a trained transformation function to obtain a transformed matrix. A measure of similarity of the test image based on the transformed matrix is compared to a threshold to determine whether the test sample is novel to a batch of material samples that are provided to train the transformation function.

Image based novelty detection of material samples

Systems and methods for image recognition are provided. A style-transfer neural network is trained for each real image to obtain a trained style-transfer neural network. The texture or style features of the real images are transferred, via the trained style-transfer neural network, to a target image to generate styled images which are used for training an image-recognition machine learning model (e.g., a neural network). In some cases, the real images are clustered and representative style images are selected from the clusters.

Neural style transfer for image varietization and recognition

Aspects of the present disclosure related to a computer system and method for analyzing whether a package has been opened. The system includes a memory and one or more processors communicatively coupled to the memory and can be configured to receive an image corresponding to a wrapped package. The one or more processors can also be configured to generate a set of feature values based on a visual representation of the wrapped package in the image. The one or more processors can also be configured to apply the set of feature values to at least one model that is trained based at least in part on a set of images that include the wrapped package having the status, to determine the status of the wrapped package.

Vision system for status detection of wrapped packages

A system and method are provided for tracking magnetically-labeled substances, such as transplanted cells, in subjects using magnetic resonance imaging (MRI). The method includes obtaining a quantity of a substance that comprises an MRI contrast compound or is otherwise magnetically-labeled for purposes of an MRI scan, administering the substance into a region of interest of a subject, performing an imaging scan of a portion of the subject comprising the region of interest, obtaining an imaging data set from the scan, reducing the dataset into pixel groupings based on intensity profiles, where the pixel groupings have a pixel size larger than the expected pixel size of a unit of the MRI contrast compound or magnetically-labeled substance, extracting candidate pixel matrices from the imaging data, training a machine learning (ML) module by using the candidate pixel matrices, quantifying the presence, number and/or location of units of the substance within the subject by using the ML module, and displaying a visual representation of an identification of the substances within the subject as a result of using the ML module.

System and method for quantifying cell numbers in magnetic resonance imaging (MRI)

Growing community of inventors

Woodbury, MN, United States of America

Muhammad Jamal Afridi