Zebra Technologies Corporation

Francesco Lupo

Andrea Mirabile

Andrea Bordone Molini

Miroslav Trajkovic

Christopher Warren Brock

Alessandro Bay

Anatoly Kotlarsky

A controller using image processing to identify produce is disclosed herein. The controller may include one or more memories and one or more processors communicatively coupled to the one or more memories. In some implementations, a controller may receive a trigger associated with presence of an item at a product processing zone. The controller may capture, via an imager, an image representing the item. Accordingly, the controller may apply a first convolutional neural network (CNN) to the image to determine a bounding box associated with the item. The controller may determine that the item within the bounding box satisfies an occlusion threshold and may apply a second CNN to the image to determine a query image representation. Accordingly, the controller may receive, from a database, an indication of one or more candidate items based on the query image representation and indicate, via a user interface, the candidate item(s).

Using image processing to identify produce

A method includes: capturing, by a scanner device comprising an image sensor, first image data representing at least a portion of a first item; detecting, by the scanner device, a first barcode represented in the first image data; decoding the first barcode to determine first identifier data for the first item; performing a lookup using the first identifier data to determine a first entry for the first item stored in a data store; generating a sub-image representing a region-of-interest of the first item; generating, using an image encoder, a first vector representation of the sub-image; comparing the first vector representation of the sub-image to at least one second vector representation stored in the data store in association with the first entry; and generating first data indicating that the first barcode is matched with the first item.

Barcode-aware object verification using image encoder

Systems and methods for enhancing decoding of indicia are disclosed herein. An example implementation includes: generating a visual embedding from image data; identifying, in the image data, a partial indicia and decoding the partial indicia to determine at least a partial payload; responsive to the at least partial payload not being a full payload, determining one or more candidate visual embeddings corresponding to the partial payload; comparing the one or more candidate visual embeddings to the visual embedding generated from the image data and assigning a correspondence score for each of the one or more candidate visual embeddings based on the comparison; and determining a recommendation based on the correspondence score for each of the one or more candidate visual embeddings.

Object identification based on a partial decode

A method and system for operating an indicia reader are disclosed herein. An example method includes detecting a first object in the scanning region; capturing one or more images of the first object to create first image data; determining that a successful decode of an indicia has not occurred; retrieving the first image data of the first object; generating, using the first image data, an embedding of the first image data; detecting a second object in the scanning region; retrieving one or more support samples from an image database; comparing the embedding of the image data to each support sample of the one or more support samples; based on the comparison that the embedding of the image data does not match any support sample, performing an operation.

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London, United Kingdom

Francesco Lupo