Scandit Ag

Standard Cognition, Corp.

Trimble Navigation Limited

Trimble Inc.

Other

Massachusetts Institute of Technology

Thingmagic, Inc.

Standard Cognitions Corp.

Bernd Schoner

Christian Floerkemeier

Ravikanth Pappu

Matthias Bloch

Oliver Akermann

Maiya Shur

Luc Humair

Brian Ahern

Yael Gregory Maguire

Matthew Stephen Reynolds

Fabian Nater

Sumukh N Pathare

Harry F Tsai

E Rehmatulla Post

Joseph L Richards

Marco Valentino Biasini

John Biondo

Thibault Priquel

Vitali Samurov

Alan Sharp

Iciar Martinez

Thomas Wesner

Christian Kündig

Peter Fredriksson

Linda Chase

Neil Adam Gershenfeld

Eric Metois

Satyan Shah

Brian Fiegel

Ugur Kart

Suren Vagharshakyan

Kimmo Roimela

Michael Blase Gwilliam

Image analysis is used to provide label verification. An image of a label within a scene is acquired. The label includes a barcode and characters. The barcode on the label is decoded to receive an identity of an object. Optical character recognition is performed to recognize the characters on the label. Recognized characters are compared to information about decoded barcode to verify the label is correct.

Label verification using image analysis

Scanning for optical patterns in a real scene using one or more images of the real scene acquired by a camera can sometimes miscount a number of optical patterns in the scene. An interface is provided to allow a user to adjust a count of the number of optical patterns.

Multi-barcode detection in a real scene using interface enhancement based on user input

A camera on a robot is used to acquire images of a shelving unit. Locations of labels are detected and cropped from the images to form segments. The segments are analyzed to obtain scan descriptions, without decoding barcodes on the labels. The scan descriptions are compared to catalog descriptions to match scan descriptions to catalog descriptions. Products can then be mapped within an environment.

Building a catalog based on optical recognition of patterns in images

Selection of on optical pattern in a scene is identified by overlaying, on a display, an indicator of a detected optical pattern identifying a location of the optical pattern in one or more images, receiving a user input on the display at a position that does not overlap the location of the optical pattern, and presenting information related to the optical pattern, based on receiving the user input, even though the position of user input did not overlap the location of the optical pattern. The user input can be received at a detached selection indicator and/or using an adaptive input area.

Adaptive selection area for image-based decoding of optical patterns

Selection and decoding of an optical pattern in a real scene from a frozen image of a video preview includes presenting a plurality of images on a display. A plurality of optical patterns are in the plurality of images. The display is frozen by one image, a frozen image, on the display. An optical pattern is identified by a user selecting the optical pattern in the frozen image. The optical pattern is decoded.

Selection and decoding of an optical pattern within a real scene using a frozen image from a video preview

Image analysis from one or more cameras are used for monitoring items on a display. A count of items on the display is received. Images from the one or more cameras are used to count a number of items removed from the display. A report is generated containing a number of items on the display, after counting the number of items removed from the display using the images from the one or more cameras.

Image analysis in a mechanized store for monitoring items on a display

A camera is used to acquire images. Labels are detected in the images. The labels contain a barcode and product information. The product information is analyzed (e.g., by optical character recognition) to obtain a plurality of scan descriptions. Scan descriptions are matched with catalog descriptions to obtain stock keeping units (SKUs). The SKUs are mapped to locations within an environment based on position data of the camera at the time images are acquired.

Mapping items to locations within an environment based on optical recognition of patterns in images

Image analysis is used to map objects in an arrangement. For example, images of a retail shelf are used to map items for sale on the retail shelf A first vector can used to identify a relative position of a first item on a shelf to a shelving diagram, and a second vector can be used to identify a relative position of a second on the shelf to the shelving diagram, using locations of optical codes (e.g., barcodes). Absolute positions can be calculated. In some configurations, multiple images having different fields of view are matched to an overview image.

Image analysis for mapping objects in an arrangement

A mechanized store uses a mobile device to authenticate the user. Items removed from one or more displays of the mechanized store by the user are tracked and a list of items removed by the user is updated. The list of items removed is linked with an account of the user.

Registry verification with authentication using a mobile device

An image is acquired by a camera. The image has a first set of characters and a second set of characters. The first set of characters are classified as an identifier. The second set of characters are classified as data associated with the identifier. The image is divided to create an image segment. The image segment includes the first set of characters and not the second set of characters. The first set of characters are decoded in the image segment to generate a first character string. The second set of characters are decoded to generate a second character string. The first character string is linked to the second character string based on classifying the first set of characters as the identifier and the second set of characters as the data associated with the identifier.

Classifying and segmenting characters within an image

Depth information from a depth sensor, such as a LiDAR system, is used to correct perspective distortion for decoding an optical pattern in a first image acquired by a camera. Image data from the first image is spatially correlated with the depth information. The depth information is used to identify a surface in the scene and to distort the first image to generate a second image, such that the surface in the second image is parallel to an image plane of the second image. The second image is then analyzed to decode an optical pattern on the surface identified in the scene.

Perspective distortion correction of discrete optical patterns in images using depth sensing

For scanning optical patterns, such as two-dimensional QR codes, with a mobile device at increased distances, a first image is acquired. A region of interest likely containing the optical pattern in the first image is identified. The mobile device then zooms in on the region of interest and a second image is acquired. The optical pattern is then decoded using the second image.

Optical data exchange while preserving social distancing

A method of image analysis is provided for recognition of a pattern in an image. The method includes receiving a plurality of images acquired by a camera, where the plurality of images include a plurality of optical patterns in an arrangement. The method also includes matching the arrangement to a pattern template, wherein the pattern template is a predefined arrangement of optical patterns. The method also includes identifying an optical pattern of the plurality of optical patterns as a selected optical pattern based on a position of the selected optical pattern in the arrangement. The method also includes decoding the selected optical pattern to generate an object identifier and storing the object identifier in a memory device.

Recognition and indication of discrete patterns within a scene or image

A memory device is provided including instructions that, when executed, cause one or more processors to perform the steps including receiving a plurality of images acquired by a camera, the plurality of images including a plurality of optical patterns, wherein an optical pattern of the plurality of optical patterns encodes an object identifier. The steps include presenting the plurality of images comprising the plurality of optical patterns on a display, and presenting a plurality of visual indications overlying the plurality of optical patterns in the plurality of images. The steps also include identifying a selected optical pattern of the plurality of optical patterns based on a user action and a position of the selected optical pattern in one or more of the plurality of images. The steps also include decoding the selected optical pattern to generate the object identifier and storing the object identifier in a second memory device.

Recognition and selection of a discrete pattern within a scene containing multiple patterns

Images of optical codes are mapped to an overview image to localize optical codes within a space. By localizing optical codes, information about locations of various products can be ascertained. One or more techniques can be used to map the images of optical codes to the overview image. The overview image can be a composite image formed by stitching together several images.

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Cambridge, MA, United States of America

Bernd Schoner