A robotic system is disclosed that include an articulated arm and a first perception system for inspecting an object, as well as a plurality of additional perception systems, each of which is arranged to be directed toward a common area in which an object may be positioned by the robotic arm such that a plurality of views within the common area may be obtained by the plurality of additional perception systems.

Disclosed in the present specification is a self-checkout device to which hybrid product recognition technology is applied and which helps a user to more conveniently and quickly make a payment. The self-checkout device according to the present specification photographs a product with a plurality of cameras, and then recognizes a barcode by detecting a barcode region from the captured images and, simultaneously, extracts a feature point of the images such that a product can be recognized through the calculation of the proportion that matches with a reference image of the product. Thus, a product can be quickly recognized through barcode recognition and packaging paper recognition in a product image.

The present disclosure describes method, apparatus, and storage medium for identifying an identification code. The method includes obtaining, by a computer device, a to-be-detected picture. The computer device includes a memory storing instructions and a processor in communication with the memory. The method also includes detecting, by the computer device, an identification code in the to-be-detected picture to obtain a detection result, the detection result comprising target information of a target code corresponding to the identification code; sampling, by the computer device, the target code according to the target information, to obtain a sampled image; and decoding, by the computer device, the sampled image, to obtain an identification result corresponding to the identification code.

The present disclosure relates generally to the processing of machine-readable visual encodings in view of contextual information. One embodiment of aspects of the present disclosure comprises obtaining image data descriptive of a scene that includes a machine-readable visual encoding; processing the image data with a first recognition system configured to recognize the machine-readable visual encoding; processing the image data with a second, different recognition system configured to recognize a surrounding portion of the scene that surrounds the machine-readable visual encoding; identifying a stored reference associated with the machine-readable visual encoding based at least in part on one or more first outputs generated by the first recognition system based on the image data and based at least in part on one or more second outputs generated by the second recognition system based on the image data; and performing one or more actions responsive to identification of the stored reference.

A food inventory device having a bar code scanner and camera to add and remove inventory items to and from the inventory. The scanner may be mounted on an inventory storage container to facilitate inventory control from one or more storage devices, including refrigerators, freezers, and pantries. The inventory system may display a table listing the inventory items along with variable details. Based on the food inventory list, the inventory system may suggest recipes, display the ingredients in stock for suggested recipes, and create a shopping list of the ingredients not in stock for a desired recipe. The device may have an ordering system allowing users to order items in the storage device that have been exhausted or otherwise need to be replenished through audio or touch input. The ordering system may be connected to third-party external sites and may be based on one or more user profile settings.

Machine vision techniques for determining a region of interest (ROI) are disclosed herein. An example implementation includes a computing device for executing an application, the application operable to: (1) receive a first image; (2) set an first ROI of the first image to a field of view (FOV) of the first image; (3) determine a barcode within the first ROI; (4) determine a bounding box of the barcode; (5) form a second ROI based on the bounding box; (6) receive a second image; and (7) set an ROI of the second image to be the second ROI of the first image.

A method in a mobile computing device includes: controlling a camera to capture an image; tracking, in association with the image, a pose of the mobile computing device in a coordinate system; detecting a region of interest (ROI) depicting an item in the image; determining a location of the ROI in the coordinate system, based on the tracked pose; obtaining an item identifier corresponding to the ROI by (i) when a previously recognized item identifier is not available, executing a recognition mechanism to derive the item identifier from the ROI, and (ii) when a previously recognized item identifier is available, bypassing the recognition mechanism and retrieving the previously recognized item identifier; and returning the obtained item identifier corresponding to the ROI.

A method for processing the packages in a material handling environment is disclosed. The method includes receiving an image of a field of view of an image capturing device. The image comprising at least one decodable indicia having a predefined shape, the at least one decodable indicia attached to a package. The method further includes identifying one or more decodable indicia image coordinates, wherein each of the one or more decodable indicia image coordinates define a corner of the at least one decodable indicia using a 2-dimensional (2D) coordinate system. The method further includes transforming the one or more decodable indicia image coordinates to one or more decodable indicia spatial coordinates in the 3D coordinate system. Further, the method includes comparing the one or more decodable indicia spatial coordinates with one or more 3D region coordinates to determine whether the package is within a 3D region.

A system for manufacturing elastomeric components is provided. The system may include a molding station having a mold configured to receive an elastomeric material, form a pad that includes a plurality of untrimmed elastomeric components, and cure the pad. The system may further include an automated marking station comprising a laser and a camera. The automated marking station may be configured to remove the cured pad from the molding station, present the cured pad to the laser to form a mark on each of the untrimmed elastomeric components, and present the cured pad to the camera to capture an image of each mark. A process for manufacturing the elastomeric components is also provided.

A multifunctional recognition device includes: a housing formed on one side thereof with an opening; a recognition section disposed on an inner side of the housing to face the opening to recognize an image of a recognition target or a barcode provided on one side of the recognition target; an information processing section provided on the inner side of the housing to be connected to the recognition section to store and analyze recognition information recognized by the recognition section; a switching section provided on one side of the housing to be connected to the information processing section to switch a setting of the recognition section to a barcode recognition mode or an image capture mode; and a communication section configured to transmit the information stored and analyzed by the information processing section to an outside.