Abstract: Systems and methods include automatically identifying items from images of items positioned at a POS system. Cameras capture images of the items in which each camera captures images with different FOVs of the items thereby capturing different item parameters of each item. The items are identified when item parameters of the items match item parameters previously identified and the items are failed to be identified when item parameters fail to match item parameters previously identified. Image pixels associated with each unknown item are extracted and mapped to real world coordinates associated with each unknown item as positioned at the POS system. A bounding polygon for each unknown item is generated that encapsulates each unknown item based on the image pixels mapped to the real world coordinates. The bounding polygon is projected onto each unknown item thereby providing feedback for each unknown item positioned at the POS system.

Abstract: Systems and methods include extracting item parameters from images of items positioned at a POS system. UPC features included in the item parameters associated with each item when combined are indicative as to an indication of the UPC of each item. The UPC features are analyzed to determine whether the UPC features associated with each item when combined match a corresponding combination of UPC features stored in a database. The database stores different combinations of UPC features with each different combination of UPC features associated with a corresponding item thereby identifying each corresponding item based on each different combination of UPC features associated with each item. Each item positioned at the POS system is identified when the UPC features associated with each item when combined match a corresponding combination of UPC features as stored in the database.

Abstract: Systems and methods include extracting item parameters from images of items positioned at a POS system. Item parameters associated with each item when mapped into a feature vector for each item are indicative as to an identification of the item. The feature vectors are analyzed to determine whether item parameters when combined and mapped into the feature vectors match a corresponding feature vector stored in a database. The database stores different combinations of item parameters as mapped into different stored feature vectors with different stored feature vectors associated with different items thereby identifying each item based on each different combination of item parameters as mapped into each stored feature vector associated with each item. Each item positioned at the POS system is identified when the feature vectors associated with each item match a corresponding stored feature vector as stored in the database.

Abstract: Systems and methods include extracting item parameters from images of items positioned at a POS system. Item parameters associated with each item when mapped into a feature vector for each item are indicative as to an identification of the item. The feature vectors are analyzed to determine whether item parameters when combined and mapped into the feature vectors match a corresponding feature vector stored in a database. The database stores different combinations of item parameters as mapped into different stored feature vectors with different stored feature vectors associated with different items thereby identifying each item based on each different combination of item parameters as mapped into each stored feature vector associated with each item. Each item positioned at the POS system is identified when the feature vectors associated with each item match a corresponding stored feature vector as stored in the database.

Abstract: Systems and methods include extracting item parameters from images of items positioned at a POS system. The item parameters associated with each item are indicative as to an identification of each item thereby enabling the identification of each item based on the item parameters. The item parameters are analyzed to determine whether the item parameters match item parameters stored in a database. The database stores different combinations of item parameters to thereby identify each item based on each different combination of item parameters associated with each item. Each item positioned at the POS system is identified when the item parameters for each item match item parameters as stored in the database and fail to identify each item when the item parameters fail to match item parameters. The item parameters associated with the items that fail to match are streamed to the database thereby enabling the identification of each failed item.

Abstract: Assisted checkout devices, including point-of-sale stations, can use computer vision and machine learning to speed the checkout process while maintaining human verification, assistance, and customer interaction provided by human clerks. A plurality of optical sensors, including cameras, can be arranged with different views of a checkout plane upon which items being purchased by a buyer are placed. Moreover, one or more support towers can be utilized to elevate the optical sensors to vertical heights at which the checkout plane, and items placed thereon, is within the field of view of the optical sensors. The information captured by the plurality of optical sensors can be analyzed using machine learning models to detect and identify the items placed on the checkout plane.

Abstract: Systems and methods include extracting item parameters from images of items positioned at a POS system. The item parameters associated with each item are indicative as to an identification of each item thereby enabling the identification of each item based on the item parameters. The item parameters are analyzed to determine whether the item parameters match item parameters stored in a database. The database stores different combinations of item parameters to thereby identify each item based on each different combination of item parameters associated with each item. Each item positioned at the POS system is identified when the item parameters for each item match item parameters as stored in the database and fail to identify each item when the item parameters fail to match item parameters. The item parameters associated with the items that fail to match are streamed to the database thereby enabling the identification of each failed item.

Abstract: Visual analytics systems can use video data from cameras placed throughout a location (e.g., a store or a hospital) to determine poses, gestures, tracks, journeys, and actions, and thereby to provide broader analytics useful in realizing efficiencies and improving human behaviors. Data from visual analytics systems can be used to generate alerts, make useful predictions, and run simulations to test hypotheses.

Abstract: The invention is a method of placement of components and networks (nets), utilized for interconnecting the components, of a circuit layout. The method includes forming for electrical devices, pads (or lands) and networks (nets) of a circuit layout a listing of the positions thereof with respect to one another, connections therebetween and the orientation of each net or subnet thereof in the circuit layout. The thus formed list is processed subject to at least one objective regarding the size of the circuit layout, whereupon a placement of the electrical devices and the pads is determined simultaneously with the placement of the networks.

Abstract: A system and method for modeling an IC (integrated circuit) employs a mesh model and a grid model for separating impedance effects between nearby and far-away pairs of mesh elements. Models for relating currents and voltages can be incrementally adapted from other designs or design elements in applications including mixed-signal, analog and RF (radio frequency) circuits.

Abstract: A system and method for modeling an IC (integrated circuit) employs a mesh model and a grid model for separating impedance effects between nearby and far-away pairs of mesh elements. Models for relating currents and voltages can be incrementally adapted from other designs or design elements in applications including mixed-signal, analog and RF (radio frequency) circuits.

Abstract: To estimate a distribution of voltages or currents in the layers of a multi-layer circuit, an exemplary current flow in each layer is discretized into a number of current vector elements and at least one scalar charge element related to the charge associated with each current vector element. A first distribution of voltages induced in each circuit layer is determined from current vector elements in all of the circuit layers. A second distribution of voltages induced in each circuit layer is determined from the scalar charge elements in all of the circuit layers. For each circuit layer, the first and second distributions of voltages induced therein are combined to determine an actual distribution of voltages in the circuit layer.