The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method includes a processing device receiving image data having one or more image frames indicative of a state of a meal preparation area. The method further includes the processing device determining, based on the image data, one of a meal preparation item or a meal preparation action associated with the state of the meal preparation area. The method further includes the processing device determining, based on the image data, a performance metric associated with the meal preparation item or the meal preparation item or the meal preparation action. The method further includes the processing device causing a notification corresponding to the performance metric to be displayed on a graphical user interface (GUI).

The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include receiving, by a processing device, image data having one or more image frames indicative of a state of a meal preparation area. The method may further include, determining, based on the image data, a first feature characterization of a first meal preparation item associated with the state of the meal preparation area. The method may further include determining that the first feature characterization does not meet object classification criteria for a set of object classifications. The method may further include causing a notification indicating the first meal preparation item and one of an object classification or a classification status corresponding to the first meal preparation item on a graphical user interface (GUI).

A method for automatically updating electronic shopping lists of customers of retail stores may include accessing an electronic shopping list of a customer of a retail store, the electronic shopping list including at least one product associated with a shopping order; receiving image data from a plurality of image sensors mounted in the retail store; analyzing the image data to predict an inventory shortage of the at least one product included on the electronic shopping list, wherein the predicted inventory shortage is expected to occur prior to fulfillment of the shopping order; and automatically updating the electronic shopping list based on the predicted inventory shortage of the at least one product.

In order to realize an image processing device capable of obtaining a high-accuracy region division result while suppressing a processing load, an image processing device includes an object detection unit configured to detect an object from image data, a region setting unit configured to set a target region in which a category is assigned to pixels in accordance with a detection result of the object detection unit, and a category assignment unit configured to assign the category to the pixels belonging to the category of the target region which is set by the region setting unit.

Digital signal representations of sub-images are obtained by applying an optimization process wherein a sum is minimized, the sum having a first term representing a measure of the consistency of the sum of a digital representations of sub-images with said radiographic image and wherein the second term is a sum of cost functions each describing the type of one of said sub-images.

Methods of segmenting an abdominal image, computer apparatuses and storage mediums. The method includes acquiring an abdominal image to be examined; and classifying pixels in the abdominal image to be examined based on a trained full convolution neural network, and determining a segmented image corresponding to the abdominal image to be examined, wherein the trained full convolution neural network is trained and determined based on a first training set and a second training set, the first training set includes first sample abdominal images and pixel classification label images corresponding to the first sample abdominal images, the second training set includes second sample abdominal images and the number of pixels of second sample abdominal images correspondingly belong to each class. Through the methods herein, the accuracy of the segmentation can be improved.

An artificial intelligence neural network apparatus, comprising: a labeled learning database having data of a feature vector composed of N elements; a first feature vector image converter configured to visualize the data in the learning database to form an imaged learning feature vector image database; a deep-learned artificial intelligence neural network configured to use a learning feature vector image in the learning feature vector image database to perform an image classification operation; an inputter configured to receive a test image, and generate test data based on the feature vector; and a second feature vector image converter configured to visualize the test data and convert the visualized test data into a test feature vector image. The deep-learned artificial intelligence neural network is configured to determine a class of the test feature vector image.

A method for addressing a shopper's eligibility for frictionless checkout may include identifying at least one shopper in a retail store designated as not eligible for frictionless checkout; in response to the identification of the at least one shopper designated as not eligible for frictionless checkout, automatically identifying an ineligibility condition associated with the at least one shopper's designation as not eligible for frictionless checkout; determining one or more actions for resolving the ineligibility condition; causing implementation of the one or more actions for resolving the ineligibility condition; receiving an indication of successful completion of the one or more actions; and in response to receipt of the indication of successful completion of the one more actions, generating a status indicator indicating that the at least one shopper is eligible for frictionless checkout and storing the generated status indicator in a memory.

A method for using an electronic shopping list to resolve ambiguity associated with a selected product may include accessing an electronic shopping list associated with a customer of a retail store; receiving image data captured using one or more image sensors in the retail store; analyzing the image data to detect a product selection event involving a shopper; identifying a product associated with the detected product selection event based on analysis of the image data and further based on the electronic shopping list; and in response to identification of the product, updating a virtual shopping cart associated with the shopper.

A method for automatically updating electronic shopping lists of customers of retail stores may include accessing an electronic shopping list of a customer of a retail store, the electronic shopping list including at least one product associated with a shopping order; receiving image data from a plurality of image sensors mounted in the retail store; analyzing the image data to predict an inventory shortage of the at least one product included on the electronic shopping list, wherein the predicted inventory shortage is expected to occur prior to fulfillment of the shopping order; and automatically updating the electronic shopping list based on the predicted inventory shortage of the at least one product.