Methods of operating a refrigerator appliance are provided. The refrigerator appliance includes a cabinet defining a food storage chamber with a drawer slidably mounted within the food storage chamber. The refrigerator appliance also includes a sensor operable to detect an atmospheric condition within the food storage chamber of the drawer and a camera assembly positioned and configured for monitoring the drawer. The methods generally include obtaining an image using the camera assembly and analyzing the image to identify a first food item and a second food item in the food storage chamber of the drawer.

Methods of operating a refrigerator appliance are provided. The refrigerator appliance includes a cabinet defining a food storage chamber with a drawer slidably mounted within the food storage chamber. The refrigerator appliance also includes a sensor operable to detect an atmospheric condition within the food storage chamber of the drawer and a camera assembly positioned and configured for monitoring the drawer. The methods generally include obtaining an image using the camera assembly and analyzing the image to identify a first food item and a second food item in the food storage chamber of the drawer.

A computer system is provided comprising a classification model management server computer configured, by instructions, to: receive a new image from a user device; apply a first digital model to first regions within the new image for classifying each of the first regions into a particular class; apply a second digital model to second regions within the new image for classifying each of the second regions into a particular class; and transmit classification data related to the class of the first regions and the class of the second regions to the user device. In connection therewith, the second regions each generally correspond to a combination of multiple first regions.

A computer system is provided comprising a classification model management server computer configured, by instructions, to: receive a new image from a user device; apply a first digital model to first regions within the new image for classifying each of the first regions into a particular class; apply a second digital model to second regions within the new image for classifying each of the second regions into a particular class; and transmit classification data related to the class of the first regions and the class of the second regions to the user device. In connection therewith, the second regions each generally correspond to a combination of multiple first regions.

An automatic control system for a cooking appliance monitors and adjusts a cooking operation on the cooking appliance. The automatic control system includes at least one control knob assembly, an image capturing device, and a controller operably coupled to the at least one control knob assembly and the image capturing device. The controller is configured to perform a series of operations, including receiving a desired temperature of a food item; capturing a first image of the food item; analyzing, by one or more computing devices using a machine learning image recognition model, the first image to determine one or more features of the food item; generating an input state of the food item based on the first image analysis; and determining an output action via a reinforcement learning system.

An automatic control system for a cooking appliance monitors and adjusts a cooking operation on the cooking appliance. The automatic control system includes at least one control knob assembly, an image capturing device, and a controller operably coupled to the at least one control knob assembly and the image capturing device. The controller is configured to perform a series of operations, including receiving a desired temperature of a food item; capturing a first image of the food item; analyzing, by one or more computing devices using a machine learning image recognition model, the first image to determine one or more features of the food item; generating an input state of the food item based on the first image analysis; and determining an output action via a reinforcement learning system.

In an embodiment, a method for estimating a composition of food includes: receiving a first three-dimensional (3D) image; identifying food in the first 3D image; determining a volume of the identified food based on the first 3D image; and estimating a composition of the identified food using a millimeter-wave radar.

An information processing system includes an imaging unit that generates an image signal by imaging and an information processing device. The information processing device performs at least any one of plural kinds of image processing on a taken image corresponding to the image signal. The information processing device specifies an object corresponding to a partial image included in the taken image on the basis of a state of the object corresponding to the partial image included in the taken image or a degree of reliability given to a processing result of the performed image processing.

The techniques described herein relate to computerized methods and apparatus for grouping images of objects based on semantic and visual information associated with the objects. The techniques described herein further relate to computerized methods and apparatus for training a machine learning model for object recognition.

The techniques described herein relate to computerized methods and apparatus for grouping images of objects based on semantic and visual information associated with the objects. The techniques described herein further relate to computerized methods and apparatus for training a machine learning model for object recognition.