Abstract: An automated checkout system modifies received images of machine-readable labels to improve the performance of a label detection model that the system uses to decode item identifiers encoded in the machine-readable labels. For example, the automated checkout system may transform subregions of an image of a machine-readable label to adjust for distortions in the image's depiction of the machine-readable label. Similarly, the automated checkout system may identify readable regions within received images of machine-readable labels and apply a label detection model to those readable regions. By modifying received images of machine-readable labels, these techniques improve on existing computer-vision technologies by allowing for the effective decoding of machine-readable labels based on real-world images using relatively clean training data.

Abstract: A smart shopping cart includes internally facing cameras and an integrated scale to identify objects that are placed in the cart. To avoid unnecessary processing of images that are irrelevant, and thereby save battery life, the cart uses the scale to detect when an object is placed in the cart. The cart obtains images from a cache and sends those to an object detection machine learning model. The cart captures and sends a load curve as input to the trained model for object detection. Labeled load data and labeled image data are used by a model training system to train the machine learning model to identify an item when it is added to the shopping cart. The shopping cart also uses weight data and the image data from a timeframe associated with the addition of the item to the cart as inputs.

Abstract: An automated checkout system modifies received images of machine-readable labels to improve the performance of a label detection model that the system uses to decode item identifiers encoded in the machine-readable labels. For example, the automated checkout system may transform subregions of an image of a machine-readable label to adjust for distortions in the image's depiction of the machine-readable label. Similarly, the automated checkout system may identify readable regions within received images of machine-readable labels and apply a label detection model to those readable regions. By modifying received images of machine-readable labels, these techniques improve on existing computer-vision technologies by allowing for the effective decoding of machine-readable labels based on real-world images using relatively clean training data.

Abstract: The present disclosure belongs to the field of biological materials, and particularly relates to a multilayer zirconia ceramic with uniform transition and a method for preparing the same. The specific technical solution of the present disclosure is as follows: a zirconia ceramic with a formula comprising, in percentage by mass, 0-3% of lanthana, 1.5-16% of yttria, 0-2.5% of silicon carbide nano-whiskers, and 0-1.5% of a coloring agent, the balance being zirconia. Correspondingly provided are a multi-layer zirconia ceramic with uniform transition prepared using the formula and a method for preparing the same. By using the method of the present disclosure, multilayer zirconia ceramics with good and uniformly transitioning core properties can be quickly and conveniently prepared, meeting the requirements of patients with dental disorders on the use and esthetics of dentures.