Abstract: Systems, methods, and apparatuses described herein may provide image processing, including displaying, by a mobile device, an image of an object located perpendicular to a reference object, calculating, based on at least one depth measurement determined using a depth sensor in the mobile device, the predicted height of the mobile device when the image was captured, calculating scale data for the image based on the predicted height, determining a reference line identifying the location of the object and the reference object in the image, segmenting pixels in the object in the image from pixels in the image outside the object, measuring the object based on the pixels in the object and the scale data, and generating model data comprising the object, the scale data, and the measurements.

Abstract: Systems, methods, and apparatuses described herein may provide image processing, including displaying, by a mobile device, an image of an object located perpendicular to a reference object, calculating, based on at least one depth measurement determined using a depth sensor in the mobile device, the predicted height of the mobile device when the image was captured, calculating scale data for the image based on the predicted height, determining a reference line identifying the location of the object and the reference object in the image, segmenting pixels in the object in the image from pixels in the image outside the object, measuring the object based on the pixels in the object and the scale data, and generating model data comprising the object, the scale data, and the measurements.

Abstract: Systems, methods, and apparatuses described herein may provide image processing, including displaying, by a mobile device, an image of an object located perpendicular to a reference object, calculating, based on at least one depth measurement determined using a depth sensor in the mobile device, the predicted height of the mobile device when the image was captured, calculating scale data for the image based on the predicted height, determining a reference line identifying the location of the object and the reference object in the image, segmenting pixels in the object in the image from pixels in the image outside the object, measuring the object based on the pixels in the object and the scale data, and generating model data comprising the object, the scale data, and the measurements.

Abstract: Systems, methods, and apparatuses described herein may provide image processing, including displaying, by a mobile device, an image of an object located perpendicular to a reference object, calculating, based on at least one depth measurement determined using a depth sensor in the mobile device, the predicted height of the mobile device when the image was captured, calculating scale data for the image based on the predicted height, determining a reference line identifying the location of the object and the reference object in the image, segmenting pixels in the object in the image from pixels in the image outside the object, measuring the object based on the pixels in the object and the scale data, and generating model data comprising the object, the scale data, and the measurements.

Abstract: Shopping patterns for a customer with respect to a particular product that is repeatedly purchased by the customer with a given frequency are recognized across multiple communication channels by mining transaction histories for the customer over each of the channels. The shopping patterns are processed on a per-communication channel basis for making recommendations, providing reminders, and/or providing default shopping lists on behalf of the customer.

Abstract: Items of a transaction and available promotions applicable to the transaction are obtained. Constraints are placed on the items and the promotions. A maximization goal is defined for a maximum reward for the transaction. The constraints, goal, and attributes of the items for the transaction are expressed in one or more equations and the equations are solved to find the maximum reward available for the transaction. The maximum reward is identified as an optimal redemption value or optimal reward value for the transaction.

Abstract: Shopping patterns for a customer with respect to a particular product that is repeatedly purchased by the customer with a given frequency are recognized across multiple channels. The shopping patterns are utilized to make suggestions, reminders, and default shopping lists on behalf of the customer.

Abstract: Methods and a system for promotion conflict resolution are provided. During a transaction, which may be associated with a consumer, a variety of available promotions are identified. Permissible permutations (non-conflicting promotion combinations) for the available promotions are dynamically formed, evaluated, and a set of optimal promotions for the transaction is resolved.