Systems and methods are directed to automatically creating customized whiteboard backgrounds. A network system accesses metadata associated with a virtual presentation (e.g., title, topic, tenant identifier). First image data is identified based on first data of the metadata and second image data is identified based on second data of the metadata. Using the first image data and the second image data, the network system generates a plurality of whiteboard backgrounds by combining a first object obtained from the first image data with a second object obtained from the second image data to form each whiteboard background. The network system then causes presentation of a representation of each of the plurality of whiteboard backgrounds on a user interface of a host, who can select one of the representations. In response to receiving a selection, a whiteboard background corresponding to the selected representation is displayed as background on a whiteboard canvas.

A system and method for classifying digital images is presented. The method includes extracting a plurality of descriptive data items of a transaction evidence from a digital image indicating a plurality of purchased items; searching in data source for informative data based on the extracted plurality of descriptive data items, wherein the informative data includes a price; determining a correlated amount for each of at least one of the plurality of descriptive data items, wherein the correlated amount determined for one of the descriptive data items defines a paid price for the descriptive data item; determining, based on at least one expense type classification rule, a primary expense type of the transaction evidence, wherein the at least one expense type classification rule is applied to the plurality of descriptive data items and each of the correlated amount; and classifying the digital image based on the primary expense type.

A system and control method for determining an amount of paper product dispensed from a dispenser or remaining in the dispenser is provided. The paper product is initially loaded in the dispenser as a paper product formation, such as a roll or stack of the product. At defined intervals, a digital image of an aspect of the paper product formation in the dispenser is taken and transmitted to a digital imager processor. A feature of the digital image that changes as the paper product formation decreases in size as the paper product is dispensed is analyzed and compared with a predetermined value of the feature at a predefined size of the paper product formation to determine an amount of the paper product dispensed or remaining in the dispenser.

A method for detecting a static logo of a video, an electronic apparatus and a storage medium. The method includes: calculating a pixel grayscale flag value, an edge gradient flag value and an edge direction flag value; calculating, in a preset neighborhood centered on the pixel at each pixel position of the current video frame, a first local confidence degree of the pixel grayscale flag value, a second local confidence degree of the edge gradient flag value, and a third local confidence degree of the edge direction flag value respectively; calculating a contribution score of each local confidence degree and a total contribution score of each pixel position; and gathering the total contribution score of each pixel position of the current video frame, and determining a static logo in the current video frame according to the total contribution score of each pixel position.

An integrated management server comprising: a data formatting means for receiving sales record information from a plurality of distributor terminals of a plurality of distributors which are selling products to retailers; a data matching means for updating brand master information for universally managing alcoholic beverage brands sold to the retailers across the plurality of distributors based on each of the received sales record information; and a brand Identification means for analyzing image related information received from a user terminal, identifying an alcoholic beverage brand by comparing a feature information corresponding to an alcoholic beverage brand registered in the brand master information, and sending information related to identified alcoholic beverage brand to the user terminal; and the user terminal comprising: a display means for displaying information on the sent alcoholic beverage brand.

A wine label recognition method, a wine information management method and apparatus, a computer device, and a computer-readable storage medium are provided. The method includes: obtaining a wine image, and performing optical character recognition (OCR) on the wine image in a preset OCR manner, to obtain text included in the wine image (S21); performing deep learning recognition on the wine image in a preset deep learning recognition manner, to obtain an image feature included in the wine image (S22); and sifting out a target wine label matching the text and the image feature from a preset wine label database according to the text and the image feature, and using the target wine label as a wine label corresponding to the wine image (S33). Advantages of deep learning and OCR are fully utilized thereby improving accuracy and efficiency of wine label recognition and improving automation efficiency of wine information management.

A system can include one or more processors and one or more non-transitory computer-readable storage media storing computing instructions configured to run on the one or more processors and perform: generating a training dataset for training a neural network detection model; identifying, using the neural network detection model, as trained, the non-compliant content in the synthetic training images; receiving, at the neural network detection model, at least one image; and utilizing the neural network detection model to determine whether the at least one image comprises the non-compliant content. Other embodiments are disclosed herein.

A method of identifying media content presented on a display device includes determining a selected input source providing a video signal to the display device, and then selecting a first set of content identification rules when it is determined that the selected input source is a first input source, and selecting a second set of content identification rules when it is determined that the selected input source is a second input source. The method further comprises applying the selected first set or second set of content identification rules to the video signal in order to generate content identification data for the media content presented on the display device. Application of the content identification rules includes waiting for a trigger event and applying an algorithm to one or more frames of the video signal following the trigger event.

Machine learning based models recognize objects in images. Specific features of the object are extracted from the image using machine learning based models. The specific features extracted from the image assist deep learning based models in identifying subtypes of a type of object. The system recognizes the objects and collections of objects and determines whether the arrangement of objects violates any predetermined policies. For example, a policy may specify relative positions of different types of objects, height above ground at which certain types of objects are placed, or an expected number of certain types of objects in a collection.

A laundry data analysis apparatus based on artificial intelligence according to an embodiment of the present invention includes: a communication unit configured to receive an image including laundry data related to characteristics of laundry from an image acquisition device corresponding to a group including at least one member; and a processor configured to recognize the laundry data from the received image, acquire additional data related to the characteristics of the laundry on the basis of the recognized laundry data, store laundry information including the laundry data and the additional data into a database, and acquire member characteristic information of each of the at least one member from a plurality of laundry information corresponding to the group stored in the database.