Disclosed are a method and a system of recommending an accommodation for tourists using multi-criteria decision making (MCDM) and augmented reality. A method of recommending an accommodation for tourists using multi-criteria decision making and augmented reality, which is performed by a server device includes: selecting a recommendation target accommodation based on a current location of a user; selecting a plurality of recommended accommodations by MCDM based on user information including pre-registered preference information among the recommendation target accommodations; and providing an augmented reality interface displaying information on a recommended accommodation to a user terminal.

A method for determining whether shoppers are eligible for frictionless checkout is disclosed. The method may include obtaining image data captured using a plurality of image sensors positioned in a retail store; analyzing the image data to identify at least one shopper at one or more locations of the retail store; detecting, based on the analysis of the image data, at least one product interaction event associated with an action of the at least one shopper at the one or more locations of the retail store; based on the detected at least one product interaction event, determining whether the at least one shopper is eligible for frictionless checkout; and in response to a determination that the at least one shopper is ineligible for frictionless checkout, causing delivery of an indicator that the at least one shopper is ineligible for frictionless checkout.

A control method for a robot cleaner includes acquiring a plurality of images of surroundings during travel of the robot cleaner in a cleaning area, estimating a plurality of room-specific feature distributions according to a rule defined for each of a plurality of rooms, based on the images acquired while acquiring the plurality of images, acquiring an image of surroundings at a current position of the robot cleaner, obtaining a comparison reference group including a plurality of room feature distributions by applying the rule for each of the plurality of rooms to the image acquired while acquiring the image at the current position, comparing the obtained comparison reference group with the estimated room-specific feature distributions, and determining a room from the plurality of rooms having the robot cleaner currently located therein.

Example implementations include a method, apparatus and computer-readable medium for configuring profiles for a camera, comprising receiving video from the camera. The implementations further include classifying a first scene of the first video stream. Additionally, the implementations further include determining a first metadata for the first scene. Additionally, the implementations further include selecting a first profile for the camera based on the first metadata, wherein the first profile comprises one or more configuration parameters, wherein values of each of the one or more configuration parameters of the first profile are based on the first metadata. Additionally, the implementations further include configuring the camera with the first profile.

This disclosure relates generally to a system and method for monitoring and quality evaluation of perishable food items in quantitative terms. Current technology provides limited capability for controlling environmental conditions surrounding the food items in real-time or any quantitative measurement for the degree of freshness of the perishable food items. The disclosed systems and methods facilitate in quantitative determination of freshness of food items by utilizing sensor data and visual data obtained by monitoring the food item. In an embodiment, the system utilizes a pre-trained CNN model and a RNN model, where the pertained CNN model is further fine-tined while training the RNN model to provide robust quality monitoring of the food items. In another embodiment, a rate kinetic based model is utilized for determining reaction rate order of the food item at a particular post-harvest stage of the food item so as to determine the remaining shelf life thereof.

An image classifier comprises a first classifier and a second classifier. The first classifier comprises L individual classifiers, which are trained at different, respective image resolutions from a first full-resolution level to a lowest-resolution level. Outputs of the first set of classifiers are used to train the second classifier at the full-resolution level. Accordingly, the second classifier exploits contextual information at multiple different image resolutions. The classifiers may be trained to optimize a joint posterior probability at multiple resolutions.

In a point cloud data set represented by an image, a first object can be caused to be distinguished from a second object. Current positions of points in the point cloud data set can be arguments for functions of a set of functions. The set of functions can include attraction functions for the points and repulsion functions for the points. Results of the set of functions can be calculated. Based on the results, at least some of the points can be caused to move from the current positions to new positions. In the point cloud data set represented by the image with the at least some of the points being at the new positions, the first object can be distinguished from the second object.

An information processing apparatus includes a processor configured to: set a rule for specifying a region in which a value of an item of an attribute assigned to a form shown by an image is shown in the image for each item, from a predetermined first rule, a second rule indicating an arrangement of the region for an element included in the form, which is input by a user, and a third rule indicating coordinates of the region in the image, which are input by the user; and extract the value of the item shown in the region specified by using the set rule.

Examples disclosed herein relate to determining a response based on hierarchical models. In one implementation, a processor applies a first model to an image of an environment to select a second model. The processor applies the selected second model to the image and creates an environmental description representation based on the output of the second model. The processor determines a response based on the environmental description information.

A class determination system includes a memory and a processor to execute classifying image data of an object to be inspected into one of a predetermined number of classes; extracting, in association with a classification target, a feature value by processing the image data classified by the classifying; determining whether the feature value of the image data of the object is included in a distribution region of the classification target into which the image data of the object is classified, from among distribution regions of feature values of image data items whose classification targets are known, where in each distribution region, a feature value space is defined for a corresponding classification target; and outputting, when the feature value of the image data of the object is determined not included in the distribution region, a determination result that the image data of the object belongs to a new class.