Various embodiments of the disclosure relate to an electronic device for providing a recognition result for an external object by using recognition information for an image, similar recognition information related to recognition information, and hierarchy information, and an operating method thereof. The electronic device may include a camera, a display, a memory, and a processor. The processor may be configured to acquire an image including an external object by using the camera, generate recognition information for the external object by using the image, acquire similar recognition information generated together with the recognition information as a recognition result of the external object corresponding to the recognition information before the generating operation, acquire hierarchy information corresponding to the recognition information or the similar recognition information, and provide, by using the display, a recognition result for the external object generated by using at least the recognition information, the similar recognition information, and the hierarchy information.

According to an embodiment, a display control apparatus includes a clustering unit, a sub-clustering unit, and a display control unit. The clustering unit is configured to classify images into a plurality of clusters based on similarity degrees of the images and a first threshold. The sub-clustering unit is configured to further classify images within each of the plurality of clusters into a plurality of sub-clusters based on the similarity degrees and a second threshold that is higher than the first threshold. The display control unit is configured to display, on a display unit, display information including a cluster representative indicative of a representative of images included in a cluster and a sub-cluster representative indicative of a representative of an image included in a sub-cluster.

An image processing apparatus includes processing circuitry configured: to obtain a plurality of images taken so as to include a target site of a subject in temporal phases; and to calculate an index indicating a state of an adhesion at a boundary between a first site of the subject corresponding to a first region and a second site of the subject corresponding to a second region, by using classification information used for classifying each of pixels into one selected from between a first class related to the first region and a second class related to a second region positioned adjacent to the first region in a predetermined direction, on a basis of mobility information among the images in the temporal phases with respect to the pixels in the images that are arranged in the predetermined direction across the boundary between the first region and the second region of the images.

A method performed by an apparatus is described. The method includes receiving a first set of object data corresponding to a first journey. The method also includes receiving a second set of object data corresponding to a second journey. The method further includes determining a similarity metric between the first set of object data and the second set of object data. The similarity metric indicates a distance between the first set of object data and the second set of object data for at least one object. The method additionally includes clustering the first set of object data and the second set of object data for the at least one object based on the similarity metric to produce at least one object cluster. The method also includes producing map data based on the at least one object cluster.

Systems and methods for performing quantitative histopathology analysis for determining tissue potency are disclosed. According to some embodiments, a method training a tissue classifier is provided. According to the method, training the tissue classifier includes generating feature fingerprints of detected nuclei within slide images in a control library and clustering the slide images based on their corresponding feature fingerprints. According to some embodiments, a method for utilizing the trained tissue classifier is provided. According to the method, the trained tissue classifier determines whether tissue in an unknown slide image corresponds to slide images clustered during the training of the tissue classifier.

Methods and systems for characterizing tissue of a subject include acquiring and receiving data for a plurality of time series of fluorescence images, identifying one or more attributes of the data relevant to a clinical characterization of the tissue, and categorizing the data into clusters based on the attributes such that the data in the same cluster are more similar to each other than the data in different clusters, wherein the clusters characterize the tissue. The methods and systems further include receiving data for a subject time series of fluorescence images, associating a respective cluster with each of a plurality of subregions in the subject time series of fluorescence images, and generating a subject spatial map based on the clusters for the plurality of subregions in the subject time series of fluorescence images. The generated spatial maps may then be used as input for tissue diagnostics using supervised machine learning.

Methods and systems for characterizing tissue of a subject include acquiring and receiving data for a plurality of time series of fluorescence images, identifying one or more attributes of the data relevant to a clinical characterization of the tissue, and categorizing the data into clusters based on the attributes such that the data in the same cluster are more similar to each other than the data in different clusters, wherein the clusters characterize the tissue. The methods and systems further include receiving data for a subject time series of fluorescence images, associating a respective cluster with each of a plurality of subregions in the subject time series of fluorescence images, and generating a subject spatial map based on the clusters for the plurality of subregions in the subject time series of fluorescence images. The generated spatial maps may then be used as input for tissue diagnostics using supervised machine learning.

A method for operating a data processing system and computer readable medium causing a data processing system to execute that method are disclosed. The method includes causing the data processing system to receive a plurality of first EDSs classified into a plurality of first clusters and a first RDS for each of the plurality of first clusters and displaying, a first display for each of the first clusters and a RDS for each of the first clusters. The data processing system receives information from a user specifying one or more of the first clusters to be further clustered to arrive at a specified number of second clusters into which the specified one or more first clusters are to be classified, and performing a second clustering on the selected clusters. The method also includes displaying a second display that includes a plurality of second EDSs, classified into the second clusters.

An electronic device and a method for controlling thereof are provided. A method for controlling an electronic device according to the disclosure includes obtaining a plurality of images for performing clustering, obtaining a plurality of target areas corresponding to each of the plurality of images, obtaining a plurality of feature vectors corresponding to the plurality of target areas, obtaining a plurality of central nodes corresponding to the plurality of feature vectors, obtaining neighbor nodes associated with each of the plurality of central nodes, obtaining a subgraph based on the plurality of central nodes and the neighbor nodes, identifying the connection probabilities between the plurality of central nodes of the subgraph and the neighbor nodes of each of the plurality of central nodes based on a graph convolutional network, and clustering the plurality of target areas based on the identified connection probabilities.

An approach is provided for large scale vehicle routing. The approach involves, for example, receiving a plurality of plans, wherein a plan of the plurality of plans assigns a vehicle, a driver of the vehicle, or a combination thereof a set of rides to traverse. The approach also involves clustering the plurality of plans into one or more clusters based on a proximity measure. The proximity measure indicates a proximity of a first plan of the plurality of plans to a second plan of a plurality of plans. The approach further involves, for each cluster of the one or more clusters, separately computing a solution to a multiple vehicle routing problem for the set of rides in said each cluster.