Systems and methods are disclosed for image-based object detection and classification. For example, methods may include accessing an image from an image sensor; applying a convolutional neural network to the image to obtain localization data to detect an object depicted in the image and to obtain classification data to classify the object, in which the convolutional neural network has been trained in part using training images with associated localization labels and classification labels and has been trained in part using training images with associated classification labels that lack localization labels; annotating the image based on the localization data and the classification data to obtain an annotated image; and storing, displaying, or transmitting the annotated image.

The present invention provides a processing apparatus (10) including an inter-face-image-group determination unit (11) that determines whether a similarity score between a first representative face image in a first face image group and a second representative face image in a second face image group satisfies a first condition, an intra-face-image-group determination unit (12) that determines, for each of the first face image group and the second face image group, whether a second condition defining a relationship between the representative face image and another image in the face image group is satisfied, based on a similarity score between the first representative face image and each of other face images in the first face image group and a similarity score between the second representative face image and each of other face images in the second face image group, and a processing unit (13) that associates a same person identifier (ID) with a plurality of face images included in the first face image group and the second face image group when it is determined that the first condition is satisfied and that the first face image group and the second face image group each satisfy the second condition.

An embodiment includes determining an experiential state of a first user participating in a mixed-reality experience. The embodiment also includes creating a first driver model that maps a relationship between the experiential state of the first user and a parameter of the mixed-reality experience. The embodiment also includes aggregating the first driver model with a plurality of driver models associated with experiential states and parameters of respective other users. The embodiment also includes creating a first cohort experience model using the aggregated driver models. The embodiment also includes deriving a first cohort experience parameter for the first cohort experience model. The embodiment also includes initiating an automated remedial action for participants in the mixed-reality system being associated with the first cohort experience model and the first cohort experience parameter.

A computer-implemented system for generating a layout plan includes a memory and a processor coupled to the memory. The processor is configured to obtain an object or a map, input the obtained object or the obtained map to a pre-trained deep neural network (DNN) model, and output the layout plan as an action suggestion based on an output result the pre-trained DNN model, where the output layout plan is an optimal layout plan based on the weight of the DNN model.

A method is proposed for managing a tree-based learner as following. In an initialisation phase: training the tree-based learner with a first set of specimen data to process input data with an active model chain comprising at least a root model to generate an output, each member of the first set of specimen data having a value of each of a first set of features; in an automated reconfiguration phase: receiving a new set of specimen data; performing a comparison between the new set of specimen data and the first set of specimen data; responding to the executed comparison indicating new values of a feature in the new set of specimen data, by reconfiguring the tree-based learner by the addition of a new sub-model to the active model chain, the new sub-model being trained to process at least the new values of the feature.

Decoding random forest problem solving through node labeling and subtree distributions. Random forests, like any other type of machine learning algorithm, are designed and configured to solve classification, regression, and/or prediction problems. Solutions (or outputs) provided by random forests, given inputs in the form of values for a set of features, may sometimes be inaccurate, unexpected, or undesirable. Understanding or decoding how a random forest solves a given problem may be a way to correct or improve the random forest. The disclosed method, accordingly, proposes decoding random forest problem solving through the identification of subtrees (by way of node labeling) amongst a random forest, as well as the frequencies that these subtrees appear (or distributions thereof) throughout the random forest.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data elements. In one aspect, a method includes determining a respective linkage value for each of multiple cluster pairs, where each cluster pair includes a respective first cluster and a respective second cluster. Determining a linkage value for a cluster pair includes determining a set of pairwise similarity values for the cluster pair. Each pairwise similarity value defines a similarity measure between: (i) a particular data element from the first cluster of the cluster pair, and (ii) a given data element from the second cluster of the cluster pair. The linkage value for the cluster pair is assigned as a given percentile of the set of pairwise similarity values, wherein the given percentile is greater than 0 and less than 100. A cluster pair is merged based on the linkage values of the cluster pairs.

Computer-implemented methods and systems are provided for interpreting a whole-slide image of a tissue specimen. Such a method includes detecting biological entities in a region of interest independently at each magnification level in a whole-slide image. An entity graph is generated for each magnification level, the entity graph comprising nodes, representing respective biological entities detected at that magnification level, interconnected by edges representing interactions between entities. The method also includes, for each region of interest, generating a hierarchical graph, defining a hierarchy of the entity graphs for that region, in which nodes of different entity graphs are interconnected by hierarchical edges representing hierarchical relations between nodes of the entity graphs. The method further comprises supplying the hierarchical graph for the region to an AI system to produce result data corresponding to a medical evaluation of the tissue specimen represented thereby, and outputting the result data for the tissue specimen.

An image processing apparatus, an image processing method, and an image processing program capable of creating an image product by extracting an image having a theme matching an interest and taste of a user are provided. A plurality of images are classified into a plurality of image groups using feature amounts of the images such that images having a similarity greater than or equal to a threshold are grouped. An image is extracted from the plurality of classified image groups. An album is created using the extracted image.

A framework that includes a feature extractor and a cluster component for clustering is described herein. The framework supports (1) offline image-based unsupervised clustering that replaces time-consuming manual gating; (2) online image-based single cell sorting. During training, one or multiple cell image datasets with or without ground truth are used to train feature extractor, which is based on a neural network including several convolutional layers. Once trained, the feature extractor is used to extract features of cell images for unsupervised cell clustering and sorting. In addition, additional datasets may be used to further refine the feature extractor after it has been trained.