A method for image segmentation includes (a) clustering, based upon k-means clustering, pixels of an image into first clusters, (b) outputting a cluster map of the first clusters (c) re-clustering the pixels into a new plurality of non-disjoint pixel-clusters, and (d) classifying the non-disjoint pixel-clusters in categories, according to a user-indicated classification. Another method for image segmentation includes (a) forming a graph with each node of the graph corresponding to a first respective non-disjoint pixel-cluster of the image and connected to each terminal of the graph and to all other nodes corresponding to other respective non-disjoint pixel-clusters that, in the image, are within a neighborhood of the first respective non-disjoint pixel-cluster, (b) setting weights of connections of the graph according to a user-indicated classification in categories respectively associated with the terminals, and (c) segmenting the image into the categories by cutting the graph based upon the weights.

Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

The disclosure relates to tools and methods for creating synthetic images with photo-realistic images. The disclosed face generation technology focuses on photo-realistic results by leveraging analysis of a pool of pre-selected images based on a user selection and preferences. The tools and methods as described herein include limiting the pool of pre-selected images by one or more criteria, including, for example, but not limited to gender, age, skin color, expression, etc. The pre-selection of a more curated pool of images allows a user to include a desired set of criteria and specifications that the user would want in a generated synthetic image or images.

Systems, computer-implemented methods, and computer program products that can facilitate a classical and quantum ensemble artificial intelligence model are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an ensemble component that generates an ensemble artificial intelligence model comprising a classical artificial intelligence model and a quantum artificial intelligence model. The computer executable components can further comprise a score component that computes probability scores of a dataset based on the ensemble artificial intelligence model.

An image processing apparatus obtains a read image of a document including a handwritten character, generates a first image formed by pixels of the handwritten character by extracting the pixels of the handwritten character from pixels of the read image using a first learning model for extracting the pixels of the handwritten character, estimates a handwriting area including the handwritten character using a second learning model for estimating the handwriting area, and performs handwriting OCR processing based on the generated first image and the estimated handwriting area.

A method includes accessing, from a data store, at least one predefined data classification for asset data associated with multiple assets in an industrial process control system, wherein the at least one predefined data classification is associated with one or more first policies, wherein the data store stores a plurality of data classifications for asset data. The method also includes receiving user input of a customization to the at least one predefined data classification to generate at least one customized data classification associated with one or more second policies. The method further includes storing the at least one customized data classification in the data store. The method also includes collecting asset data from at least one of the multiple assets. The method further includes processing the collected asset data according to the one or more second policies associated with the at least one customized data classification.

A method includes accessing, from a data store, at least one predefined data classification for asset data associated with multiple assets in an industrial process control system, wherein the at least one predefined data classification is associated with one or more first policies, wherein the data store stores a plurality of data classifications for asset data. The method also includes receiving user input of a customization to the at least one predefined data classification to generate at least one customized data classification associated with one or more second policies. The method further includes storing the at least one customized data classification in the data store. The method also includes collecting asset data from at least one of the multiple assets. The method further includes processing the collected asset data according to the one or more second policies associated with the at least one customized data classification.

Provided are an information processing apparatus, an information processing method, and a program capable of accumulating appropriate relearning data. An information processing apparatus includes an input unit that inputs input data to a learned model acquired in advance through machine learning using learning data, an acquisition unit that acquires output data output from the learned model through the input using the input unit, a reception unit that receives correction performed by a user for the output data acquired by the acquisition unit, and a storage controller that performs control for storing, as relearning data of the learned model, the input data and the output data that reflects the correction received by the reception unit in a storage unit in a case where a value indicating a correction amount acquired by performing the correction for the output data is equal to or greater than a threshold value.

Systems, devices, methods, and computer-readable media for evaluation and visualization of machine learning data drift. A method can include receiving a series of data indicating accuracy and confidence associated with classification of respective batches of input samples, and dynamically displaying, on the GUI, a concurrent plot of the accuracy and confidence as the series of data are received.

In an approach for creating synthetic visual inspection data sets for training an artificial intelligence computer vision deep learning model utilizing augmented reality, a processor enables a user to capture a plurality of images of an anchor object using a camera on a user computing device. A processor receives the plurality of images of the anchor object from the user. A processor generates a baseline model of an anchor object. A processor generates a training data set. A processor trains the baseline model of the anchor object. A processor creates a trained Artificial Intelligence (AI) computer vision deep learning model. A processor enables the user to interact with the trained AI computer vision deep learning model in an access mode.