A device includes image generation circuitry and convolutional-neural-network circuitry. The image generation circuitry, in operation, generates a digital image representation of a wafer defect map (WDM). The convolutional-neural-network circuitry, in operation, generates a defect classification associated with the WDM based on: the digital image representation of the WDM and a data-driven model associating WDM images with classes of a defined set of classes of wafer defects and generated using a training data set augmented based on defect pattern orientation types associated with training images.

Systems, apparatuses, and methods include technology that identifies, with a neural network, that a predetermined amount of a first action is completed at a first portion of a plurality of portions. A subset of the plurality of portions collectively represents the first action. The technology generates a first loss based on the predetermined amount of the first action being identified as being completed at the first portion. The technology updates the neural network based on the first loss.

Systems, apparatuses, and methods include technology that identifies, with a neural network, that a predetermined amount of a first action is completed at a first portion of a plurality of portions. A subset of the plurality of portions collectively represents the first action. The technology generates a first loss based on the predetermined amount of the first action being identified as being completed at the first portion. The technology updates the neural network based on the first loss.

Methods, systems, and apparatus for controlling smart devices are described. In one aspect a method includes capturing, by a camera on a user device, a plurality of successive images for display in an application environment of an application executing on the user device, performing an object recognition process on the images, the object recognition process including determining that a plurality of images, each depicting a particular object, are required to perform object recognition on the particular object, and in response to the determination, generating a user interface element that indicates a camera operation to be performed, the camera option capturing two or more images, determining that a user, in response to the user interface element, has caused the indicated camera operation to be performed to capture the two or more images, and in response, determining whether a particular object is positively identified from the plurality of images.

Provided herein are systems and methods for automated identification of volumes of interest in volumetric brain images using artificial intelligence (AI) enhanced imaging to diagnose and treat acute stroke. The methods can include receiving image data of a brain having header data and voxel values that represent an interruption in blood supply of the brain when imaged, extracting the header data from the image data, populating an array of cells with the voxel values, applying a segmenting analysis to the array to generate a segmented array, applying a morphological neighborhood analysis to the segmented array to generate a features relationship array, where the features relationship array includes features of interest in the brain indicative of stroke, identifying three-dimensional (3D) connected volumes of interest in the features relationship array, and generating output, for display at a user device, indicating the identified 3D volumes of interest.

Provided herein are systems and methods for automated identification of volumes of interest in volumetric brain images using artificial intelligence (AI) enhanced imaging to diagnose and treat acute stroke. The methods can include receiving image data of a brain having header data and voxel values that represent an interruption in blood supply of the brain when imaged, extracting the header data from the image data, populating an array of cells with the voxel values, applying a segmenting analysis to the array to generate a segmented array, applying a morphological neighborhood analysis to the segmented array to generate a features relationship array, where the features relationship array includes features of interest in the brain indicative of stroke, identifying three-dimensional (3D) connected volumes of interest in the features relationship array, and generating output, for display at a user device, indicating the identified 3D volumes of interest.

The present disclosure relates to an information processing apparatus, an information processing method, an information processing system, and a program capable of appropriately evaluating an object recognition filter by simpler processing. A generation unit that generates teacher data of a preprocessing filter provided in a preceding stage of the object recognition filter is generated by a cyclic generative adversarial network (Cyclic GAN) that is unsupervised learning. The teacher data generated by the generated generation unit is applied to the object recognition filter, an evaluation image is generated from a difference between object recognition result images, and an evaluation filter that generates an evaluation image from the evaluation image and the teacher data is generated. The evaluation filter is applied to an input image to generate an evaluation image, and the object recognition filter is evaluated by the generated evaluation image. The present disclosure can be applied to an object recognition device.

The present disclosure relates to an information processing apparatus, an information processing method, an information processing system, and a program capable of appropriately evaluating an object recognition filter by simpler processing. A generation unit that generates teacher data of a preprocessing filter provided in a preceding stage of the object recognition filter is generated by a cyclic generative adversarial network (Cyclic GAN) that is unsupervised learning. The teacher data generated by the generated generation unit is applied to the object recognition filter, an evaluation image is generated from a difference between object recognition result images, and an evaluation filter that generates an evaluation image from the evaluation image and the teacher data is generated. The evaluation filter is applied to an input image to generate an evaluation image, and the object recognition filter is evaluated by the generated evaluation image. The present disclosure can be applied to an object recognition device.

This disclose relates to a data processing method and apparatus. The method includes: acquiring a first prediction region in a target image, the first prediction region being a prediction region corresponding to a maximum prediction category probability in N prediction regions in the target image, a prediction category probability being a probability that an object in a prediction region belongs to a prediction object category; determining a coverage region jointly covered by a second prediction region and the first prediction region; the second prediction region being a prediction region other than the first prediction region in the N prediction regions; and determining a target prediction region in the prediction regions based on an area of the coverage region and a similarity associated with the second prediction region, the similarity being for indicating a similarity between an object in the second prediction region and an object in the first prediction region.

This disclose relates to a data processing method and apparatus. The method includes: acquiring a first prediction region in a target image, the first prediction region being a prediction region corresponding to a maximum prediction category probability in N prediction regions in the target image, a prediction category probability being a probability that an object in a prediction region belongs to a prediction object category; determining a coverage region jointly covered by a second prediction region and the first prediction region; the second prediction region being a prediction region other than the first prediction region in the N prediction regions; and determining a target prediction region in the prediction regions based on an area of the coverage region and a similarity associated with the second prediction region, the similarity being for indicating a similarity between an object in the second prediction region and an object in the first prediction region.