A training device may include one or more processors configured to generate, using a data augmentation model, augmented sensor data for sensor data, the sensor data provided by a plurality of sensors, wherein the augmented sensor data comprise error states of one or more sensors of the plurality of sensors providing the sensor data, and to train an object detection model based on the augmented sensor data.

Systems and methods for applying an ensemble of segmentations to microscopy images of a tissue sample to determine if the tissue sample is representative of cancerous tissue. The ensemble of segmentations is applied to a plurality of greyscale or color microscopy images to generate a final image level segmentation and a final blob level segmentation. The final image level segmentation and final blob level segmentation are used to calculate a mean nuclear volume to determine if the tissue sample is representative of cancerous tissue.

Type prediction method, apparatus and electronic device for recognizing an object in an image are disclosed. The method may include processing an image to be processed using a full image recognition technique to obtain a first type prediction result of an object in the image to be processed; processing a subject area of the image to be processed using a feature recognition technique to obtain a second type prediction result of an object of the subject area; determining whether the first type prediction result matches the second type prediction result; if the first type prediction result matches the second type prediction result, determining a type of the object of the image to be processed to be the first type prediction result or the second type prediction result.

Various embodiments utilize application-based processing parameters to dynamically configure a radar-based detection system based upon an operating context of an associated device. A first application with execution priority on a device dynamically configures the radar-based detection system to emit a radar field suitable for a first operating context associated with the first application. The first application can also dynamically configure processing parameters of the radar-based detection system, such as digital signal processing parameters and machine-learning parameters. In some cases, a second application assumes execution priority over the first application, and dynamically reconfigures the radar-based detection system to emit a radar field suitable to a second operating context associated with the second application. Alternately or additionally, the second application can dynamically reconfigure the processing parameters of the radar-based detection system based upon the second operating context of the second application.

Systems and methods for monitoring a user by a plurality of image sensors are disclosed. The system includes a plurality of image sensors that output content descriptive of one or more visual attributes of the user, where each of the image sensors are positioned at a unique angle facing a user. The system also includes one or more processors and one or more non-transitory memory modules communicatively coupled to the one or more processors. The non-transitory memory modules storing machine-readable instructions that, when executed, cause the one or more processors to receive the content from the plurality of image sensors, and assign respective weights to each of the plurality of image sensors based on the content. The respective weights of the plurality of image sensors are actively re-weighted over a period of time. The processors are also caused to merge the content of the plurality of image sensors together.

The technology described in this document can be embodied in a method for generating an iris identifier. The method includes obtaining a plurality of images of an iris, and generating a binary code for each of the plurality of images of the iris, the binary code including a sequence of bits. The method also includes identifying a first pattern of bits for which bit values and bit-locations are the same across a plurality of the binary codes, generating a first index based on the first pattern of bits, and then storing the first index on a storage device in accordance with a database management system. The first index is linked to biometric information of a different modality for a corresponding user.

A method, apparatus and system for understanding visual content includes determining at least one region proposal for an image, attending at least one symbol of the proposed image region, attending a portion of the proposed image region using information regarding the attended symbol, extracting appearance features of the attended portion of the proposed image region, fusing the appearance features of the attended image region and features of the attended symbol, projecting the fused features into a semantic embedding space having been trained using fused attended appearance features and attended symbol features of images having known descriptive messages, computing a similarity measure between the projected, fused features and fused attended appearance features and attended symbol features embedded in the semantic embedding space having at least one associated descriptive message and predicting a descriptive message for an image associated with the projected, fused features.

An electronic device includes: a camera; a microphone; a display; a memory; and a processor configured to receive an input for activating an intelligent agent service from a user while at least one application is executed, identify context information of the electronic device, control to acquire image information of the user through the camera, based on the identified context information, detect movement of a user's lips included in the acquired image information to recognize a speech of the user, and perform a function corresponding to the recognized speech.

Provided are methods and apparatuses for determining a target object in an image based on an interactive input. A target object determining method acquires first feature information corresponding to an image and second feature information corresponding to an interactive input; and determines a target object corresponding to the interactive input from among objects in the image based on the first feature information and the second feature information.

Systems and methods for classifying blood cells in a blood sample are disclosed. A series of frames of the blood sample as it flows through a field of view of an image capture device are captured and analysed. Advantageously, the disclosed systems and methods combine the availability of morphological cell data with the convenience of a flow-through arrangement. The classification results can be used for estimating cell counts in a blood sample.