Various implementations disclosed herein include devices, systems, and methods that provide measurements of objects based on a location of a surface of the objects. An exemplary process may include obtaining a three-dimensional (3D) representation of a physical environment that was generated based on depth data and light intensity image data, generating a 3D bounding box corresponding to an object in the physical environment based on the 3D representation, determining a class of the object based on the 3D semantic data, determining a location of a surface of the object based on the class of the object, the location determined by identifying a plane within the 3D bounding box having semantics in the 3D semantic data satisfying surface criteria for the object, and providing a measurement of the object, the measurement of the object determined based on the location of the surface of the object.

Various implementations disclosed herein include devices, systems, and methods that obtain a three-dimensional (3D) representation of a physical environment that was generated based on depth data and light intensity image data, generate a 3D bounding box corresponding to an object in the physical environment based on the 3D representation, classify the object based on the 3D bounding box and the 3D semantic data, and display a measurement of the object, where the measurement of the object is determined using one of a plurality of class-specific neural networks selected based on the classifying of the object.

Quality associated with an interpretation of data captured as unstructured data can be determined. Attributes can be identified within the unstructured data automatically. Subsequently, sentiment associated with each of the attributes can be determined based on the unstructured data. Correctness of the unstructured data, and thus the interpretation, can be assessed based on a comparison of the attribute and associated sentiment with structured data. A quality score can be generated that captures the quality of the data interpretation in terms of correctness and as well as results of another analysis including completeness, among others. Comparison of the quality score to a threshold can dictate whether or not the interpretation is subject to further review.

An identification method executed by a computer, the identification method includes receiving a face image; generating each of a plurality of first estimated values regarding an attribute of a face image by using a plurality of estimation models that generates a first estimated value regarding the attribute of the face image from the face image; generating a plurality of pieces of similarity information that indicates a similarity between feature information of the face image and a plurality of pieces of feature information respectively associated with the plurality of estimation models; and generating a second estimated value regarding the attribute of the face image, based on the plurality of first estimated values and the plurality of pieces of similarity information.

An image processing method includes: extracting a candidate modality image to be visualized from a data group of a single or a plurality of modality images; first associating separate modality data referring to an extracted modality image, with the extracted modality image; uniquely determining an image to be visualized, based on the modality image extracted at the extracting and an association result of the first associating; second associating different modality data being not associated at the first associating, with the modality image extracted at the extracting; and displaying the different modality data on the modality image extracted at the extracting.

Systems and methods for determining a target data processing model is provided. The methods may include obtaining a data set including data to be processed by a target data processing model; processing the data set using an evaluation model to obtain an evaluation result for each of a plurality of candidate data processing models, the evaluation model being a trained machine learning model; and determining, from the plurality of candidate data processing models, the selected data processing model based on the evaluation results.

This invention makes it possible to build an image identification model having high accuracy of identification using divided training images into which training images are divided. An image identification system includes: an image dividing unit which divides training images of a first training data set and assigns a label assigned to a training image from which dividing occurs to the divided training images as tentative labels; a texture index computing unit which computes texture indexes for each of the divided training images; a tentative label prediction model building unit which builds a tentative label prediction model to predict tentative labels assigned to the divided training images based on the texture indexes; and a label comparison unit which compares first tentative labels assigned to the divided training images with second tentative labels predicted with respect to the divided training images by the tentative label prediction model and extracts divided training images for which there is discrepancy between the first and second tentative labels as those images for which it is highly necessary to modify tentative labels.

Provided are methods for customized tags for annotating sensor data, which can include receiving sensor data captured during a plurality of sensor data capture sessions, processing the sensor data using a plurality of machine learning models to identify a plurality of capture session collections represented in the sensor data, filtering the sensor data based at least partly on a user-specified category of the plurality of categories of capture session to identify a capture session collection, of the plurality of capture session collections, representing sensor data of one or more sensor data capture sessions that conforms to the user-specified category, and transmitting the sensor data of one or more sensor data capture sessions that conforms to the user-specified category to an end user computing device. Systems and computer program products are also provided.

An electronic device recognizing an object is provided. The electronic device includes a plurality of computing units, a memory, and a processor configured to control at least one of the plurality of computing units such that object information about objects obtained by recognizing the objects existing in a space by using a first recognition model, divide the space into a plurality of subset spaces, based on the object information, determine at least one recognition model, based on characteristic information of each of the subset spaces, assign the determined recognition model to one computing unit, based on characteristic information of each of a plurality of computing units and characteristic information of the determined recognition model, and control the plurality of computing units to perform object recognition by using the determined recognition model and the one computing unit in each of the subset spaces.

Methods, systems, and apparatus, including medium-encoded computer program products, for a secure edge platform that uses image classification machine learning models. An edge platform can include at least one camera and can identify image classification models that generate classification output data from image data generated by the cameras. The edge platform can receive image data generated by the camera, and provide the image data to the models. In response to providing the image data classification models, the edge platform can receive classification output data. In response to receiving the classification output data from the image classification models, the edge platform can generate augmentation data that is associated with the image data, then transmit detection data to a central server platform. The detection data can include (i) the classification output data and (ii) the augmentation data associated with the image data. Data can be made recordable, reportable, searchable, and alarmable.