A method and system for a feature subset-classifier pair for a classification task. The classification task corresponds to automatically classifying data associated with a subject(s) or object(s) of interest into an appropriate class based on a feature subset selected among a plurality of features extracted from the data and a classifier selected from a set of classifier types. The method proposed includes simultaneously determining the feature subset-classifier pair based on a relax-greedy {feature subset, classifier} approach utilizing sub-greedy search process based on a patience function, wherein the feature subset-classifier pair provides an optimal combination for more accurate classification. The automatic joint selection is time efficient solution, effectively speeding up the classification task.

A sensor device includes an array sensor having a plurality of detection elements arrayed in one or two dimensional manner, a signal processing unit configured to acquire a detection signal by the array sensor and perform signal processing, and a calculation unit. The calculation unit detects an object from the detection signal by the array sensor, and gives an instruction, to the signal processing unit, on region information generated on the basis of the detection of the object as region information regarding the acquisition of the detection signal from the array sensor or the signal processing for the detection signal.

Scene filtering using motion estimation, including identifying, in camera data from an autonomous vehicle, based on motion relative to the autonomous vehicle, one or more pixels; filtering, from the camera data, the one or more pixels; and training, based on the filtered camera data, a neural network.

The present technology analyzes the content of images to create complex representations of the images and then reduces the complexity of these representations into a size that is both suitable for comparison but also contains critical image descriptive aspects. These reduced complexity representations can then be used to efficiently search for similar images. Moreover, the reduced complexity representations are formatted such that they can take advantage of existing text search engines, which are well suited to efficiently searching through a large number of unique results.

A method of detecting an image, an electronic device, and a storage medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision and deep learning technologies, and may be applied to a smart city and an intelligent cloud. The method includes: performing a feature extraction on an image to be detected, so as to obtain a feature map of the image to be detected; generating a prediction box in the feature map according to the feature map; generating a mask for the prediction box according to a key region of a target object; and classifying the prediction box using the mask as a classification enhancement information, so as to obtain a category of the prediction box.

A method of detecting an image, an electronic device, and a storage medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision and deep learning technologies, and may be applied to a smart city and an intelligent cloud. The method includes: performing a feature extraction on an image to be detected, so as to obtain a feature map of the image to be detected; generating a prediction box in the feature map according to the feature map; generating a mask for the prediction box according to a key region of a target object; and classifying the prediction box using the mask as a classification enhancement information, so as to obtain a category of the prediction box.

A computer-implemented method is provided for improving image quality. The method comprises: acquiring, using a medical imaging apparatus, a medical image of a subject, wherein the medical image is acquired with shortened scanning time or reduced amount of tracer dose; applying a deep learning network model to the medical image to generate one or more feature attention maps a medical image of the subject with improved image quality for analysis by a physician.

A computer-implemented method is provided for improving image quality. The method comprises: acquiring, using a medical imaging apparatus, a medical image of a subject, wherein the medical image is acquired with shortened scanning time or reduced amount of tracer dose; applying a deep learning network model to the medical image to generate one or more feature attention maps a medical image of the subject with improved image quality for analysis by a physician.

An electronic device extracts feature data from an input image, calculates one or more class maps from the feature data using a classifier layer, calculates one or more cluster maps from the feature data using a clustering layer, and generates image segmentation data using the one or more class maps and the one or more cluster maps.

An electronic device extracts feature data from an input image, calculates one or more class maps from the feature data using a classifier layer, calculates one or more cluster maps from the feature data using a clustering layer, and generates image segmentation data using the one or more class maps and the one or more cluster maps.