An image processing system includes: a memory; and a processor coupled to the memory and configured to: generate information that indicates a feature portion that affects image recognition processing, by executing image recognition processing on first image data acquired at a first time; predict information that indicates the feature portion at a second time after the first time, based on the information that indicates the feature portion at the first time; and encode second image data acquired at the second time, by using a compression rate based on the predicted information that indicates the feature portion.

An image processing system includes: a memory; and a processor coupled to the memory and configured to: generate information that indicates a feature portion that affects image recognition processing, by executing image recognition processing on first image data acquired at a first time; predict information that indicates the feature portion at a second time after the first time, based on the information that indicates the feature portion at the first time; and encode second image data acquired at the second time, by using a compression rate based on the predicted information that indicates the feature portion.

An image processing and model training methods, an electronic device, and a storage medium are provided, and relate to the technical field of artificial intelligence, and in particular to the technical fields of computer vision and deep learning, which can be specifically applied to smart cities and intelligent cloud scenes. The image processing method includes: obtaining at least one first feature map of an image to be processed, wherein feature data of a target pixel in the first feature map is generated according to the target pixel and another pixel within a set range around the target pixel; determining a classification to which the target pixel belongs according to the feature data of the target pixel; and determining a target object corresponding to the target pixel and association information of the target object according to the classification to which the target pixel belongs.

An image processing and model training methods, an electronic device, and a storage medium are provided, and relate to the technical field of artificial intelligence, and in particular to the technical fields of computer vision and deep learning, which can be specifically applied to smart cities and intelligent cloud scenes. The image processing method includes: obtaining at least one first feature map of an image to be processed, wherein feature data of a target pixel in the first feature map is generated according to the target pixel and another pixel within a set range around the target pixel; determining a classification to which the target pixel belongs according to the feature data of the target pixel; and determining a target object corresponding to the target pixel and association information of the target object according to the classification to which the target pixel belongs.

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a deep visual fingerprinting model with parameters learned from robust contrastive learning to identify matching digital images and image provenance information. For example, the disclosed systems utilize an efficient learning procedure that leverages training on bounded adversarial examples to more accurately identify digital images (including adversarial images) with a small computational overhead. To illustrate, the disclosed systems utilize a first objective function that iteratively identifies augmentations to increase contrastive loss. Moreover, the disclosed systems utilize a second objective function that iteratively learns parameters of a deep visual fingerprinting model to reduce the contrastive loss. With these learned parameters, the disclosed systems utilize the deep visual fingerprinting model to generate visual fingerprints for digital images, retrieve and match digital images, and provide digital image provenance information.

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a deep visual fingerprinting model with parameters learned from robust contrastive learning to identify matching digital images and image provenance information. For example, the disclosed systems utilize an efficient learning procedure that leverages training on bounded adversarial examples to more accurately identify digital images (including adversarial images) with a small computational overhead. To illustrate, the disclosed systems utilize a first objective function that iteratively identifies augmentations to increase contrastive loss. Moreover, the disclosed systems utilize a second objective function that iteratively learns parameters of a deep visual fingerprinting model to reduce the contrastive loss. With these learned parameters, the disclosed systems utilize the deep visual fingerprinting model to generate visual fingerprints for digital images, retrieve and match digital images, and provide digital image provenance information.

A medical image processing device includes an image acquisition unit that acquires a medical video image, a brightness analysis unit that analyzes brightness information of each of a plurality of specific medical images within a specific time range among a plurality of medical images constituting the medical video image to output brightness analysis information, and an image selection unit that selects a training medical image to be used for machine learning from among the plurality of specific medical images using the brightness analysis information.

A medical image processing device includes an image acquisition unit that acquires a medical video image, a brightness analysis unit that analyzes brightness information of each of a plurality of specific medical images within a specific time range among a plurality of medical images constituting the medical video image to output brightness analysis information, and an image selection unit that selects a training medical image to be used for machine learning from among the plurality of specific medical images using the brightness analysis information.

Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.