Digital image ordering based on object position and aesthetics is leveraged in a digital medium environment. According to various implementations, an image analysis system is implemented to identify visual objects in digital images and determine aesthetics attributes of the digital images. The digital images can then be arranged in way that prioritizes digital images that include relevant visual objects and that exhibit optimum visual aesthetics.

Digital image ordering based on object position and aesthetics is leveraged in a digital medium environment. According to various implementations, an image analysis system is implemented to identify visual objects in digital images and determine aesthetics attributes of the digital images. The digital images can then be arranged in way that prioritizes digital images that include relevant visual objects and that exhibit optimum visual aesthetics.

An image processing system includes a processor, the processor performing processing, based on association information of an association between a biological image captured under a first imaging condition and a biological image captured under a second imaging condition, of outputting a prediction image corresponding to an image in which an object captured in an input image is to be captured under the second imaging condition. The association information is indicative of a trained model obtained through machine learning of a relationship between a first training image captured under the first imaging condition and a second training image captured under the second imaging condition. The processor is capable of outputting a plurality of different kinds of prediction images based on a plurality of trained models and the input image, and performs processing, based on a given condition, of selecting the prediction image to be output among a plurality of prediction images.

An image processing system includes a processor, the processor performing processing, based on association information of an association between a biological image captured under a first imaging condition and a biological image captured under a second imaging condition, of outputting a prediction image corresponding to an image in which an object captured in an input image is to be captured under the second imaging condition. The association information is indicative of a trained model obtained through machine learning of a relationship between a first training image captured under the first imaging condition and a second training image captured under the second imaging condition. The processor is capable of outputting a plurality of different kinds of prediction images based on a plurality of trained models and the input image, and performs processing, based on a given condition, of selecting the prediction image to be output among a plurality of prediction images.

The present disclosure provides a method and device for detecting an image category. The method includes: acquiring a sample data set including a plurality of sample images labeled with a category, the sample data set including a training data set and a verification data set; training a deep learning model using the training data set to obtain, according to different numbers of training rounds, at least two trained models; testing the at least two trained models using the verification data set to generate a verification test result; generating, based on the verification test result, a verification test index; determining, according to the verification test index, a target model from the at least two trained models; and predict a to-be-tested image of the target object using the target model to obtain the category of the to-be-tested image.

Embodiments described herein provide systems and processes for scene-aware object detection. This can involve an object detector that modulates its operations based on image location. The object detector can be a neural network detector or a scanning window detector, for example.

A training method for an image denoising model that can include collecting multiple sample image groups through a shooting device, each sample image group including multiple frames of sample images with a same photographic sensitivity and sample images in different sample image groups having different photographic sensitivities. The method can further include acquiring a photographic sensitivity of each sample image group, determining a noise characterization image corresponding to each sample image group based on the photographic sensitivity, determining a training input image group and a target image associated with each sample image group, each training input image group including all or part of sample images in a corresponding sample image group and a corresponding noise characterization image, constructing multiple training pairs each including a training input image group and a target image, and training the image denoising model based on the multiple training pairs until the image denoising model converges.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

An improvement to automatic classifying of threat level of objects in CT scan images of container content, methods include automatic identification of non-classifiable threat level object images, and displaying on a display of an operator a de-cluttered image, to improve operator efficiency. The de-cluttered image includes, as subject images, the non-classifiable threat level object images. Improvement to resolution of non-classifiable threat objects includes computer-directed prompts for the operator to enter information regarding the subject image and, based on same, identifying the object type. Improvement to automatic classifying of threat levels includes incremental updating the classifying, using the determined object type and the threat level of the object type.

An inference apparatus comprises a plurality of PEs (Processing Elements) and a control part. The control part operates a convolution operation in a convolutional neural network using each of a plurality of pieces of input data and a weight group including a plurality of weights corresponding to each of the plurality of pieces of input data by controlling the plurality of PEs. Further, each of the plurality of PEs executes a computation including multiplication of a single piece of the input data by a single weight and also executes multiplication included in the convolution operation using an element with a non-zero value included in each of the plurality of pieces of input data.