An image recognition method includes: obtaining a to-be-recognized image; determining whether the image is a forged image by recognizing the image through a trained generative adversarial network, the generative adversarial network including a generator and a classifier. Training the classifier includes: obtaining an original image group having a plurality of original images, and a category label of each original image. Each of the plurality of original images includes a real image and a forged image corresponding to the real image. The method includes obtaining using the classifier, for a respective original image of the plurality of original images, first-type noise corresponding to the respective original image; inputting the respective original image into the generator to obtain an output of the generator, and obtaining second-type noise corresponding to the respective original image as the output; and training the classifier using the respective original image, the first-type noise, and the second-type noise.

A face image processing method and apparatus, a face image display method and apparatus, and a device are provided, belonging to the technical field of image processing. The method includes: acquiring a first face image of a person; invoking an age change model to predict a texture difference map of the first face image at a specified age, the texture difference map being used for reflecting a texture difference between a face texture in the first face image and a face texture of a second face image of the person at the specified age; and performing image processing on the first face image based on the texture difference map to obtain the second face image.

A face image processing method and apparatus, a face image display method and apparatus, and a device are provided, belonging to the technical field of image processing. The method includes: acquiring a first face image of a person; invoking an age change model to predict a texture difference map of the first face image at a specified age, the texture difference map being used for reflecting a texture difference between a face texture in the first face image and a face texture of a second face image of the person at the specified age; and performing image processing on the first face image based on the texture difference map to obtain the second face image.

A computer-implemented method for evaluating an image classifier, in which a classifier output of the image classifier is provided for the actuation of an at least semi-autonomous robot. The evaluation method includes: ascertaining a first dataset including image data and annotations being assigned to the image data, the annotations including information about the scene imaged in the respective image and/or about image regions to be classified and/or about movement information of the robot; ascertaining regions of the scenes that are reachable by the robot based on the annotations; ascertaining relevance values for image regions to be classified by the image classifier; classifying the image data of the first image dataset with the aid of the image classifier; evaluating the image classifier based on image regions correctly classified by the image classifier and incorrectly classified image regions, as well as the calculated relevance values of the corresponding image regions.

Systems and methods include an image capture component configured to capture infrared images of a scene, and a logic device configured to identify a target in the images, acquire temperature data associated with the target based on the images, evaluate the temperature data and determine a corresponding temperature classification, and process the identified target in accordance with the temperature classification. The logic device identifies a person and tracks the person across a subset of the images, identify a measurement location for the target in a subset of the images based on target feature points identified by a neural network, and measure a temperature of the location using corresponding values from one or more captured thermal images. The logic device is further configured calculate a core body temperature of the target using the temperature data to determine whether the target has a fever and calibrate using one or more black bodies.

Systems and methods include an image capture component configured to capture infrared images of a scene, and a logic device configured to identify a target in the images, acquire temperature data associated with the target based on the images, evaluate the temperature data and determine a corresponding temperature classification, and process the identified target in accordance with the temperature classification. The logic device identifies a person and tracks the person across a subset of the images, identify a measurement location for the target in a subset of the images based on target feature points identified by a neural network, and measure a temperature of the location using corresponding values from one or more captured thermal images. The logic device is further configured calculate a core body temperature of the target using the temperature data to determine whether the target has a fever and calibrate using one or more black bodies.

A learning data generation device that can generate learning data suitable for learning of an identification model. The learning data generation device has a function of cutting out part of first image data as second image data, a function of generating a two-dimensional graphic corresponding to the area of the second image data and representing a pseudo defect, a function of generating third image data by combining the second image data and the two-dimensional graphic, and a function of assigning a label corresponding to the two-dimensional graphic to the third image data. By using the third image data for learning of the identification model, a highly accurate identification model can be generated.

A learning data generation device that can generate learning data suitable for learning of an identification model. The learning data generation device has a function of cutting out part of first image data as second image data, a function of generating a two-dimensional graphic corresponding to the area of the second image data and representing a pseudo defect, a function of generating third image data by combining the second image data and the two-dimensional graphic, and a function of assigning a label corresponding to the two-dimensional graphic to the third image data. By using the third image data for learning of the identification model, a highly accurate identification model can be generated.

A program and the like that make a catheter system relatively easy to use. The program including a non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process comprising: acquiring a tomographic image generated using a diagnostic imaging catheter inserted into a lumen organ; and inputting the acquired tomographic image to a first model configured to output types of a plurality of objects included in the tomographic image and ranges of the respective objects in association with each other when the tomographic image is input, and outputting the types and ranges of the objects output from the first model.

A program and the like that make a catheter system relatively easy to use. The program including a non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process comprising: acquiring a tomographic image generated using a diagnostic imaging catheter inserted into a lumen organ; and inputting the acquired tomographic image to a first model configured to output types of a plurality of objects included in the tomographic image and ranges of the respective objects in association with each other when the tomographic image is input, and outputting the types and ranges of the objects output from the first model.