A computer-implemented method of searching for a region indicative of a pathology in an image of a portion of an eye acquired by an ocular imaging system, the method comprising: receiving image data defining the image; searching for the region in the image by processing the received image data using a learning algorithm; and in case a region in the image that is indicative of the pathology is found: determining a location of the region in the image; generating an instruction for an eye measurement apparatus to perform a measurement on the portion of the eye to generate measurement data, using a reference point based on the determined location for setting a location of the measurement on the portion of the eye; and receiving the measurement data from the eye measurement apparatus.

There is described a computer-implemented method and system for classifying images, the computer-implemented method comprising: receiving an image to be classified, generating a vector representation of the image to be classified using an image embedding method, comparing the vector representation of the image to predefined vector representations of the predefined image categories, and identifying a relevant category amongst the predefined image categories based on the comparison, the relevant category being associated with the image to be classified and outputting the relevant category.

Embodiments pertain to a method for characterizing a droopy upper eyelid performed on a computer having a processor, memory, and one or more code sets stored in the memory and executed in the processor. The method may comprise capturing an image of a patient's facial features comprising an eye and a droopy upper eyelid; identifying at least one geometric feature of a pupil of the eye within the image; and determining, based on the at least one geometric feature, whether the droopy upper eyelid is vision impairing or not, or whether the droopy upper eyelid is more likely vision impairing than not vision-impairing.

Embodiments pertain to a method for characterizing a droopy upper eyelid performed on a computer having a processor, memory, and one or more code sets stored in the memory and executed in the processor. The method may comprise capturing an image of a patient's facial features comprising an eye and a droopy upper eyelid; identifying at least one geometric feature of a pupil of the eye within the image; and determining, based on the at least one geometric feature, whether the droopy upper eyelid is vision impairing or not, or whether the droopy upper eyelid is more likely vision impairing than not vision-impairing.

A computer-readable recording medium has stored a program that causes a computer to execute a process including: generating a trained model that includes performing machine learning of a 1st_model based on a 1st_output value that is obtained when a 1st_image is input to the 1st_model in response to input of training data containing pair of the 1st_image and a 2nd_image and containing a 1st_label indicating which of the 1st and 2nd_image has captured greater movement of muscles of facial expression of a photographic subject, a 2nd_output value obtained when the 2nd_image is input to a 2nd_model that has common parameters with the 1st_model, and the 1st_label; and generating a 3rd_model that includes performing machine learning based on a 3rd_output value obtained when a 3rd_image is input to the trained model, and a 2nd_label indicating of movement of muscles of facial expression of a photographic subject captured in the 3rd_image.

A computer-readable recording medium has stored a program that causes a computer to execute a process including: generating a trained model that includes performing machine learning of a 1st_model based on a 1st_output value that is obtained when a 1st_image is input to the 1st_model in response to input of training data containing pair of the 1st_image and a 2nd_image and containing a 1st_label indicating which of the 1st and 2nd_image has captured greater movement of muscles of facial expression of a photographic subject, a 2nd_output value obtained when the 2nd_image is input to a 2nd_model that has common parameters with the 1st_model, and the 1st_label; and generating a 3rd_model that includes performing machine learning based on a 3rd_output value obtained when a 3rd_image is input to the trained model, and a 2nd_label indicating of movement of muscles of facial expression of a photographic subject captured in the 3rd_image.

A method and system for detecting a typical object of a transmission line based on UAV federated learning. The method includes: determining a detection model for a typical object of a transmission line by YOLOv3 object detection algorithm according to a prior database for the typical object; dividing a UAV network into multiple federated learning units; acquiring pictures, taken by the UAV network, of the typical object and tags corresponding to each picture to determine a training database; training, based on Horovod framework and FATE federated learning framework, each federated learning unit according to the training database and the detection model for the typical object, and determining the trained UAV network according to the trained federated learning unit; and determining, by the trained UAV network, the typical object in each picture. A congestion of communication links is avoided, thereby improving detection efficiency.

A method and system for detecting a typical object of a transmission line based on UAV federated learning. The method includes: determining a detection model for a typical object of a transmission line by YOLOv3 object detection algorithm according to a prior database for the typical object; dividing a UAV network into multiple federated learning units; acquiring pictures, taken by the UAV network, of the typical object and tags corresponding to each picture to determine a training database; training, based on Horovod framework and FATE federated learning framework, each federated learning unit according to the training database and the detection model for the typical object, and determining the trained UAV network according to the trained federated learning unit; and determining, by the trained UAV network, the typical object in each picture. A congestion of communication links is avoided, thereby improving detection efficiency.

The present disclosure is to provide a method for providing customized cooking content and a user terminal for implementing the same that may increase a quality of the dish and interest in cooking by allowing a user to cook at a pace that suits a user's level in consideration of cooking skills of the user, and may provide an environment more suitable for cooking in association with a near kitchen tool and/or home appliance. Provided is a user terminal including a display, a camera, and a controller for performing control to recognize information about a cooking process appearing in cooking content via artificial intelligence, recognize, via the artificial intelligence, a cooking situation of a user filmed via the camera during reproduction of the cooking content, and adjust a reproduction speed of the cooking content based on the cooking process of the cooking content and the cooking situation of the user.

The present disclosure is to provide a method for providing customized cooking content and a user terminal for implementing the same that may increase a quality of the dish and interest in cooking by allowing a user to cook at a pace that suits a user's level in consideration of cooking skills of the user, and may provide an environment more suitable for cooking in association with a near kitchen tool and/or home appliance. Provided is a user terminal including a display, a camera, and a controller for performing control to recognize information about a cooking process appearing in cooking content via artificial intelligence, recognize, via the artificial intelligence, a cooking situation of a user filmed via the camera during reproduction of the cooking content, and adjust a reproduction speed of the cooking content based on the cooking process of the cooking content and the cooking situation of the user.