Abstract: A distributed learning method of a server managing an ID card recognition model includes releasing an ID card recognition model performing at least one convolution operation on an ID card image captured in a user terminal so that the user terminal uses the ID card recognition model, receiving update information of the ID card recognition model generated according to an ID card recognition result of the released ID card recognition model, and verifying the update information received from the user terminal and updating the ID card recognition model using the verified update information.

Abstract: A distributed learning method of a server managing an ID card recognition model includes releasing an ID card recognition model performing at least one convolution operation on an ID card image captured in a user terminal so that the user terminal uses the ID card recognition model, receiving update information of the ID card recognition model generated according to an ID card recognition result of the released ID card recognition model, and verifying the update information received from the user terminal and updating the ID card recognition model using the verified update information.

Abstract: The present invention relates to a neural network training method. The neural network training method using a de-identified image according to the present invention comprises the steps of: encoding a first image represented by a n-th dimensional vector into a predetermined p-th dimensional second image; decoding the second image into a q-th dimensional third image; inputting the third image to a neural network and extracting object information included in the third image; and training at least one parameter information used for computation in the neural network by using the extracted object information. According to the present invention, de-identified images are used for neural network training such that neural network training is made possible without using personal information included in images.

Abstract: An identification card authenticity determining method based on deep learning according to the disclosure for automatically checking authenticity of an identification card includes: inputting identification card data to a feature information extraction model to extract pieces of feature information, expressing an indicator for checking authenticity of the identification card, from the identification card data; inputting the extracted pieces of feature information to a classification model to determine authenticity of the identification card; and when it is determined that the identification card is falsified, extracting a class activation map, where a falsification region of the identification card data is activated, from the pieces of feature information.

Abstract: A method for recognizing an identification (ID) card of a user terminal using deep learning includes extracting an outline region of the ID card included in an input image using a first neural network model, modifying the ID card image of the image to a reference form using at least a partial value of the extracted outline region, and determining whether the modified ID card image is valid and recognizing text information in the valid ID card image. A recognition rate of the ID card may be increased by modifying an ID card image into a reference form using a trained neural network model.

Abstract: An identification card authenticity determining method based on deep learning according to the disclosure for automatically checking authenticity of an identification card includes: inputting identification card data to a feature information extraction model to extract pieces of feature information, expressing an indicator for checking authenticity of the identification card, from the identification card data; inputting the extracted pieces of feature information to a classification model to determine authenticity of the identification card; and when it is determined that the identification card is falsified, extracting a class activation map, where a falsification region of the identification card data is activated, from the pieces of feature information.

Abstract: A method for recognizing an identification (ID) card of a user terminal using deep learning includes extracting an outline region of the ID card included in an input image using a first neural network model, modifying the ID card image of the image to a reference form using at least a partial value of the extracted outline region, and determining whether the modified ID card image is valid and recognizing text information in the valid ID card image. A recognition rate of the ID card may be increased by modifying an ID card image into a reference form using a trained neural network model.

Abstract: A distributed learning method of a server managing an ID card recognition model includes releasing an ID card recognition model performing at least one convolution operation on an ID card image captured in a user terminal so that the user terminal uses the ID card recognition model, receiving update information of the ID card recognition model generated according to an ID card recognition result of the released ID card recognition model, and verifying the update information received from the user terminal and updating the ID card recognition model using the verified update information.

Abstract: Provided is a method for processing a radiographic image including obtaining a radiographic image using an indirect radiographic detector comprising a scintillator panel and a pixel array panel, determining a parameter value for defining a point spread function (PSF) according to the scintillator panel or the pixel array panel, and correcting the radiographic image by deconvoluting the radiographic image using the PSF to which the parameter value is applied.

Abstract: Provided is a method for processing a radiographic image including obtaining a radiographic image using an indirect radiographic detector comprising a scintillator panel and a pixel array panel, determining a parameter value for defining a point spread function (PSF) according to the scintillator panel or the pixel array panel, and correcting the radiographic image by deconvoluting the radiographic image using the PSF to which the parameter value is applied.

Abstract: A media controller with anti-microbial and anti-fungal capability is provided. The media controller not only receives and outputs an audio signal provided from an external media device such as a television or a recorded media player and controls the external media device, but also outputs the sound or audio signal received from the media device as audible sound. The media controller is capable of having an antimicrobial, antifungal, or antibacterial agent used in hospitals coated on an external surface thereof to provide anti-microbial and anti-fungal properties when a user comes into contact with the media controller.