Abstract: The present invention is directed toward a system and method for STED nanography, which reduces background noise. To remove background noise from a STED image, the polarization of the STED beam is altered from that used to obtain the original image. A polarized image is obtained. This polarized image can then be subtracted from the original image to remove noise inherent to the image.

Abstract: Provided are a method and program for computing a bone age using a deep neural network. The method for computing a bone age using a deep neural network, including: receiving an analysis target image that is a specific medical image to compute the bone age; and analyzing the analysis target image by at least one computer using the deep neural network to compute the bone age. According to the present disclosure, since the bone age is computed by accumulating medical images of a specific race (particularly, Korean) and analyzing the same, it is possible to compute an accurate bone age that conforms to race.

Abstract: Provided are a method and program for computing a bone age using a deep neural network. The method for computing a bone age using a deep neural network, including: receiving an analysis target image that is a specific medical image to compute the bone age; and analyzing the analysis target image by at least one computer using the deep neural network to compute the bone age. According to the present disclosure, since the bone age is computed by accumulating medical images of a specific race (particularly, Korean) and analyzing the same, it is possible to compute an accurate bone age that conforms to race.

Abstract: An apparatus and a method for emotion recognition are provided. The apparatus for emotion recognition includes a frame parameter generator configured to detect a plurality of unit frames from an input speech and to generate a parameter vector for each of the unit frames, a key-frame selector configured to select a unit frame as a key frame among the plurality of unit frames, an emotion-probability calculator configured to calculate an emotion probability of each of the selected key frames, and an emotion determiner configured to determine an emotion of a speaker based on the calculated emotion probabilities.

Abstract: An apparatus and a method for emotion recognition are provided. The apparatus for emotion recognition includes a frame parameter generator configured to detect a plurality of unit frames from an input speech and to generate a parameter vector for each of the unit frames, a key-frame selector configured to select a unit frame as a key frame among the plurality of unit frames, an emotion-probability calculator configured to calculate an emotion probability of each of the selected key frames, and an emotion determiner configured to determine an emotion of a speaker based on the calculated emotion probabilities.