Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map being labeled with a plurality of corresponding classes of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; and outputting a detected dominant class of the scene based on a plurality of ranked labels based on the area ratios.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map being labeled with a plurality of corresponding classes of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; and outputting a detected dominant class of the scene based on a plurality of ranked labels based on the area ratios.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map being labeled with a plurality of corresponding classes of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; and outputting a detected dominant class of the scene based on a plurality of ranked labels based on the area ratios.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map being labeled with a plurality of corresponding classes of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; and outputting a detected dominant class of the scene based on a plurality of ranked labels based on the area ratios.

Abstract: An image correction method is provided. The image correction method includes receiving an input image from a first camera; receiving a guide image from a second camera; generating a corrected image by performing image correction on the input image using the guide image, the image correction being performed by an image generation unit having a pre-learned image generation parameter learned using a training image; generating a quality value of the corrected image by an image evaluation unit having an pre-learned image evaluation parameter learned using the training image; and performing the image correction on the corrected image again when the quality value of the corrected image is less than a set value, and outputting the corrected image as a final image when the quality value of the corrected image is greater than the set value.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map being labeled with a plurality of corresponding classes of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; and outputting a detected dominant class of the scene based on a plurality of ranked labels based on the area ratios.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map including a plurality of pixels, each of the pixels being labeled with a corresponding class of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; applying inference to generate a plurality of ranked labels based on the area ratios; and outputting a detected dominant class of the scene based on the plurality of ranked labels.

Abstract: An image correction method is provided. The image correction method includes receiving an input image from a first camera; receiving a guide image from a second camera; generating a corrected image by performing image correction on the input image using the guide image, the image correction being performed by an image generation unit having a pre-learned image generation parameter learned using a training image; generating a quality value of the corrected image by an image evaluation unit having an pre-learned image evaluation parameter learned using the training image; and performing the image correction on the corrected image again when the quality value of the corrected image is less than a set value, and outputting the corrected image as a final image when the quality value of the corrected image is greater than the set value.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map including a plurality of pixels, each of the pixels being labeled with a corresponding class of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; applying inference to generate a plurality of ranked labels based on the area ratios; and outputting a detected dominant class of the scene based on the plurality of ranked labels.

Abstract: A computing device configured to perform an operation of a neural network including a plurality of layers includes a memory in which a gain corresponding to each of the plurality of layers is stored, and a processor configured to receive an input image to generate a plurality of raw feature maps at each of the plurality of layers, apply a gain corresponding to each of the plurality of raw feature maps to generate a plurality of output feature maps, and generate an output image as a result of summation of the plurality of output feature maps due to an image reconstruction layer.

Abstract: An apparatus and method for controlling wireless transmission. The apparatus, which controls a transmitter that provides an output signal by upconverting and amplifying an input signal, includes a memory configured to store first calibration data representing a saturation power and a linear gain of a reference transmitter as functions of operating conditions of the reference transmitter. A controller may be configured to estimate a saturation power and a linear gain of the transmitter of the apparatus based on operating conditions of the transmitter and the first calibration data. The controller may generate at least one control signal for controlling an operation of the transmitter based on a saturation power difference between the estimated saturation power and a target saturation power and a linear gain difference between the estimated linear gain and a target linear gain.