Disclosed are a semantic image extrapolation method and a semantic image extrapolation apparatus. The present invention provides a technique for generating an empty region for image-extension in an image by using an extrapolated segmentation map and an inpainting technique. The present invention is to provide, considering that there is no information in an empty region for image-extension in an image, a semantic image extrapolation method, of first generating an extrapolated segmentation map on the basis of a segmentation map from an input image, and filling the empty region for image-extension in the image with information on the basis of the extrapolated segmentation map and the input image.

The present invention enables highly accurate analysis when visualizing analysis results in spectral imaging.

An surface analysis method includes: acquiring spectral image data regarding a sample surface with use of a spectral camera; extracting n wavelengths dispersed in a specific wavelength range in the acquired spectral image data, and converting spectrums of the wavelengths in the spectral image data into n-dimensional spatial vectors for each pixel; normalizing the spatial vectors of the pixels; clustering the normalized spatial vectors into a specific number of classifications; and identifying and displaying pixels clustered into the classifications, for each of the classifications.

The present invention enables highly accurate analysis when visualizing analysis results in spectral imaging.

An surface analysis method includes: acquiring spectral image data regarding a sample surface with use of a spectral camera; extracting n wavelengths dispersed in a specific wavelength range in the acquired spectral image data, and converting spectrums of the wavelengths in the spectral image data into n-dimensional spatial vectors for each pixel; normalizing the spatial vectors of the pixels; clustering the normalized spatial vectors into a specific number of classifications; and identifying and displaying pixels clustered into the classifications, for each of the classifications.

In a learning apparatus, an acquisition unit acquires image data and label data corresponding to the image data. An object candidate extraction unit extracts each object candidate rectangle from the image data. A correct answer data generation unit generates a background object label corresponding to each background object included in each object candidate rectangle as correct answer data corresponding to the object candidate rectangle by using the label data. A prediction unit predicts a classification using each object candidate rectangle and outputs a prediction result. An optimization unit optimizes the object candidate extraction unit and the prediction unit using the prediction result and the correct answer data.

In a learning apparatus, an acquisition unit acquires image data and label data corresponding to the image data. An object candidate extraction unit extracts each object candidate rectangle from the image data. A correct answer data generation unit generates a background object label corresponding to each background object included in each object candidate rectangle as correct answer data corresponding to the object candidate rectangle by using the label data. A prediction unit predicts a classification using each object candidate rectangle and outputs a prediction result. An optimization unit optimizes the object candidate extraction unit and the prediction unit using the prediction result and the correct answer data.

An information processing system for obtaining an audio content file for video data providing video content representing a sport event, including: a receiver configured to receive a data stream including the video data; a preference data obtainer configured to obtain preference data, wherein the preference data indicate a selected competitor participating in the sport event; a category identifier obtainer configured to obtain a category identifier from a machine learning algorithm into which the video data is input, wherein the machine learning algorithm is trained to classify a scene represented in the video content into a category of a predetermined set of categories associated with the sport event, wherein the category identifier indicates the category into which the scene is classified; an audio content file obtainer configured to obtain, based on the obtained category identifier and the obtained preference data, the audio content file from a prestored set of audio content files, wherein the audio content file provides audio content associated with the category of the scene and the preference data; and a synchronizer configured to synchronize the audio content and the video content for synchronized play back of the scene by a media player configured to play back the video content and the audio content file.

An information processing system for obtaining an audio content file for video data providing video content representing a sport event, including: a receiver configured to receive a data stream including the video data; a preference data obtainer configured to obtain preference data, wherein the preference data indicate a selected competitor participating in the sport event; a category identifier obtainer configured to obtain a category identifier from a machine learning algorithm into which the video data is input, wherein the machine learning algorithm is trained to classify a scene represented in the video content into a category of a predetermined set of categories associated with the sport event, wherein the category identifier indicates the category into which the scene is classified; an audio content file obtainer configured to obtain, based on the obtained category identifier and the obtained preference data, the audio content file from a prestored set of audio content files, wherein the audio content file provides audio content associated with the category of the scene and the preference data; and a synchronizer configured to synchronize the audio content and the video content for synchronized play back of the scene by a media player configured to play back the video content and the audio content file.

An individual object identification system includes: an image acquisition processor configured to perform an image acquisition process to acquire an image of a subject acquired using imaging equipment; a feature point extraction processor that extracts a feature point from the image; a local feature amount calculation processor that calculates a local feature amount of the feature point; a local feature amount group classification processor that performs classification into a predetermined number of local feature amount groups; a global feature amount calculation processor that calculates a global feature amount based on each of the local feature amount groups; a searching target image registration processor that registers a plurality of images that are searching targets; a global feature amount registration processor that registers the global feature amount related to each of the registered images in a global feature amount registration unit; a narrowing processor that narrows down the plurality of registered images to registered images each having the global feature amount highly correlated with a global feature amount of an identification image; and a determination processor that compares the registered images as candidates with the identification image to determine the registered image having the largest number of corresponding points.

The present disclosure provides a method of processing an image, a device, and a medium. The method of processing the image includes: performing an image processing on an original image to obtain a component image for brightness of the original image; determining at least one of the original image and the component image as an image to be processed; classifying a pixel in the image to be processed, so as to obtain a classification result; processing the image to be processed according to the classification result, so as to obtain a target image; and determining an image quality of the original image according to the target image.

The present disclosure provides a method of processing an image, a device, and a medium. The method of processing the image includes: performing an image processing on an original image to obtain a component image for brightness of the original image; determining at least one of the original image and the component image as an image to be processed; classifying a pixel in the image to be processed, so as to obtain a classification result; processing the image to be processed according to the classification result, so as to obtain a target image; and determining an image quality of the original image according to the target image.