Abstract: The image processing device 1X includes an acquisition means 30X and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The lesion detection means 34X detects a lesion based on a selection model which is selected from a first model and a second model, the first model being configured to make an inference regarding a lesion of the examination target based on a predetermined number of endoscopic images, the second model being configured to make an inference regarding a lesion of the examination target based on a variable number of endoscopic images. Besides, the lesion detection means 34X changes a parameter to be used for detection of the lesion based on a non-selection model that is the first model or the second model other than the selection model.

Abstract: The image processing device 1X includes an acquisition means 30X, a variation detection means 311X, a selection means 312X, and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The variation detection means 311X detects a degree of variation between the endoscopic images. The selection means 312X selects either one of a first model or a second model based on the degree of variation, the first model making an inference regarding a lesion of the examination target based on a predetermined number of the endoscopic images, the second model making an inference regarding the lesion based on a variable number of the endoscopic images. The lesion detection means 34X detects the lesion based on a selection model that is either the first model or the second model selected.

Abstract: The image processing device 1X includes an acquisition means 30X and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The lesion detection means 34X detects a lesion based on a selection model which is selected from a first model and a second model, the first model being configured to make an inference regarding a lesion of the examination target based on a predetermined number of endoscopic images, the second model being configured to make an inference regarding a lesion of the examination target based on a variable number of endoscopic images. Besides, the lesion detection means 34X changes a parameter to be used for detection of the lesion based on a non-selection model that is the first model or the second model other than the selection model.

Abstract: An information processing system includes: an acquisition unit that obtains a plurality of elements included in series data; a calculation unit that calculates a likelihood ratio indicating a likelihood of a class to which the series data belong, on the basis of at least two consecutive elements of the plurality of elements; a classification unit that classifies the series data into at least one class of a plurality of classes that are classification candidates, on the basis of the likelihood ratio; and a learning unit that performs learning related to calculation of the likelihood ratio, by using a loss function of a log-sum-exp type. According to such an information processing system, it is possible to properly select the class to which the series data belong, from a plurality of classes that are classification candidates.

Abstract: An information processing apparatus includes: an acquisition unit that obtains a plurality of elements included in series data; a calculation unit that calculates a likelihood ratio indicating a likelihood that the series data are derived from the same target as that of predetermined data, on the basis of at least two consecutive elements of the plurality of elements; and a determination unit that determines whether or not the series data are derived from the same target as that of the predetermined data, on the basis of the likelihood ratio. The calculation unit calculates the likelihood ratio in view of a degree of similarity or difference between the series data and the predetermined data. According to the information processing apparatus, it is possible to accurately determine whether or not the series data is derived from the same target as that of the predetermined data.

Abstract: An information processing system includes: an acquisition unit that obtains a plurality of elements included in series data; a calculation unit that calculates a likelihood ratio indicating a likelihood of a class to which the series data belong, on the basis of at least two consecutive elements of the plurality of elements; a classification unit that classifies the series data into at least one class of a plurality of classes that are classification candidates, on the basis of the likelihood ratio; and a learning unit that performs learning related to calculation of the likelihood ratio, by using a loss function in which the likelihood ratio increases when a correct answer class to which the series data belong is in a numerator of the likelihood ratio and the likelihood ratio decreases when the correct answer class is in a denominator of the likelihood ratio.

Abstract: An information processing system includes: an acquisition unit that obtains a plurality of elements included in series data; a calculation unit that calculates a likelihood ratio indicating a likelihood of a class to which the series data belong, on the basis of at least two consecutive elements of the plurality of elements: a classification unit that classifies the series data into at least one class, on the basis of the likelihood ratio; and a learning unit that performs learning related to calculation of the likelihood ratio, by using a plurality of series data. The learning unit changes a degree of contribution to the learning of each of the plurality of series data in accordance with ease of classification of the series data. According to such an information processing system, it is possible to properly perform the learning related to the calculation of the likelihood ratio.

Abstract: An information processing apparatus includes: an acquisition unit that obtains a plurality of elements included in series data; a calculation unit that calculates a classification index indicating a likelihood of a class to which the series data belong, on the basis of at least two elements of the plurality of elements; and a selection unit that selects and outputs, from N classes that are classification candidates of the series data (where N is a natural number), K classes to which the series data are likely to belong (where K is a natural number that is less than or equal to N and that is greater than or equal to 1), on the basis of the classification index. According to the information processing apparatus, it is possible to select a plurality of classes to which the series data are likely to belong.

Abstract: Provided is an information processing device including: an acquisition unit that sequentially acquires a plurality of elements included in sequential data; a first calculation unit that calculates, for each of the plurality of elements, indicators each indicating which of a plurality of classes is appropriate for corresponding element to belong to, in consideration of two or more elements among the plurality of elements; a second calculation unit that calculates, by integrating the indicators of the plurality of elements, an integrated indicator indicating which of the plurality of classes is appropriate for the sequential data to belong to; and a classification unit that classifies the sequential data into one of the plurality of classes based on the integrated indicator.

Abstract: Provided is an information processing device including: an acquisition unit that sequentially acquires a plurality of elements included in sequential data; a first calculation unit that calculates, for each of the plurality of elements, indicators each indicating which of a plurality of classes is appropriate for corresponding element to belong to, in consideration of two or more elements among the plurality of elements; a second calculation unit that calculates, by integrating the indicators of the plurality of elements, an integrated indicator indicating which of the plurality of classes is appropriate for the sequential data to belong to; and a classification unit that classifies the sequential data into one of the plurality of classes based on the integrated indicator.

Abstract: Provided is an information processing device including: an acquisition unit that sequentially acquires a plurality of elements included in sequential data; a first calculation unit that calculates, for each of the plurality of elements, indicators each indicating which of a plurality of classes is appropriate for corresponding element to belong to, in consideration of two or more elements among the plurality of elements; a second calculation unit that calculates, by integrating the indicators of the plurality of elements, an integrated indicator indicating which of the plurality of classes is appropriate for the sequential data to belong to; and a classification unit that classifies the sequential data into one of the plurality of classes based on the integrated indicator.

Abstract: Provided is an information processing device including: an acquisition unit that sequentially acquires a plurality of elements included in sequential data; a first calculation unit that calculates, for each of the plurality of elements, indicators each indicating which of a plurality of classes is appropriate for corresponding element to belong to, in consideration of two or more elements among the plurality of elements; a second calculation unit that calculates, by integrating the indicators of the plurality of elements, an integrated indicator indicating which of the plurality of classes is appropriate for the sequential data to belong to; and a classification unit that classifies the sequential data into one of the plurality of classes based on the integrated indicator.

Abstract: A spoofing detection apparatus includes a face image obtaining unit that obtains a first image frame that includes the face of a person when emitting light and a second image frame that includes the face of the person when not emitting light, a face information extraction unit that extract, from the first image frame, a first face information specifying a face portion, and extract, from the second image frame, a second face information specifying a face portion, a feature calculation unit that extracts a portion that includes a bright point in an iris region of an eye based on the first face information, extracts a portion corresponding to the portion that includes the bright point based on the second face information, and calculates a feature that is independent of the position of the bright point, and a spoofing determination unit that determines authenticity of person based on the feature.

Abstract: A spoofing detection apparatus comprises obtaining, from an image capture apparatus, a first image frame that includes the face of a subject person obtained when a light-emitting apparatus is emitting light and a second image frame that includes the face of the subject person obtained when the light-emitting apparatus is turned off, extracting, from the first image frame, information specifying a face portion of the subject person, and extract, from the second image frame, information specifying a face portion of the subject person, extracting a portion that includes a bright point formed by reflection in an iris region of an eye of the subject person, from the first image frame, extracts a portion corresponding to the portion that includes the bright point, from the second image frame, and calculates a feature that is independent of the position of the bright point, and determining authenticity of subject person based on the feature.

Abstract: A spoofing detection apparatus 300 includes a face image obtaining unit 301 that obtains a first image frame that includes the face of a subject person obtained with a light-emitting apparatus 320 turned on and a second image frame that includes the face of the subject person obtained with a light-emitting apparatus 320 turned off, a face information extraction unit 302 that extracts first face information, and extracts second face information, a feature value calculation unit 303 that obtains a luminance value of the face portion in the first image frame, obtains a luminance value of the face portion in the second image frame, and calculates, based on these values, a feature value that reflects a three-dimensional shape, and that is independent of colors of a face surface, and a spoofing determination unit 304 that determines authenticity of subject person based on the feature value.

Abstract: An information processing system (10) includes: an acquisition unit (50) configured to sequentially acquire a plurality of elements included in sequential data; a first calculation unit (110) configured to calculate, for each of the plurality of elements, a first indicator indicating which one of a plurality of classes the element belongs to; a weight calculation unit (130) configured to calculate, for each of the plurality of elements, a weight according to a confidence related to calculation of the first indicator; a second calculation unit (120) configured to calculate, based on the first indicators each weighted with the weight, a second indicator indicating which one of the plurality of classes the sequential data belongs to; and a classification unit (60) configured to classify the sequential data as any one of the plurality of classes, based on the second indicator. According to such an information processing system, sequential data can be appropriately classified.

Abstract: A determination device includes a partial image acquisition unit that repeatedly acquires a partial image of a target image until a predetermined end condition is established, a score calculation unit that calculates a score related to the presence or absence of processing of the partial image in each time the partial image acquisition unit acquires the partial image, and a processing determination unit that determines the presence or absence of processing of the target image on the basis of the score.

Abstract: A learning device includes a class classification learning unit that learns class classification of a classification target by using a loss function in which a loss is calculated to become smaller as a magnitude of a difference between a function value obtained by inputting a log-likelihood ratio to a function having a finite value range and a constant associated with a correct answer to the class classification of the classification target becomes smaller, the log-likelihood ratio being the logarithm of a ratio between the likelihood that the classification target belongs to a first class and the likelihood that the classification target belongs to a second class.

Abstract: A three-dimensional facial shape estimating device (300) includes a face image acquiring unit (301) configured to acquire a plurality of image frames that capture a subject's face; a face information acquiring unit (302) having, preset therein, a predetermined number of facial feature points, the face information acquiring unit (302) being configured to acquire, for each of the plurality of image frames, face information that indicates a position of each of the predetermined number of facial feature points of the subject's face within the image frame; and a three-dimensional shape estimating unit (303) configured to perform mapping of each of the predetermined number of facial feature points of the subject's face between the plurality of image frames based on the face information of each of the plurality of image frames and to estimate the three-dimensional shape of the subject's face based on a result from the mapping.

Abstract: An information processing apparatus (10) includes: an acquisition unit (50) configured to sequentially acquire a plurality of elements included in sequential data; a calculation unit (100) configured to calculate, based on at least two elements of the plurality of elements, a classification indicator indicating which one of a plurality of classes the sequential data belongs to; a processing unit (200) configured to execute either a first process of resetting the classification indicator to a predetermined value, or a second process of establishing a new thread to calculate the classification indicator; and a determination unit (300) configured to determine an interval including an element of a detection-target class, based on the classification indicator. According to such an information processing apparatus, an interval including elements of the detection-target class can be appropriately determined.