Abstract: To precisely predict future data even when the number of pieces of time-series data is small, in predicting the future data, using the time-series data. When the future data is predicted using the time-series data, whether present time data is used is determined based on prediction variation or a data transition, and then the prediction of the future data is performed.

Abstract: The present invention aims to estimate a more consistent posture in regard to a multi-joint object. A target range image is first input, a human body region is extracted from the input range image, a target joint position candidate is calculated from the input range image, and a joint position is finally determined based on the calculated joint position candidate and a likelihood of each joint to estimate the posture. At this time, joint position permissible range information concerning inter-joint distance and angle of a human body model previously set by learning is obtained from a human body model storing unit, consistency is evaluated for a relation between the joint position candidates of a certain joint and other joint based on the obtained information, and thus the posture corresponding to the best combination of the joint positions is determined.

Abstract: The present invention aims to estimate a more consistent posture in regard to a multi-joint object. A target range image is first input, a human body region is extracted from the input range image, a target joint position candidate is calculated from the input range image, and a joint position is finally determined based on the calculated joint position candidate and a likelihood of each joint to estimate the posture. At this time, joint position permissible range information concerning inter-joint distance and angle of a human body model previously set by learning is obtained from a human body model storing unit, consistency is evaluated for a relation between the joint position candidates of a certain joint and other joint based on the obtained information, and thus the posture corresponding to the best combination of the joint positions is determined.

Abstract: The present invention aims to estimate a more consistent posture in regard to a multi-joint object. A target range image is first input, a human body region is extracted from the input range image, a target joint position candidate is calculated from the input range image, and a joint position is finally determined based on the calculated joint position candidate and a likelihood of each joint to estimate the posture. At this time, joint position permissible range information concerning inter-joint distance and angle of a human body model previously set by learning is obtained from a human body model storing unit, consistency is evaluated for a relation between the joint position candidates of a certain joint and other joint based on the obtained information, and thus the posture corresponding to the best combination of the joint positions is determined.

Abstract: An image processing apparatus includes an extraction unit, a setting unit, a reference value calculation unit, a comparison unit, and an information determination unit. The extraction unit extracts a target area from an image. The setting unit sets a pixel from the target area as a pixel-of-interest. The reference value calculation unit calculates a reference value based on a pixel value of at least one or more pixels included in the target area. The comparison unit repeatedly performs processing of comparing a pixel value of at least one or more pixels among a plurality of pixels in a predetermined layout with respect to the pixel-of-interest with the reference value. The information determination unit determines information of the pixel-of-interest based on a comparison result of the comparison unit and a value corresponding to the comparison result.

Abstract: To precisely predict future data even when the number of pieces of time-series data is small, in predicting the future data, using the time-series data. When the future data is predicted using the time-series data, whether present time data is used is determined based on prediction variation or a data transition, and then the prediction of the future data is performed.

Abstract: A learning apparatus comprises a plurality of detection units configured to detect a part or whole of a target object in an image and output a plurality of detection results; an estimation unit configured to estimate a state of the target object based on at least one of the plurality of detection results; a classification unit configured to classify the image into a plurality of groups based on the state of the target object; and a weight calculation unit configured to calculate weight information on each of the plurality of detection units for each of the groups based on the detection results.

Abstract: An image processing apparatus includes an extraction unit, a setting unit, a reference value calculation unit, a comparison unit, and an information determination unit. The extraction unit extracts a target area from an image. The setting unit sets a pixel from the target area as a pixel-of-interest. The reference value calculation unit calculates a reference value based on a pixel value of at least one or more pixels included in the target area. The comparison unit repeatedly performs processing of comparing a pixel value of at least one or more pixels among a plurality of pixels in a predetermined layout with respect to the pixel-of-interest with the reference value. The information determination unit determines information of the pixel-of-interest based on a comparison result of the comparison unit and a value corresponding to the comparison result.

Abstract: The present invention aims to estimate a more consistent posture in regard to a multi-joint object. A target range image is first input, a human body region is extracted from the input range image, a target joint position candidate is calculated from the input range image, and a joint position is finally determined based on the calculated joint position candidate and a likelihood of each joint to estimate the posture. At this time, joint position permissible range information concerning inter-joint distance and angle of a human body model previously set by learning is obtained from a human body model storing unit, consistency is evaluated for a relation between the joint position candidates of a certain joint and other joint based on the obtained information, and thus the posture corresponding to the best combination of the joint positions is determined.

Abstract: A learning apparatus comprises a plurality of detection units configured to detect a part or whole of a target object in an image and output a plurality of detection results; an estimation unit configured to estimate a state of the target object based on at least one of the plurality of detection results; a classification unit configured to classify the image into a plurality of groups based on the state of the target object; and a weight calculation unit configured to calculate weight information on each of the plurality of detection units for each of the groups based on the detection results.