Abstract: In a plane detection apparatus, a plane detection unit (3) includes a line fitting block (4) to select a group of distance data points being in one plane from distance data forming an image and extract lines from the distance data point group, and a region growing block (5) to detect one or more planar regions existing in the image from a group of all lines included in the image and extracted by the line fitting block (4). The line fitting block (4) first draws a line D1 connecting end points of the distance data point group, searches a point of interest brk whose distance to the line L1 is largest, segments the data point group by the point of interest brk when the distance is larger than a predetermined threshold, and determines a line L2 by the least-squares method when the distance is smaller than the predetermined threshold.

Abstract: An image processing apparatus includes: an image feature outputting unit that outputs each of image features in correspondence with a time of the frame; a foreground estimating unit that estimates a foreground image at a time s by executing a view transform as a geometric transform on a foreground view model and outputs an estimated foreground view; a background estimating unit that estimates a background image at the time s by executing a view transform as a geometric transform on a background view model and outputs an estimated background view; a synthesized view generating unit that generates a synthesized view by synthesizing the estimated foreground and background views; a foreground learning unit that learns the foreground view model based on an evaluation value; and a background learning unit that learns the background view model based on the evaluation value by updating the parameter of the foreground view model.

Abstract: An information processing device includes a learning unit that performs, using an action performed by an object and an observation value of an image as learning data, learning of a separation learning model that includes a background model that is a model of the background of the image and one or more foreground model(s) that is a model of a foreground of the image, which can move on the background, in which the background model includes a background appearance model indicating the appearance of the background, and at least one among the one or more foreground model(s) includes a transition probability, with which a state corresponding to the position of the foreground on the background is transitioned by an action performed by the object corresponding to the foreground, for each action, and a foreground appearance model indicating the appearance of the foreground.

Abstract: An information processing apparatus includes: a distinguishing unit which, by using an ensemble classifier, which includes a plurality of weak classifiers outputting weak hypotheses which indicates whether a predetermined subject is shown in an image in response to inputs of a plurality of features extracted from the image, and a plurality of features extracted from an input image, sequentially integrates the weak hypotheses output by the weak classifiers in regard to the plurality of features and distinguishes whether the predetermined subject is shown in the input image based on the integrated value. The weak classifier classifies each of the plurality of features to one of three or more sub-divisions based on threshold values, calculates sum divisions of the sub-divisions of the plurality of features as whole divisions into which the plurality of features is classified, and outputs, as the weak hypothesis, a reliability degree of the whole divisions.

Abstract: A data processing device includes a state value calculation unit which calculates a state value of which the value increases as much as a state with a high transition probability for each state of the state transition model, an action value calculation unit which calculates an action value, of which the value increases as a transition probability increases for each state of the state transition model and each action that the agent can perform, a target state setting unit which sets a state with great unevenness in the action value among states of the state transition model to a target state that is the target to reach by action performed by the agent, and an action selection unit which selects an action of the agent so as to move toward the target state.

Abstract: A data processing device includes: a data obtaining section obtaining time series data on a total value of current consumed by a plurality of electric apparatuses; and a parameter estimating section obtaining a model parameter when states of operation of the plurality of electric apparatuses are modeled by a factorial HMM on a basis of the obtained time series data.

Abstract: An information processing device includes a feature amount extracting unit configured to extract the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene; a clustering unit configured to use cluster information that is the information of the cluster obtained by performing cluster learning; a highlight label generating unit configured to generate a highlight label sequence; and a highlight detector learning unit configured to perform learning of the highlight detector.

Abstract: There is provided an information processing apparatus including: a positioning unit acquiring positioning information on a latitude and longitude showing a position of the positioning unit; a transmission unit transmitting a time-series log, which includes the positioning information acquired by the positioning unit, to a server; a reception unit receiving an activity model showing an activity state of a user, the activity model being obtained by a learning process carried out by the server based on the time-series log; a recognition unit recognizing a present activity state of the user using the positioning information acquired by the positioning unit and the activity model received by the reception unit; and a prediction unit predicting behavior of the user from the present activity state of the user recognized by the recognition unit.

Abstract: An information processing apparatus includes a first generation unit that generates learning images corresponding to a learning moving image, a first synthesis unit that generates a synthesized learning image such that a plurality of the learning images is arranged at a predetermined location and synthesized, a learning unit that computes a feature amount of the generated synthesized learning image, and performs statistical learning using the feature amount to generate a classifier, a second generation unit that generates determination images, a second synthesis unit that generates a synthesized determination image such that a plurality of the determination images is arranged at a predetermined location and synthesized, a feature amount computation unit that computes a feature amount of the generated synthesized determination image, and a determination unit that determines whether or not the determination image corresponds to a predetermined movement.

Abstract: A data processing device including a learning section which expresses user movement history data obtained as learning data as a probability model which expresses activities of a user and learns parameters of the model; a destination and stopover estimation section which estimates a destination node and a stopover node from state nodes of the probability model; a current location estimation section which inputs the user movement history data in the probability model and estimates a current location node which is equivalent to the current location of the user; a searching section which searches for a route from the current location of the user to a destination using information on the estimated destination node and stopover node and the current location node and the probability model obtained by learning; and a calculating section which calculates an arrival probability and a necessary time to the searched destination.

Abstract: An information processing device includes a first calculation unit which calculates a score of each sample image including a positive image in which an object as an identification object is present and a negative image in which the object as the identification object is not present, for each weak identifier of an identifier including a plurality of weak identifiers, a second calculation unit which calculates the number of scores when the negative image is processed, which are scores less than a minimum score among scores when the positive image is processed; and an realignment unit which realigns the weak identifiers in order from a weak identifier in which the number calculated by the second calculation unit is a maximum.

Abstract: An information processing device includes a face detection unit that detects a face area from a target image, a feature point detection unit that detects a feature point of the detected face area, a determination unit that determines an attention area that is an area to which attention is paid in the face area based on the detected feature point, a reference color extraction unit that extracts a reference color that is color setting obtained from the target image in the determined attention area, an adjustment unit that adjusts the extracted reference color to a color setting for a modified image generated from the target image as a base, and a generation unit that generates the modified image from the target image by drawing the attention area using the color setting for the modified image.

Abstract: A learning control apparatus for an autonomous agent including a functional module having a function of multiple inputs and multiple outputs, the function receiving at least one variable and outputting at least one value, includes an estimating unit for estimating a causal relationship of at least one variable, a grouping unit for grouping at least one variable into a variable group in accordance with the estimated causal relationship, a determining for determining a behavior variable corresponding to each of the variable groups, and a layering unit for layering, in accordance with the variable group and the behavior variable, the function corresponding to each variable group, the function receiving the variable grouped into the variable group and outputting the behavior variable.

Abstract: A learning apparatus includes: a location acquiring section for acquiring time series data on locations of a user; a time acquiring section for acquiring time series data on times; and learning section for learning an activity model indicating an activity state of the user as a probabilistic state transition model, using the respective acquired time series data on the locations and the times as an input.

Abstract: An information processing device includes an acquisition unit acquiring a viewing log including information representing content of an operation for viewing content and time of the operation, a learning unit learning, based on the viewing log acquired by the acquisition unit, a viewing behavior model which is a stochastic state transition model representing a viewing behavior of a user, a recognition unit recognizing, using the viewing behavior model obtained through learning by the learning unit, a current viewing state of the user, a prediction unit predicting, using the viewing behavior model, the viewing behavior of the user after a predetermined period of time with the current viewing state of the user recognized by the recognition unit as a starting point, and a display control unit displaying information relating to content predicted to be viewed through the viewing behavior predicted by the prediction unit.

Abstract: A learning apparatus includes: an interpolating section which interpolates data missing in time series data; an estimating section which estimates a Hidden Markov Model from the time series data; and a likelihood calculating section which calculates the likelihood of the estimated Hidden Markov Model. The likelihood calculating section calculates the likelihood for normal data which does not have missing data and the likelihood for interpolation data which is interpolated data in different conditions and calculates the likelihood of the Hidden Markov Model for the time series data in which the data is interpolated. The estimating section updates the Hidden Markov Model so that the likelihood calculated by the likelihood calculating section becomes high.

Abstract: The data processing apparatus includes a state series generation unit and a computing unit. The state series generation unit generates a time series data of state nodes from a time series data of event. The state transition model of the event is expressed as a stochastic state transition model. The computing unit computes the parameters for the stochastic state transition model of events by computing parameters of time series data corresponding to an appearance frequency of the state nodes, the appearance frequency of transitions among the state nodes and the like.

Abstract: An information processing device includes a model learning unit that carries out learning for self-organization of internal states of a state transition prediction model which is a learning model having internal states, a transition model of the internal states, and an observation model where observed values are generated from the internal states, by using first time series data, wherein the model learning unit learns the observation model of the state transition prediction model after the learning using the first time series data, by fixing the transition model and using second time series data different from the first time series data, thereby obtaining the state transition prediction model having a first observation model where each sample value of the first time series data is observed and a second observation model where each sample value of the second time series data is observed.

Abstract: An information processing apparatus including an image acquisition unit that acquires a target image; a face part extraction unit that extracts a face region including a face part from the target image; an identification unit that identifies a model face part by comparing the face part to a plurality of model face parts stored in a storage unit; and an illustration image determination unit that determines an illustration image corresponding to the identified model face part.

Abstract: An environment map building method and an environment map building apparatus can express the surrounding embodiment with a high resolution in the height direction in a manner robust relative to observation noises.