| Patent application number | Description | Published |
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| 20080235165 | Weak hypothesis generation apparatus and method, learning aparatus and method, detection apparatus and method, facial expression learning apparatus and method, facial enpression recognition apparatus and method, and robot apparatus - A facial expression recognition system that uses a face detection apparatus realizing efficient learning and high-speed detection processing based on ensemble learning when detecting an area representing a detection target and that is robust against shifts of face position included in images and capable of highly accurate expression recognition, and a learning method for the system, are provided. When learning data to be used by the face detection apparatus by Adaboost, processing to select high-performance weak hypotheses from all weak hypotheses, then generate new weak hypotheses from these high-performance weak hypotheses on the basis of statistical characteristics, and select one weak hypothesis having the highest discrimination performance from these weak hypotheses, is repeated to sequentially generate a weak hypothesis, and a final hypothesis is thus acquired. In detection, using an abort threshold value that has been learned in advance, whether provided data can be obviously judged as a non-face is determined every time one weak hypothesis outputs the result of discrimination. If it can be judged so, processing is aborted. A predetermined Gabor filter is selected from the detected face image by an Adaboost technique, and a support vector for only a feature quantity extracted by the selected filter is learned, thus performing expression recognition. | 09-25-2008 |
| 20080247598 | Weak hypothesis generation apparatus and method, learning apparatus and method, detection apparatus and method, facial expression learning apparatus and method, facial expression recognition apparatus and method, and robot apparatus - A facial expression recognition system that uses a face detection apparatus realizing efficient learning and high-speed detection processing based on ensemble learning when detecting an area representing a detection target and that is robust against shifts of face position included in images and capable of highly accurate expression recognition, and a learning method for the system, are provided. When learning data to be used by the face detection apparatus by Adaboost, processing to select high-performance weak hypotheses from all weak hypotheses, then generate new weak hypotheses from these high-performance weak hypotheses on the basis of statistical characteristics, and select one weak hypothesis having the highest discrimination performance from these weak hypotheses, is repeated to sequentially generate a weak hypothesis, and a final hypothesis is thus acquired. In detection, using an abort threshold value that has been learned in advance, whether provided data can be obviously judged as a non-face is determined every time one weak hypothesis outputs the result of discrimination. If it can be judged so, processing is aborted. A predetermined Gabor filter is selected from the detected face image by an Adaboost technique, and a support vector for only a feature quantity extracted by the selected filter is learned, thus performing expression recognition. | 10-09-2008 |
| 20090041340 | Image Processing System, Learning Device and Method, and Program - The present invention relates to an image processing system, a learning device and method, and a program which enable easy extraction of feature amounts to be used in a recognition process. Feature points are extracted from a learning-use model image, feature amounts are extracted based on the feature points, and the feature amounts are registered in a learning-use model dictionary registration section | 02-12-2009 |
| 20090060290 | FACE IMAGE PROCESSING APPARATUS, FACE IMAGE PROCESSING METHOD, AND COMPUTER PROGRAM - A face image processing apparatus selects feature points and feature for identifying a person through statistical learning. The apparatus includes input means for inputting a face image detected by arbitrary face detection means, face parts detection means for detecting the positions of face parts in several locations from the input face image, face pose estimation means for estimating face pose based on the detected positions of face parts, feature point position correcting means for correcting the position of each feature point used for identifying the person based on the result of estimation of face pose by the face pose estimation means, and face identifying means for identifying the person by calculating a feature of the input face image at each feature point after position correction is performed by the feature point position correcting means and checking the feature against a feature of a registered face. | 03-05-2009 |
| 20090138418 | LEARNING CONTROL APPARATUS, LEARNING CONTROL METHOD, AND COMPUTER PROGRAM - 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. | 05-28-2009 |
| 20090175533 | INFORMATION PROCESSING APPARATUS AND METHOD, RECORDING MEDIUM, AND PROGRAM - In an information processing apparatus, such as a robot that discriminates human faces, nodes are hierarchically arranged in a tree structure. Each of the nodes has a number of weak classifiers. Each terminal node learns face images associated with one label. An upper node learns learning samples of all labels learned by lower nodes. When a window image to be classified is input, discrimination is performed sequentially from upper nodes to lower nodes. When it is determined that the window image does not correspond to a human face, discrimination by lower nodes is not performed, and discrimination proceeds to sibling nodes. | 07-09-2009 |
| 20090232363 | INFORMATION PROCESSING APPARATUS, METHOD, AND PROGRAM - An information processing apparatus includes: face detecting means for detecting the orientation of a face in a face image; weight distribution generating means for generating a weight distribution based on a statistical distribution of the position of a predetermined feature of the face in the face image according to the orientation of the face; first calculation means for calculating a first evaluation value for evaluating each of predetermined regions of the face image to determine whether the region is the predetermined feature of the face; and face feature identifying means for identifying the predetermined region as the predetermined feature of the face based on the first evaluation value and the weight distribution. | 09-17-2009 |
| 20100021066 | INFORMATION PROCESSING APPARATUS AND METHOD, PROGRAM, AND RECORDING MEDIUM - An information processing apparatus includes a face detecting unit configured to detect a face in an image; a discriminating unit configured to discriminate an attribute of the face detected by the face detecting unit; a generating unit configured to generate, from the face detected by the face detecting unit and the attribute discriminated by the discriminating unit, a feature amount of the image; and a learning unit configured to learn, from the feature amount generated by the generating unit, information for discriminating whether the image corresponds to a predetermined scene. | 01-28-2010 |
| 20100318478 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing device includes: a calculating unit configured to calculate a current-state series candidate that is a state series for an agent capable of actions reaching the current state, based on a state transition probability model obtained by performing learning of the state transition probability model stipulated by a state transition probability that a state will be transitioned according to each of actions performed by an agent capable of actions, and an observation probability that a predetermined observation value will be observed from the state, using an action performed by the agent, and an observation value observed at the agent when the agent performs an action; and a determining unit configured to determine an action to be performed next by the agent using the current-state series candidate in accordance with a predetermined strategy. | 12-16-2010 |
| 20100318479 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing device includes: a learning section configured to learn a state transition probability model defined by state transition probability for each action of a state making a state transition due to an action performed by an agent capable of performing action and observation probability of a predetermined observed value being observed from the state, using an action performed by the agent and an observed value observed in the agent when the agent has performed the action. | 12-16-2010 |
| 20100329544 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing apparatus includes the following elements. A learning unit is configured to perform Adaptive Boosting Error Correcting Output Coding learning using image feature values of a plurality of sample images each being assigned a class label to generate a multi-class classifier configured to output a multi-dimensional score vector corresponding to an input image. A registration unit is configured to input a register image to the multi-class classifier, and to register a multi-dimensional score vector corresponding to the input register image in association with identification information about the register image. A determination unit is configured to input an identification image to be identified to the multi-class classifier, and to determine a similarity between a multi-dimensional score vector corresponding to the input identification image and the registered multi-dimensional score vector corresponding to the register image. | 12-30-2010 |
| 20100332229 | APPARATUS CONTROL BASED ON VISUAL LIP SHARE RECOGNITION - An information processing apparatus that includes an image acquisition unit to acquire a temporal sequence of frames of image data, a detecting unit to detect a lip area and a lip image from each of the frames of the image data, a recognition unit to recognize a word based on the detected lip images of the lip areas, and a controller to control an operation at the information processing apparatus based on the word recognized by the recognition unit. | 12-30-2010 |
| 20110010176 | HMM LEARNING DEVICE AND METHOD, PROGRAM, AND RECORDING MEDIUM - An HMM (Hidden Markov Model) learning device includes: a learning unit for learning a state transition probability as the function of actions that an agent can execute, with learning with HMM performed based on actions that the agent has executed, and time series information made up of an observation signal; and a storage unit for storing learning results by the learning unit as internal model data including a state-transition probability table and an observation probability table; with the learning unit calculating frequency variables used for estimation calculation of HMM state-transition and HMM observation probabilities; with the storage unit holding the frequency variables corresponding to each of state-transition probabilities and each of observation probabilities respectively, of the state-transition probability table; and with the learning unit using the frequency variables held by the storage unit to perform learning, and estimating the state-transition probability and the observation probability based on the frequency variables. | 01-13-2011 |
| 20110029465 | DATA PROCESSING APPARATUS, DATA PROCESSING METHOD, AND PROGRAM - A data processing apparatus includes an obtaining unit configured to obtain time-series data from a wearable sensor, an activity model learning unit configured to learn an activity model representing a user activity state as a stochastic state transition model from the obtained time-series data, a recognition unit configured to recognize a current user activity state by using the activity model of the user obtained by the activity model learning unit, and a prediction unit configured to predict a user activity state after a predetermined time elapses from a current time from the current user activity state recognized by the recognition unit. | 02-03-2011 |
| 20110060709 | DATA PROCESSING APPARATUS, DATA PROCESSING METHOD, AND PROGRAM - A data processing apparatus includes an action learning unit configured to train a user activity model representing activity states of a user in the form of a probabilistic state transition model using time-series location data items of the user, an action recognizing unit configured to recognize a current location of the user using the user activity model obtained through the action learning unit, an action estimating unit configured to estimate a possible route for the user from the current location recognized by the action recognizing unit and a selection probability of the route, and a travel time estimating unit configured to estimate an arrival probability of the user arriving at a destination and a travel time to the destination using the estimated route and the estimated selection probability. | 03-10-2011 |
| 20110091071 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - 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. | 04-21-2011 |
| 20110112997 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - 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. | 05-12-2011 |
| 20110137831 | LEARNING APPARATUS, LEARNING METHOD AND PROGRAM - 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. | 06-09-2011 |
| 20110137833 | DATA PROCESSING APPARATUS, DATA PROCESSING METHOD AND PROGRAM - 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. | 06-09-2011 |
| 20110137834 | LEARNING APPARATUS AND METHOD, PREDICTION APPARATUS AND METHOD, AND PROGRAM - 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. | 06-09-2011 |
| 20110137835 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - 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. | 06-09-2011 |
