Panasonic Corporation

Matsushita Electric Industrial Co., Ltd.

Panasonic Intellectual Property Management Co., Ltd.

Sovereign Peak Ventures, LLC

Panasonc Corporation

Kyoko Kawaguchi

Masamoto Tanabiki

Hisashi Takayama

Yuji Sato

Yoshiaki Nakanishi

Osamu Sasaki

Mitsuko Fujita

Kensuke Maruya

Atsushi Ando

Tetsuo Matsuse

Eiji Takahashi

Masataka Sugiura

Yoshihiro Tsukamoto

Kouji Hatano

Kazuhiko Iwai

Taiki Sekii

Kazunori Inoue

Takao Isogawa

Kazuo Sakushima

Tateaki Nogami

Yoshimitsu Aoki

Akio Shibata

Yoshihiro-c/o Mats ElecCoLtd Iproc Tsukamoto

Eiji Mat Elec Ind Co Ltd Iproc Takahashi

Kouj Mats Elec Ind Co Ltd Iproc Hatano

Kouji c/o Matsushita Ind Co Ltd Hatano

Eiji c/o Matsushita El Ind Co Ltd Takahashi

Yoshihiro c/o MatsElecInd Co Iproc Tsukamoto

A learning device includes an input section and a learning section. The input section obtains training data in which an image containing an image of equipment of a vehicle, first information indicating a skeleton location of a specific part of a vehicle-occupant, and second information indicating a state of the vehicle-occupant with respect to the equipment are associated with each other. The learning section forms a model such that an estimating device can obtain the first information and the second information associated with the image containing the image of the equipment.

Learning device, estimating device, estimating system, learning method, estimating method, and storage medium to estimate a state of vehicle-occupant with respect to vehicle equipment

A learning apparatus includes a first learning unit, a learning data generator, and a second learning unit. The first learning unit implements a learning process for a first classifier such that a class applicable degree of a candidate class corresponding to a correct answer class becomes the maximum as compared with class applicable degrees of other candidate classes. The learning data generator performs a classification process for a classification target using the first classifier already subjected to the learning process, converts class applicable degrees of a plurality of candidate classes, which are output by the first classifier already subjected to the learning process into a dimensionally compressed value based on a predetermined compression rule, and generates second learning data in which the identification target is associated with the dimensionally compressed value. The second learning unit implements a learning process for a second classifier using the second learning data.

Learning apparatus, learning method, estimation apparatus, estimation method, and computer-readable storage medium

This posture state estimation device is capable of estimating with high accuracy the posture state of an object. The posture state estimation device () is a device for estimating the posture state of an object having a plurality of sections connected by joints on the basis of image data that images the object, and has a section candidate extraction unit () for extracting a section candidate for a section from the image data, a complementary section candidate extraction unit () which estimates that a portion of an unextracted section for which a section candidate has not been extracted is being shadowed by an already extracted section for which a section candidate has been extracted in order to extract a section candidate of the unextracted section, and a posture state estimation unit () for estimating the posture state of the object on the basis of the extracted section candidate.

Posture state estimation apparatus and posture state estimation method

The present invention provides an electronic wallet having a higher level of anonymity, security and convenience, which is capable of efficient electronizing of value information as well as which allows the user to efficiently manage the electronic value information. A presentation card indicating the fixed property of an electronic value is digitally signed by the service provider; a variable property is digitally signed with the private key of that electronic value. A service control, security information, representation control and representation resource are digitally signed by the service provider. These digital signature will be authenticated each time an electronic value object is generated.

Communication terminal and communication method using plural wireless communication schemes

A processor of an orientation estimation device, as an orientation estimation method for estimating an orientation of a player existing in a sport video, receives reference orientation information which is information obtained based on information input by a user and which is information designating a joint position of a specific player existing in the sport video of an estimation target game and estimates an orientation of an estimation target player who is a player other than the specific player existing in a sport video of an estimation target using the reference orientation information.

Orientation estimation method and orientation estimation device

This technology is a video processing method and a video processing device, in which a processor performs processing on video data of video obtained by capturing a sports game. A processor receives video data, calculates a motion amount of a player for each frame, from the received video data, and estimates at least one of a start frame of a play in the game, and an end frame at which an immediately preceding play, that is a one-previous play of the play, is ended, based on the calculated motion amount.

Video processing method, and video processing device

State-of-posture estimation device and state-of-posture estimation method

With this device, an edge pair likelihood map generation unit () computes edge pair likelihood which denotes the plausibility that a pixel of a pair is an edge of an estimation subject site, and generates an edge pair likelihood map which denotes the edge pair likelihood for each pixel. A continuity likelihood map generation unit () evaluates, upon the edge pair likelihood map, the continuity for the edge pair likelihood of a pixel which is included in a region wherein the estimate subject site is presumed, and generates a continuity likelihood map which denotes an edge pair likelihood which has continuity as a candidate region of the estimate subject site. An integrated likelihood map generation unit () generates an integrated likelihood map which denotes the candidate region which the continuity likelihood map denotes, refined based on a predetermined condition.

Site estimation device, site estimation method, and site estimation program

A candidate region extraction unit () extracts candidate regions from an image by using a common characteristic amount. A part region extraction unit () separates a part region, which has a high likelihood of being a part, from a second-part candidate region other than the part region. A characteristic amount calculation unit () calculates the intrinsic characteristic amount of each of the part region and the second-part candidate region. A characteristic amount correction unit () corrects the intrinsic characteristic amount of either one of the candidate regions on the basis of the length, the width, and the angle of the other candidate region. A determination unit () determines whether or not the part region and the second-part candidate region are parts that constitute a pair on the basis of the similarity between the corrected intrinsic characteristic amount and the intrinsic characteristic amount of the other candidate region.

Posture estimation device, posture estimation method, and posture estimation program

Provided is a posture estimation device with which it is possible to estimate the posture of an object having joints with a high degree of precision. A posture estimation device () carries out an estimation of a posture of an object on the basis of image data. An object having a plurality of sites which are connected by joints is photographed. The posture estimation device includes a reference model storage unit () which stores a reference model, which defines the locations of the sites, for each posture. A weighting computation unit () carries out weighting on the sites for each posture such that the weighting of concentrated parts is reduced. A posture estimation unit () estimates the posture of the object by applying the weighting and comparing the reference model with the object.

Posture estimation device, posture estimation system, and posture estimation method

A trajectory analyzing apparatus includes a matching unit that calculates the correlation between a forward trajectory obtained by tracking a moving object over pictures along the forward direction of the time axis and a backward trajectory obtained by tracking a moving object over the pictures along the backward direction of the time axis and a result output unit that outputs trajectory information indicating at least one of the forward trajectory, the backward trajectory, and an integrated trajectory obtained by integrating the forward trajectory and backward trajectory and indicating the degree of reliability of the at least one trajectory based on the calculated correlation.

Apparatus and method for analyzing trajectory

A posture estimation device, which is able to estimate the posture of a humanoid articulated body with high precision, has: a head estimation unit () that estimates the position of a person's head from image information for an image that contains a person; a foot estimation unit () that, from the image information, estimates the position of the person's foot for which the sole of the foot is parallel to the floor surface; and a posture estimation unit () that estimates the posture of the person on the basis of the relative positional relationship between the estimated head position and the estimated foot position. For example, the posture estimation unit () estimates the side on which the foot is located, with respect to the head, as the front side of the person.

The present invention provides an electronic wallet having a higher level of anonymity, security and convenience, which is capable of efficient electronizing of value information as well as which allows the user to efficiently manage the electronic value information. A presentation cardindicating the fixed property of an electronic value is digitally signed by the service provider; a variable propertyis digitally signed with the private key of that electronic value. A service control, security information, representation controland representation resourceare digitally signed by the service provider. These digital signature will be authenticated each time an electronic value object is generated.

The present invention is a posture estimation device for estimating a wide variety of 3-dimensional postures by using a skeletal model. The posture estimation device () has: a skeletal backbone estimation unit () for estimating the position of a feature location of a person within an acquired image; a location extraction unit () which generates a likelihood map indicating the certainty that a location other than the feature location of the person exists in the acquired image based on the position of the feature location of the person; and a skeletal model evaluation unit () for evaluating, based on the likelihood map, a candidate group which includes a plurality of 2-dimensional skeletal models as candidates and such that each 2-dimensional skeletal model is configured from a line group representing each location and a point group representing coupling between each location and corresponds to one posture of the person.

Posture estimation device and posture estimation method

A posture estimation device that is capable of highly precisely estimating the posture of an object comprising multiple parts. Said device () comprises: a posture information database () that for each of multiple postures, holds posture information that defines the placement of multiple parts; a fitting unit () that computes, for each of the parts in an image, a correlation level between the placement of the parts and the posture information; a difficulty level information table () that holds an estimation difficulty level that is a degree of difficulty of estimating each part position and computed on the basis of each parallel line components of each of the parts contained in the posture information; and a posture estimation unit () that to the correlation level, applies a weighting based on the estimation difficulty level, and on the basis of the weighted correlation level, estimates the posture of the object.

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Tokyo, Japan

Kyoko Kawaguchi