Nec Corporation

Daiwa Seiko, Inc.

Tokyo Institute of Technology

Globeride, Inc.

Kyushu University

Sankyo Oilless Industry, Inc.

Globride, Inc.

Takashi Shibata

Shoji Yachida

Keiichi Chono

Masato Tsukada

Yuka Ogino

Chisato Funayama

Yasuhiko Yoshida

Emi Kitagawa

Yusuke Mori

Hiroshi Imai

Akihiko Iketani

Ryuichi Akashi

Masayuki Tanaka

Toru Takahashi

Masatoshi Okutomi

Wataru Tsutsumi

Hiroyoshi Miyano

Kyoichi Kaneko

Atsushi Sato

Hiroshi Yamada

Rui Ishiyama

Takuya Ogawa

Soma Shiraishi

Eiji Shinohara

Masato Toda

Yukio Ito

Katsuhiko Takahashi

Kota Iwamoto

Yuichi Abe

Shinichi Asano

Kenta Senzaki

Shuji Senda

Azusa Sawada

Masashi Fujioka

Keiko Inoue

Shigeaki Namiki

Takeshige Ohara

Masayoshi Fujii

Manabu Matsuhashi

Kapik Lee

Masanori Kato

Akira Kobayashi

Gaku Nakano

Kosuke Yoshimi

Takashi Kondo

Eiji Kaneko

Shinji Takeuchi

Kengo Makino

Hiromi Takahashi

Hironori Horie

Tomohiro Murayama

Tomoyuki Amano

Tetsuaki Suzuki

Takehito Hayami

Shohei Ikeda

Takashi Kondoh

Kentarou Kudou

Muneaki Tsukihiji

Sakae Kobayashi

Atsushi Morita

Ryou Nagai

Atsushi Shimada

Shinichiro Nagata

Naoya Oodama

Katsuya Masaki

Motoki Masaka

Emi Inui

Kazuto Takizawa

Tsubasa Minematsu

Rin-Ichiro Taniguchi

Lee Kapik

Hiroyoshi Kako

Kouichi Taguchi

Masamitsu Miyajima

Thapanapong Rukkanchanunt

Shizu Harada

The image recognition apparatus includes an image selection unit and a recognition unit. The image selection unit selects a feature image representing a feature portion of an object from among captured images of a time series in which the object is photographed. For example, the feature image corresponds to an image showing an abnormal portion. The recognition unit performs a recognition process of the object using the feature image. By the recognition process, an abnormality of the object is detected.

Image recognition apparatus, image recognition method, and recording medium

The learning device learns the target detection model used in the radar device. The learning device includes an acquisition unit, a learning data generation unit, and a learning processing unit. The acquisition unit acquires a reception signal generated based on the received wave and a tracking signal generated based on the reception signal from the radar device. The learning data generation unit generates learning data using the reception signal and the tracking signal. The learning processing unit learns a target detection model that detects a target from the reception signal, using the learning data.

Learning device, learning method, recording medium, and radar device

According to the present disclosure, it is an object to provide, in particular, an image processing apparatus, an image processing system, an image processing method, and a non-transitory computer-readable medium storing an image processing program therein, capable of tracking an object in a video with high accuracy. An image processing apparatus according to the present disclosure includes: image feature calculation means for outputting an image feature of an object using a time-series image of the object; object identification means for outputting an identification result obtained by identifying an object using the image feature and a tracking result of the object; kinetic model selection means for selecting an appropriate kinetic model from a plurality of kinetic models based on the identification result and the tracking result; and object tracking means for tracking the object in time series and calculating the tracking result, from the identified object.

Image processing apparatus, image processing system, image processing method, and non-transitory computer-readable medium storing image processing program therein

An authentication device includes an image acquisition unit, an identification unit, and an authentication unit. The image acquisition unit acquires an image of an eye of a subject. The identification unit identifies the colored pattern of a colored contact lens worn by the subject by comparing a reference image with the image of the eye. The authentication unit identifies the subject, using a feature in a region other than a colored region of the colored pattern in the iris region of the eye.

Authentication device, authentication method, and recording medium

An image processing apparatus includes: a three-dimensional point cloud segmentation unit for segmenting a three-dimensional point cloud of an object using clustering parameters so that the smaller the distance between points in the three-dimensional point cloud is, the more likely the points are to be clustered into the same cluster; a cluster mapping unit for generating a mapped point cloud by mapping clusters of the three-dimensional point cloud to a two-dimensional image based on the correspondence between the coordinates of the three-dimensional point cloud and the coordinates of pixels of the two-dimensional image; a label matching unit for assigning labels to points in the mapped point cloud based on a target label and position information in the two-dimensional image; a matching result voting unit for voting for labels of the points; and a label determination unit for determining the label for the points based on voting results.

Determination, for each point in mapping point cloud, of one of the labels assigned to the point based on voting results of the labels

A control device according to an aspect of the present disclosure includes: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: acquire an input image including an eye region that is a region of an eye part; estimate illuminance of the eye part from the acquired input image; and determine a light amount of illumination of visible light with which the eye part is irradiated in such a way that a pupil size of the eye part satisfies a size condition based on an illuminance size relationship that is a relationship between the illuminance and the pupil size.

Determination of light amount of illumination of visible light with which the eye part is irradiated such that pupil size satisfies a size condition

A learning apparatus includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from sets of attribute-attached image data for each combination of different attributes by using the sets of attribute-attached image data to which pieces of attribute information are added. The case example storage unit calculates feature vectors from sets of case example image data, and stores the feature vectors as case examples associated with the metric space.

Learning apparatus, learning method, inference apparatus, inference method, and recording medium

The disclosure is detecting an authentication target who is moving in a predetermined direction in a video; inputting a first image in which an entire body of the target; calculating characteristic information from an image of the entire body in the first image, comparing the calculated characteristic information with characteristic information of the entire body stored in first memory that stores characteristic information of entire bodies of targets, and extracting candidate information of the targets from the first memory based on a first authentication result; inputting a second image in which an iris of the target; and comparing characteristic information of irises stored in second memory that stores the characteristic information of the irises of targets with characteristic information of an iris from the second image, calculating a verification score, executing second authentication on the target in the second image based on the verification score, and outputting an authentication result.

The learning device includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from attributed image data, for each combination of different attributes, using the attributed image data to which attribute information is assigned. The case example storage unit computes the feature vector from the image data for case example to store the computed feature vector as a case example associated with the metric space, and stores additional information associated with the case example.

Object recognition device, method, and computer-readable medium

An information providing device according to one aspect of the present disclosure includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: receive a face image; determine whether a person in the face image is unsuitable for iris data acquisition based on the face image; and output information based on determining that the person is unsuitable for the iris data acquisition when the person is determined to be unsuitable for the iris data acquisition.

Information providing device, information providing method, and storage medium

A surface property estimation system includes an image acquisition means for acquiring an image of a surface of an object, an estimation means for estimating a surface property from the acquired image by using an estimation model obtained through machine learning with use of an image of a surface of an object and a surface property shown by the image as training data, an extraction means for extracting, from the acquired image, a feature amount unique to the image, and a registration means for storing the estimated surface property and the extracted feature amount in a storage means in association with each other.

Surface property estimation system

The learning apparatus classifies target domain data into (N-c) classes based on unique features of the target domain data, classifies source domain data into N classes based on unique features of the source domain data, and classifies the target domain data and the source domain data into the N classes based on common features of the target domain data and the source domain data. Also, the learning apparatus calculates a first distance between the common features of the target domain data and the source domain data, and calculates a second distance between the unique features of the target domain data and the source domain data. Next, the learning apparatus updates parameters of a common feature extraction unit based on the first distance, and updates parameters of a target domain feature extraction unit and a source domain feature extraction unit based on the second distance.

Learning apparatus, learning method, and recording medium

A learning apparatus () according to the present invention includes a detection unit () that detects, as a candidate region of a learning target, a region detected by one of first detection processing of detecting an object region from a predetermined image and second detection processing of detecting a change region from background image information and the image, and not detected by the other, an output unit () that outputs at least a part of the candidate region as a labeling target, and a learning unit () that learns a model for performing the first detection processing or a model for performing the second detection processing by using the labeled candidate region as learning data.

Learning apparatus, learning method, and non-transitory computer-readable medium in which learning program has been stored

Growing community of inventors

Tokyo, Japan

Takashi Shibata