Nec Corporation

The University of Electro-communications

Takahiro Toizumi

Eisuke Saneyoshi

Ryo Hashimoto

Koji Kudo

Hisato Sakuma

Hitoshi Yano

Kosuke Homma

Yuma Iwasaki

Masato Tsukada

Yuka Ogino

Ryota Suzuki

Ryuichi Akashi

Shigeru Koumoto

Koichi Takahashi

Yuzo Senda

Katsuya Suzuki

Chisato Funayama

Kazuhiko Aoki

Yoshiho Yanagita

Kazutoshi Sagi

Keiichi Chono

Ryo Yamakabe

Masumi Ishikawa

Akira Shoujiguchi

Yu Kiyokawa

Alexander Viehweider

Sakiko Mishima

Rie Iwasaki

Daisuke Uenoyama

Masatsugu Ichino

Tsubasa Boura

A learning system includes: an area candidate generation unit that generates an area candidate that is a candidate for an area in which an object is detected from an image; a ground truth area acquisition unit that obtains information about a ground truth area including the object; and a ground truth target generation unit that generates a ground truth target used for learning of an object detection unit that detects the object from the image, on the basis of a score indicating a degree of area matching between the area candidate and the ground truth area and a size of the ground truth area. According to such a learning system, it is possible to properly perform the learning in accordance with the size of the ground truth area.

Learning system, object detection system, learning method, and computer program

An image processing system includes a detection unit, an identification unit, and a feature extraction unit. The detection unit detects, from a first image in which a target person is captured, a candidate region being an image region estimated to represent an eye of the target person, based on a first evaluation value. The identification unit identifies, from the detected candidate region, an eye region being an image region that represents the eye, based on a second evaluation value. The feature extraction unit extracts a feature value of the identified eye region. The first evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the first image. The second evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the detected candidate region.

Image processing system, imaging system, image processing method, and non-transitory computer-readable medium

An information processing apparatus () includes: a reducing unit () that generates a second image (IMG) by reducing a first image (MG) in which a target object is included; a first extracting unit () that extracts, as a first key point, a key point (KP) of the target object from the second image; a setting unit () that sets a target area (TA) that designates a part of the first image based on the first key point; and a second extracting unit () that extracts, as a second key point, a key point of the target object from a target image (IMG_TA) of the first image that is included in the target area.

Information processing apparatus, information processing method and recording medium

An information processing apparatus () includes: a reducing unit () that generates a second image (IMG) by reducing a first image (MGI) in which a target object is included; a first extracting unit () that extracts, as a first key point, a key point (KP) of the target object from the second image; a setting unit () that sets a target area (TA) that designates a part of the first image based on the first key point; and a second extracting unit () that extracts, as a second key point, a key point of the target object from a target image (IMGTA) of the first image that is included in the target area.

A learning system () comprises: a generation unit () configured to synthesize with an original image including a living body, information on a reflection component of light not derived from the original image to generate a synthetic image; and a learning unit () configured to perform learning for an estimation model () based on information on a specific portion of the living body estimated from the synthetic image by the estimation model and correct answer information showing a correct answer of the information on the specific portion in the original image. According to this learning system, it is possible to learn the estimation model appropriately.

Correction of answer information on original image portion

An information processing apparatus () includes: an extracting unit () that extracts a key point (KP) of a target object as a target key point (KP) from an input image (IMG); and a calculating unit () that calculates, as a score (SC) of the input image from which the target key point is extracted, an index value related to a reliability of the target key point based on the target key point.

Processing apparatus, information processing method and recording medium

The optical control apparatus includes a light source, a light collecting section, and an optical path control section. The light source emits light. The light collecting section collects the light emitted from the light source and illuminates the light onto an object.

Optical control apparatus and optical control method

A feature conversion learning device is configured to acquire a first image, reduce the first image to a second image having lower resolution than the first image, enlarge the second image to a third image having the same resolution as the first image, extract a first feature that is a feature of the first image and a second feature, convert the second feature into a third feature, and learn a feature conversion method based on a result of comparing the first feature with the third feature.

Feature conversion learning device, authentication device, feature conversion learning method, authentication method, and recording medium

An image processing system according to an example aspect of the present disclosure includes: an image acquisition unit configured to acquire a monochrome image; a pixel value correction unit configured to correct a pixel value of the monochrome image based on information related to a pixel value; and a colorization generation unit configured to generate a colorized image corresponding to the monochrome image from the corrected monochrome image by using a colorization prediction model trained by machine learning. With the present disclosure, it is possible to improve the reproduction accuracy of a color in colorization of a monochrome image.

Image processing system configured to generate a colorized image

A parameter optimization system () includes: an image sensor () having at least one sensing parameter; a parameter setting unit () configured to be able to change the sensing parameter; a score calculation unit () configured to calculate a score from an image acquired by the image sensor; and a parameter determination unit () configured to determine a right parameter value that is a value of the sensing parameter at which the score is relatively high, based on a value of the sensing parameter and the score corresponding to the value of the sensing parameter. According to such a parameter optimization system, the sensing parameter of the image sensor can be set to an appropriate value.

Optimizing sensing parameter in imaging device using scores

Provided are a sound model generation device and the like that make it possible to more easily generate a sound model capable of distinguishing sound events using a plurality of features. A concatenating unit concatenates a plurality of features of sound signals that are learning data and generates a concatenated feature. A learning unit learns the generated concatenated feature for generating a sound model for distinguishing a sound event from a sound signal.

Sound model generation device, sound model generation method, and recording medium

An image processing system according to the present disclosure includes: an image acquisition unit configured to acquire a monochrome image of a subject including a target part; a hint acquisition unit configured to acquire a category of the target part and a first color hint indicating a color of the target part; a hint conversion unit configured to convert the first color hint into a second color hint indicating the color of the target part based on the acquired category; and a colorization generation unit configured to generate a colorized image corresponding to the monochrome image from the monochrome image and the second color hint of the target part by using a prediction model trained by machine learning.

Image processing system, image processing method, and non-transitory computer readable medium for generating a colorized image

An iris recognition apparatus for performing recognition of a target person based on an iris pattern of the target person, is provided with: an imaging device that performs an imaging processing for sequentially outputting imaged images while changing a focal position; and a determining device that starts a focusing determination for determining whether or not an image is a focused image before the imaging device completes the imaging processing.

Iris recognition apparatus, iris recognition method, computer program and recording medium

A region determining device includes a likelihood calculating unit that calculates a likelihood of wetness of a pixel in an image including a plurality of frames, a non-water pixel measuring unit that measures the number of non-water pixels in which the likelihood falls within a first non-water range in any one of determination regions to be determined in the respective frames, a boundary pixel measuring unit that measures the number of boundary pixels in which the likelihood falls within a first water range in any one of the determination regions and the likelihood falls within a second non-water range in any one of the determination regions, and a region determining unit that determines whether the determination region satisfies a predetermined determination condition based on the number of non-water pixels and the number of boundary pixels.

Growing community of inventors

Tokyo, Japan

Takahiro Toizumi