Kabushiki Kaisha Toshiba

Tatsuo Kozakaya

Tomokazu Kawahara

Nao Mishima

Mayumi Yuasa

Osamu Yamaguchi

Takayuki Sasaki

Tomoki Watanabe

Susumu Kubota

Akihisa Moriya

Jun Yamaguchi

Tomoya Tsuruyama

Toshimitsu Kaneko

Masaki Yamazaki

Quoc Viet Pham

Yasuyuki Tanaka

Satoshi Ito

Hidenori Takeshima

Yoshihiro Oba

Tomoyuki Takeguchi

Masashi Nishiyama

Masako Kashiwagi

Tatsuya Tanaka

Ryosuke Nonaka

Ryuzo Okada

Mitsuru Kanda

Masahide Nishiura

Seijiro Yoneyama

Takaomi Murakami

Masayuki Maruyama

Tomohiro Nakai

Mayu Okumura

Viet Quoc Pham

According to one embodiment, an electronic device includes one or more processors. The one or more processors obtain an image captured by a camera with a filter having a first area transmitting light of a first wavelength range and a second area transmitting light of a second wavelength range. The image includes a first color-component image based on the light of the first wavelength range and a second color-component image based on the light of the second wavelength range. The one or more processors notify a user of an effective area for calculation of depth information based on a bias of color information in the first color-component image and the second color-component image.

Electronic device and notification method

According to one embodiment, an image analysis device includes one or more processors configured to receive input of an image; calculate feature amount information indicating a feature of a region of the image; recognize a known object from the image on the basis of the feature amount information, the known object being registered in learning data of image recognition; recognize a generalization object from the image on the basis of the feature amount information, the generalization object being generalizable from the known object; and output output information on an object identified from the image as the known object or the generalization object.

Object identification image device, method, and computer program product

According to one embodiment, a learning method for causing a second statistical model to learn using a first statistical model is provided. The method includes obtaining a first learning image, cutting out each local area of the obtained first learning image, and obtaining a first prediction value output from the first statistical model by inputting each local area to the first statistical model and obtaining a second prediction value output from the second statistical model by inputting the entire area of the first learning image to the second statistical model, and causing the second statistical model to learn based on a difference between the first prediction value and the second prediction value.

Learning method, storage medium and image processing device

According to one embodiment, a robot motion planning device includes processing circuitry. The processing circuitry receives observation information obtained by observing at least part of a movable range of a robot. The processing circuitry determines, in a case where first observation information is received, a target position to which the robot is to make a motion, using an action-value function and the first observation information. The processing circuitry receives measurement information obtained by measuring a state of the robot, calculates a difference corresponding to the first observation information, using the measurement information, and determines a motion plan of a force-controlled motion of the robot, based on the target position and the difference.

Robot motion planning device, robotic system, and method

According to one embodiment, an image processing device includes a hardware processor implemented by one or more processors. The hardware processor is configured to acquire an image captured by a monocular camera. The hardware processor is configured to estimate a distance to an object based on the acquired image. The hardware processor is configured to calculate the distance to the object by considering an atmosphere failure influencing the acquired image and executing a statistical process on the estimated distance.

Image processing device and image capture apparatus

According to one embodiment, an image processing device includes storage and a processor. The storage stores a statistical model generated by learning bokeh produced in a first image affected by aberration of an optical system, the bokeh changing nonlinearly in accordance with a distance to a subject in the first image. The processor obtains a second image affected by the aberration of the optical system. The processor inputs the second image to the statistical model and obtains distance information indicating a distance to a subject in the second image.

Image processing device, ranging device and method

According to one embodiment, an optical filter associated with a lens unit having a first end includes a filter part, a filter frame, and an attachment part. The filter part includes a first filter area that transmits light rays of a first wavelength band and a second filter area that transmits light rays of a second wavelength band. The first wavelength band and the second wavelength band include a common wavelength band. The filter frame holds the filter part. The attachment part is coupled with the filter frame and is detachably attached to the first end of the lens unit. When the attachment part is attached to the first end of the lens unit, a distance between the filter part and an aperture within the lens unit is longer than a distance between the first end and the aperture.

Optical filter, imaging device, and ranging device

According to an embodiment, an information processing device includes a memory and processing circuitry. The processing circuitry is configured to receive an image and area information indicating a first area. The processing circuitry is configured to detect an object from the image. The processing circuitry is configured to, based on positional relationship between the first area and the object, estimate probability of occurrence of an event in the first area.

Information processing device, information processing method, and computer program product

According to an embodiment, an image output device includes an extractor, a search unit, an associate unit, and a controller. The extractor is configured to extract a first parameter that varies in accordance with a movement of an object from at least one first image of the object, and extract a second parameter that varies in accordance with a movement of the object from each second image of the object. The search unit is configured to search for a second parameter similar to the first parameter. The associate unit is configured to associate the first image from which the first parameter is extracted with the second image from which the second parameter that is retrieved with respect to the first parameter is extracted. The controller is configured to instruct an output unit to output an image based on the first and second images that are associated to each other.

Image output device, image output method, and computer program product

An image processing method includes: retaining marker information including markers, each of the markers having a type which is shape, pattern or color; acquiring images in which at least one of the markers is caught; referring the marker information to detect the type and a position of the marker caught in the image; and dividing the image into a plurality of divided areas on the basis of the positions of one or more markers in the image, the plurality of divided areas having no common area and each including at least one type of the marker.

Image processing apparatus for dividing an image based on markers, method thereof and program therefor

According to an embodiment, a feature point detection device includes a generator to generate a K-class classifier and perform, for T times, an operation in which a first displacement vector is obtained that approximates D number of initial feature points of each training sample classified on a class-by-class basis to true feature points; a calculator to calculate, from the first displacement vectors, second displacement label vectors each unique to one second displacement vector, and a second displacement coordinate vector common to the second displacement vectors; a classifier to apply the K-class classifiers to the input image and obtain a second displacement label vector associated with a class identifier output from each K-class classifier; an adder to add up the second displacement label vectors; and a detector to detect D number of true feature points based on the initial feature points, the added label vector, and the second displacement coordinate vector.

Feature point detection device, feature point detection method, and computer program product

An example collation apparatus includes a user information storage medium and processing circuitry. The processing circuitry is configured to at least receive a captured image including a face of a user and a non-facial object; detect a face region from the image; set an object region corresponding to the location of the non-facial object in the image; extract a face feature vector from the face region and collate the extracted face feature vector with face feature vectors stored in the user information storage medium; extract an object feature vector from the object region and collate the extracted object feature vector with object feature vectors stored in the user information storage medium; and calculate a collation result of the user using at least one of a collation result of face feature vectors and a collation result of object feature vectors.

Collation apparatus, collation method, and computer program product

When a feature vector is converted to a reduced vector, a converting unit samples N components of interest from the M components of the feature vector, executes the process of calculating one component of the reduced vector from the N components of interest by d times to create the d-dimensional reduced vector and, the converting unit (1) excludes the components within a predetermined distance D in the same row as the previous component of interest sampled at the previous sampling, (2) excludes the components in the same column as the previous component of interest including the component k rows apart and within the distance (D&#x2212;k) from the component k rows apart, and (3) samples the component of interest of this time from the remaining components after exclusion when sampling the component of interest.

Pattern recognition apparatus, method thereof, and program product therefor

According to an embodiment, a wireless communication device includes a detector to detect a mobile object by referring to sensing information; a first communicating unit to communicate mobile object information, which is related to a detection result of the mobile object, with another wireless communication device; a first calculator to calculate a moving path of the mobile object between wireless communication devices by referring to the detection result of the mobile object and mobile object information received from the other wireless communication device; a second communicating unit to communicate movement information, which is related to the moving path of the mobile object, with the other wireless communication device; and a second calculator to calculate distances between wireless communication devices that are dependent on the number of times of movement in the moving path by referring to the calculated moving path and movement information received from the other wireless communication device.

Wireless communication device, wireless communication method, and computer program product

An estimating apparatus configured to estimate a correct attribute value is provided. The estimating apparatus extracts feature quantities from an image including a person, calculates a first likelihood of the feature quantity for respective attribute classes; calculating second likelihoods for the respective attribute classes from the first likelihoods for the respective attribute classes; specifies the attribute class having the highest second likelihood; calculates an estimated attribute value of the specific attribute class and estimated attribute values of selected classes by using the feature quantity; and applies the second likelihood on the estimated attribute value of the specific attribute class as a weight, applies the second likelihoods on the estimated attribute values of the selected classes as a weight and add the same, and calculates a corrected attribute value of the specific attribute class.

Growing community of inventors

Kanagawa, Japan

Tatsuo Kozakaya