Kabushiki Kaisha Toshiba

Yasutoyo Takeyama

Nao Mishima

Masahiro Baba

Goh Itoh

Hisashi Kobiki

Toshimitsu Kaneko

Yoshiyuki Kokojima

Kazuyasu Ohwaki

Yoshie Imai

Akiyuki Tanizawa

Masahiro Ito

Toshiyuki Ono

Tomoki Watanabe

Takuma Yamamoto

Shuhei Nitta

Ryusuke Hirai

Yukinobu Sakata

Ryosuke Nonaka

Tomohiro Yamasaki

Atsushi Yaguchi

Mikiko Karasawa

Kiichi Goto

An information processing apparatus includes a processor including hardware. The processor extracts neighboring nodes in two or more different extraction ranges for each node constituting input data of a graph structure. The processor calculates an anomaly score representing a degree of anomaly of the node for each extraction range based on a representation of a combination of the node and the neighboring nodes. The processor records each calculated anomaly score in a storage. The processor selects a maximum anomaly score among the anomaly scores recorded in the storage. The processor determines an anomaly node in the input data of the graph structure based on the selected maximum anomaly score. The processor outputs information of the anomaly node.

Information processing apparatus, information processing method, and storage medium

According to an embodiment, an information processing apparatus includes one or more processors. The one or more processors display a relation display screen including text box images each indicating one of a plurality of text ranges included in a document and a relation image indicating a relation between the plurality of text ranges.

Information processing apparatus, information processing method, and information processing system

An information processing apparatus includes a processor. The processor receives an input of a graph structure. The graph structure has nodes including text and edge. The processor assigns the nodes to one or more clusters. The processor partitions the text into words. The processor classifies the words into 1) a word representing a subject or target of an operation, 2) a word representing a content or state of the operation, and 3) other words. The processor extracts a frequent word by counting a frequency of occurrence of one or more words classified as the words representing the subject or target of the operation and extracts a frequent word by counting a frequency of occurrence of one or more words classified as the words representing the content or state of the operation, for the respective clusters.

Information processing apparatus, information processing method, and storage medium storing information processing program

A robot system according to an embodiment includes one or more processors. The processors acquire first input data predetermined as data affecting an operation of a robot. The processors calculate a calculation cost of inference processing using a machine learning model for inferring control data used for controlling the robot, on the basis of the first input data. The processors infer the control data by the machine learning model set according to the calculation cost. The processors control the robot using the inferred control data.

Robot system and driving method

According to one embodiment, an image processor includes a processor. The processor implements acquiring a first image and first information. The first information indicates a condition of a first projection surface. The first information includes a first color gamut of the first projection surface. The processor implements deriving a second color gamut according to the first color gamut derived by using the first information. The processor implements converting a chromaticity of the first image to obtain a second image. Each pixel of the second image has a color included in the second color gamut. The processor implements generating a first converted image by correcting the second image based on the first color gamut.

Image processor, image display device, and image processing method for correcting input image

According to one embodiment, a motion vector detection apparatus includes following units. The layering unit generates layers with different resolutions for each of first and second images. The first extraction unit extracts a space candidate vector. The second extraction unit extracts a time candidate vector. The third extraction unit extracts a layer candidate vector. The determination unit determines a motion vector to be assigned to the target block, based on correlations between the target block and blocks, the blocks being located in a layer which corresponds to the second image and being specified by assigning, to the target block, the space, time, and layer candidate vectors.

Motion vector detection apparatus and method

According to one embodiment, a first extraction unit extracts a first group of pixels having a specific hue from an input image. A second extraction unit extracts a second group of pixels having large distance from a reference axis on a plane defined by a chroma axis and a luminance axis from the first group. A first transform unit executes a plurality of color-transform processing to the second group and obtains a plurality of third groups of pixels as the second group transformed by each color-transform processing. A reliability calculation unit calculates a reliability of each third group. The reliability is larger when pixels of the third group are more located in shape of a straight line on the plane. A selection unit selects a color-transform processing by which the third group has the largest reliability from the plurality of color-transform processing. A second transform unit executes the color-transform processing to the input image to obtain an output image.

Apparatus and method for processing image, and apparatus for displaying image

An image processing device includes a hue calculation unit configured to calculate a hue of a pixel included in a target image; an extraction unit configured to extract a first group including a pixel, a hue of which is close to a hue of the target pixel; a chromaticity calculation unit configured to calculate a chromaticity of the pixel included in the first group and that of the target pixel; a diffuse reflection factor calculation unit configured to calculate an estimated diffuse reflection factor by using the chromaticity of the pixel included in the first group and that of the target pixel; and a separation unit configured to separate a diffuse reflection component from a pixel value of the target pixel by using the diffuse reflection factor, the diffuse reflection component representing a component in the pixel value that varies as the diffuse reflection factor varies.

Image processing device and image processing method

An image display device includes: an image input unit configured to input an image; a content categorizer configured to determine a content category of the image; a database configured to store adjustment items corresponding to each of a plurality of content categories, the adjustment items being related to sensibility words each representing an impression for images; an adjustment item display configured to display the adjustment items corresponding to the content category, the adjustment item being selectable by a user; a receiver configured to receive set values of the adjustment items selected from the user; an image quality parameter calculator configured to calculate image quality parameters in accordance with the set values of the adjustment items; an image processor configured to perform image processing on the image in accordance with the image quality parameters; and an image display configured to display the processed image.

Image display device and image display method

An apparatus for creating an interpolation frame includes a first computing unit that computes a first motion vector in relation to a first block in a first reference frame, a second extracting unit that extracts a second block in a second reference frame based on the first motion vector, a first calculating unit that calculates a correlation between the first block and the second block, a third extracting unit that extracts a third block that is shifted from the second block by a certain number of pixels, a second calculating unit that calculates a correlation between the first block and the third block, and a third computing unit that computes a motion vector for an interpolation block based on a most-highly correlated block-pair.

Apparatus, method, and computer program product for detecting motion vector and for creating interpolation frame

An interpolated image generating apparatus calculates a motion vector reliability level indicating reliability of a motion vector used for inserting an interpolated image, the value of the motion vector reliability level being determined in such a manner that the stronger the correlation is between a source image area and a destination image area that are brought into correspondence with each other by the motion vector, the larger is the value. The apparatus also calculates a failure-preventing vector reliability level indicating reliability of a predetermined failure-preventing vector used for preventing image failures. The apparatus obtains a motion compensated image based on the motion vector, obtains a failure-preventing image based on the failure-preventing vector, and blends the motion compensated image and the failure-preventing image according to a weighted average thereof calculated by using a weighting factor based on the motion vector reliability level and the failure-preventing vector reliability level.

Apparatus, method, and computer program product for generating interpolated images

An area detecting unit detects a first matching area in a first reference block in the first reference frame, a second matching area in a second reference block in the second reference frame. A settling unit settles a secondary motion vector to be assigned to a mismatching area in the each interpolation block, based on surrounding interpolation blocks around the mismatching area. A motion compensating unit assigns an image to the interpolation frame based on the primary motion vector and the secondary motion vector.

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Kanagawa, Japan

Yasutoyo Takeyama