Jvc Kenwood Corporation

Shingo Kida

Yincheng Yang

Hideki Takehara

Hideki Aiba

Daisuke Hachiri

Kenji Kubota

Ryotaro Futamura

Hisashi Oka

Yuya Takehara

Ryouji Hoshi

Hideya Tsujii

Masahiro Kitaura

A far-infrared image acquisition unit acquires a far-infrared image. An image conversion unit converts the acquired far-infrared image into a visible light image. A visible light image trained model storage unit stores a first visible light image trained model having performed learning using the visible light image as training data. A transfer learning unit performs transfer learning on a first visible light image trained model by using the visible light image obtained by conversion as training data to generate a second visible light image trained model.

Machine learning device and far-infrared image capturing device

A far-infrared image training data acquisition unit acquires a far-infrared image in a first predetermined time zone. A visible light image training data acquisition unit acquires a visible light image in a second predetermined time zone. A visible light image generation model training unit machine-learns the far-infrared image in the first predetermined time zone and the visible light image in the second predetermined time zone as training data by a generative adversarial network, and generates a trained generation model, which generates the visible light image in the second predetermined time zone from the far-infrared image in the first predetermined time zone. Through machine learning by a generative adversarial network, the visible light image generation model training unit further generates a trained identification model, which identifies whether or not the far-infrared image is a far-infrared image captured in the first predetermined time zone.

Machine learning device and image processing device

A visible light image generation model learning unit generates a trained visible light image generation model that generates a visible light image in a second time zone from a far-infrared image in a first time zone. The visible light image generation model learning unit includes a first learning unit that machine-learns the far-infrared image in the first time zone and a far-infrared image in the second time zone as teacher data and generates a trained first generation model that generates the far-infrared image in the second time zone from the far-infrared image in the first time zone, and a second learning unit that machine-learns the far-infrared image in the second time zone and the visible light image in the second time zone as teacher data and generates a trained second generation model that generates the visible light image in the second time zone from the far-infrared image in the second time zone.

Machine learning device, image processing device, machine learning method, and machine learning program

The game device includes a reception unit that is configured to receive instruction information created when a user performs an operation on an input device triggered by a sound output while a game progresses and a derivation unit that is configured to start measuring the degree of fatigue on the basis of the instruction information received by the reception unit and derive the degree of fatigue of the user on the basis of an operation performed by the user during the started measurement of the degree of fatigue.

Game device, method, and non-transitory computer readable medium

A map data analysis unit refers to map data for a game in which a plurality of players compete in a three-dimensional space to extract positional information on each player. A feature parameter extraction unit extracts a feature parameter related to the game. A spectating area analysis unit analyzes one or more areas in a map that should be viewed by spectators, based on the positional information on each player and the feature parameter related to the game. A map data generation unit generates spectating map data by associating information indicating the area that should be viewed by spectators with the map data.

Spectating support apparatus, spectating support method, and spectating support program

An imaging unit is configured to capture an image of a subject in the water. A lighting unit is configured to illuminate the subject. A histogram generator is configured to divide a range from the minimum to the maximum luminance values of a luminance signal, included in an image signal generated by capturing the image of the subject into a plurality of luminance groups, and to generate histogram data representing the distribution of frequencies of the plurality of luminance groups. Based on the generated histogram data, a histogram analyzer is configured to analyze the distribution of frequencies of the plurality of luminance groups. According to the result of the analysis for the histogram data by the histogram analyzer, a distance controller is configured to control to adjust the distance between the subject and the lighting unit.

Underwater imaging apparatus, method for controlling an underwater imaging apparatus, and program for controlling an underwater imaging apparatus

An RB rate calculator calculates an RB rate based on an R signal and a B signal. A starting point changing unit changes a starting point based on the RB rate. An offset calculating unit calculates an offset value to adjust for selection of a basic depth model type based on a bottom high frequency component evaluation value. An adding unit adds a signal from the starting point changing unit and an offset. Another adding unit adds an offset-added signal from the adding unit and a basic depth model-composed image signal supplied from a composing unit, and generates depth estimation data wherein a degree of superimposition of object information is changed according to a composition of a composed image of basic depth models selected to be composed.

Depth estimation data generating apparatus, depth estimation data generating method, and depth estimation data generating program, and pseudo three-dimensional image generating apparatus, pseudo three-dimensional image generating method, and pseudo three-dimensional image generating program

A depth estimation data generating device includes a difference data generator and negative and positive shift amount generators. The difference data generator calculates the difference between a non-stereo image and the average value data of the average brightness of the non-stereo image to generate the difference data. The negative shift amount generator generates a negative shift amount, wherein the difference data generated using the minimum value data of the minimum brightness of the non-stereo image has a minimum value of shift amount and the shift amount approaches zero from the minimum value as the difference data approaches zero. The positive shift amount generator generates a positive shift amount, wherein the difference data generated using the maximum value data of the maximum brightness of the non-stereo image has a maximum value of shift amount and the shift amount approaches zero from the maximum value as the difference data approaches zero.

Depth estimation data generating device, computer readable recording medium having depth estimation data generating program recorded thereon, and pseudo-stereo image display device

A high frequency component detector detects a high frequency component of an R signal. A high frequency component comparator outputs a flag indicating a threshold value division range having the highest threshold value including a value of a high frequency component in a specific period. A gain calculating unit calculates a ratio as a gain, the ratio set according to the threshold value division range indicated by the flag input. A multiplying unit multiplies the R signal and the gain to generate an object signal R' wherein a concavity and convexity difference with adjacent pixels in a small region of an image is suppressed compared to the R signal.

An average value, minimum value, and maximum value data detecting unit detects an average value, a minimum value, and a maximum value in one frame of a red (R) signal. A difference value calculating unit calculates a difference value by subtracting the average value from the R signal. A processing unit estimates the average value to be a level of a background image occupying a large area of a screen, and generates a signal indicating a minimum value (concavity) at the average value and a maximum value (convexity) at the maximum or minimum value as a compensated object signal. An adding unit adds the compensated object signal to a composed image of basic depth models to generate depth estimation data for generating an image in a different viewpoint from a non-3D image, the image in the different viewpoint and the non-3D image constituting the pseudo 3D image.

Low frequency components are extracted from a video signal for pixels in a specific area of an image carried by the signal. The low frequency components are allocated, based on luminance components of the signal, to gradations ranging from maximum to minimum levels exhibited by the luminance components, luminance histogram data being produced indicating the frequency of gradations for the pixels. The gradations are allocated to the pixels based on the luminance components to produce high-frequency component histogram data indicating the frequency of gradations for specific pixels each carrying a high frequency component. The luminance histogram data is corrected in accordance with the frequency of gradations in the high-frequency component histogram data to produce corrected luminance histogram data. A gradation correction curve is produced for correcting the gradations exhibited by the signal using the corrected data. The luminance components are corrected based on the correction curve.

Video signal processing method and apparatus using histogram

An electronic appliance has a video camera for picking up an image of a user of the electronic appliance. The user's image is displayed on a display of the electronic appliance. To adjust the size of the user's image on the display, a measurement zone having a first area is defined on the display. The user holds his or her hand so that the hand is displayed on the measurement zone. A control information determination unit or a CPU generates information indicative of the ratio of the area of the hand in the measurement zone to the area of the measurement zone. According to the generated information, the CPU controls an image adjuster that optimizes the size and position of the user's image on the display.

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Tokyo, Japan

Shingo Kida