Jvc Kenwood Corporation

Yincheng Yang

Shingo Kida

Hideki Takehara

Hideki Aiba

Daisuke Hachiri

Ryotaro Futamura

Hisashi Oka

Yuya Takehara

Ryouji Hoshi

Hideya Tsujii

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

A first object detection unit detects an object from a captured image and outputs object position information, in a non-high speed processing mode. A switching unit switches to a high-speed processing mode when the first object detection unit outputs the object position information. An image trimming unit extracts a trimmed image from the captured image based on the object position information output from the first object detection unit, in the high-speed processing mode. A second object detection unit detects an object from the trimmed image and outputs the object position information. A protrusion determination unit determines whether the object thus detected protrudes from the trimmed image. When it is determined that the object thus detected protrudes from the trimmed image, the switching unit switches to the non-high speed processing mode.

Object detection apparatus, object detection method, and object detection 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.

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Yokohama, Japan

Yincheng Yang