Panasonic Intellectual Property Management Co., Ltd.

Panasonic Corporation

Matsushita Electric Industrial Co., Ltd.

Nitto-denko Corporation

Kyocera Document Solutions Inc.

Panasonic Intellectual Property Corporation of America

Tsubakimoto Chain Company

Satoshi Sato

Kunio Nobori

Takeo Azuma

Nobuhiko Wakai

Katsuhiro Kanamori

Ayako Maruyama

Mikiya Nakata

Yoshihiko Matsukawa

Shusaku Okamoto

Osamu Yamada

Tomonobu Naruoka

Jun Ozawa

Hiroki Takeuchi

Kohsuke Yoshioka

Yoshinao Kawai

Noritaka Shimizu

Takaaki Amada

Yoko Kawai

Takeshi Murai

Hideto Motomura

Yasunari Ooyabu

Yasunori Ishii

Takamasa Ando

Masamichi Nakagawa

Hisataka Ito

Yuki Yamamoto

Yukihiro Shibata

Yusuke Okazaki

Akira Ohashi

Koji Tagaki

Kazutoyo Takata

Hiroyuki Motoyama

Yuki Shinbori

Kenji Kondo

Katsuji Aoki

Hisashi Watanabe

Tsuguhiro Korenaga

Pongsak Lasang

Yumiko Kato

Toshiya Fujii

Ryota Fujimura

Yoshihide Sawada

Ayako Komoto

Changxin Zhou

Taro Imagawa

Yasuhisa Inada

Sanshiro Shishido

Atsushi Morimura

Hiroaki Okayama

Toshiki Naito

Katsuya Nozawa

Takeyoshi Tokuhara

Sotaro Tsukizawa

Toru Tanigawa

Masaaki Yanagida

Masahiko Saito

Shinichi Machida

Masumi Izuchi

Shinya Ota

Mitsuru Harada

Natsuki Saito

Takrit Tanasnitikul

Takuya Fujimaru

Yoshihiro Kiyomura

Yasuhiro Izumi

Yuji Fukuike

Tomohide Takada

Yasuhiro Morinaka

Yoshito Yoneda

Yasunori Tabaru

A depth acquisition device includes memory and processor performing: acquiring, from the memory, intensities of infrared light emitted from a light source and measured by imaging with the infrared light reflected on a subject by pixels in an imaging element; generating a depth image by calculating a distance to the subject as a depth for each pixel based on an intensity received by the pixel; acquiring, from the memory, a visible light image generated by imaging, with visible light, a substantially same scene with a substantially same viewpoint and at a substantially same timing as those of imaging the infrared light image; detecting, from the visible light image, an edge region including an edge along a direction perpendicular to a direction of movement of the visible light image; and correcting, in the depth image, a depth of a target region corresponding to the edge region in the depth image.

Depth acquisition device and depth acquisition method

A depth acquisition device includes a memory and a processor. The processor performs: acquiring timing information indicating a timing at which a light source irradiates a subject with infrared light; acquiring, from the memory, an infrared light image generated by imaging a scene including the subject with the infrared light according to the timing indicated by the timing information; acquiring, from the memory, a visible light image generated by imaging a substantially same scene as the scene of the infrared light image, with visible light from a substantially same viewpoint as a viewpoint of imaging the infrared light image at a substantially same time as a time of imaging the infrared light image; detecting a flare region from the infrared light image; and estimating a depth of the flare region based on the infrared light image, the visible light image, and the flare region.

Imaging device, imaging method, and information processing device

A control device includes a parameter acquisition unit that acquires an augmentation parameter regarding geometric augmentation or optical augmentation of training data to be used for training a machine learning model, a display controller that causes a display device to display an image based on the augmentation parameter, an imaging controller that causes an imaging device to capture the displayed image, and an output unit that outputs the captured image obtained by the imaging device as the training data.

Control system and control method

An object recognition apparatus includes a light source, an image sensor, a control circuit, and a signal processing circuit. The control circuit causes the light source to emit first light toward a scene and subsequently emit second light toward the scene, the first light having a first spatial distribution, the second light having a second spatial distribution. The control circuit causes the image sensor to detect first reflected light and second reflected light in the same exposure period, the first reflected light being caused by reflection of the first light from the scene, the second reflected light being caused by reflection of the second light from the scene. The signal processing circuit recognizes an object included in a scene based on photodetection data output from the image sensor, and based on an object recognition model pre-trained by a machine learning algorithm.

Object recognition apparatus, object recognition method, and non-transitory computer readable medium

Provided is a learning device that acquires computational imaging information of a computational imaging camera that captures an image with blurring; acquires a normal image captured by a normal camera that captures an image without blurring or an image with blurring smaller than that of the computational imaging camera, and a correct answer label assigned to the normal image; generates an image with blurring based on the computational imaging information and the normal image; and performs machine learning using the image with blurring and the correct answer label to create an image identification model for identifying an image captured by the computational imaging camera.

Learning method, image identification method, learning device, and image identification system

An image processing device includes a noise adder that obtains a captured image from an image capturing device including a mask having at least one aperture, an MPH information obtainer that obtains aperture pattern information corresponding to the pattern of the at least one aperture, the noise adder that adds, to the captured image, noise determined according to the aperture pattern information, and a transmitter that outputs the noise added captured image.

Image processing device, method for training machine learning model, identification device, and image processing method

An interval between a pinhole and a pinhole is set to a first interval at which a degree of superimposition of subject images captured through the corresponding pinholes falls within a predetermined range when an image of a subject located at a distance less than a predetermined distance from the multi-pinhole camera is captured. An interval between the pinhole and a pinhole is set to a second interval narrower than the first interval at which a degree of superimposition of subject images captured through the corresponding pinholes falls within a predetermined range when an image of the subject located at a distance equal to or more than the predetermined distance from the multi-pinhole camera is captured.

Multi-pinhole camera and image identification system

A range-information acquiring apparatus includes a light source, an image sensor, a control circuit, and a signal processing circuit. The control circuit causes the light source to emit first light toward a scene and subsequently emit second light toward the scene, the first light having a first spatial distribution, the second light having a second spatial distribution. The control circuit causes at least a portion of plural photodetector elements of the photodetector device to detect first reflected light and second reflected light in the same exposure period, the first reflected light being caused by reflection of the first light from the scene, the second reflected light being caused by reflection of the second light from the scene. The signal processing circuit generates range data based on photodetection data output from the photodetector elements of the photodetector device.

Range-information acquiring apparatus, range-information acquiring method, and non-transitory computer-readable medium

An image generating apparatus generates an image to be displayed on a display and includes at least one memory and a control circuit. The control circuit acquires a plurality of camera images captured by a plurality of cameras installed in a vehicle, calculates a distance between one of the cameras and a target to be projected in in the camera images, detects a position of a light-transmissive object or a reflective object in the camera images, and generates an image from a point of view that is different from points of view of the plurality of camera images by using the plurality of camera images and the distance, the generated image including a predetermined image that is displayed at the position of the light-transmissive object or the reflective object.

Image generating apparatus, image generating method, and recording medium

A crossing point detector includes memory and a crossing point detection unit that reads out a square image from a captured image in the memory, and detects a crossing point of two boundary lines in a checker pattern depicted in the square image. The crossing point detection unit decides multiple parameters of a function model treating two-dimensional image coordinates as variables, the parameters optimizing an evaluation value based on a difference between corresponding pixel values represented by the function model and the square image, respectively, and computes the position of a crossing point of two straight lines expressed by the decided multiple parameters to thereby detect the crossing point with subpixel precision. The function model uses a curved surface that is at least first-order differentiable to express pixel values at respective positions in a two-dimensional coordinate system at the boundary between black and white regions.

Crossing point detector, camera calibration system, crossing point detection method, camera calibration method, and recording medium

A depth acquisition device includes a memory and a processor. The processor performs; acquiring timing information indicating a timing at which a light source irradiates a subject with infrared light; acquiring an infrared light image stored in the memory, the infrared light image being generated by imaging a scene including the subject with the infrared light according to the timing indicated by the timing information; acquiring a visible light image stored in the memory, the visible light image being generated by imaging a substantially same scene as that of the infrared light image, with visible light from a substantially same viewpoint and at a substantially same imaging time of those of the infrared light image; detecting a dust region showing dust from the infrared light image; and estimating a depth of the dust region based on the infrared light image, the visible light image, and the dust region.

Depth acquisition device and depth acquisition method including estimating a depth of a dust region based on a visible light image

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Kyoto, Japan

Satoshi Sato