Abstract: A vehicle display control device, includes a memory; and a processor coupled to the memory. The processor is configured to cause a display unit provided in a vehicle cabin to display an inducement image requesting that an occupant grip a steering wheel in a case in which it is necessary for the occupant to grip the steering wheel, and cause the display unit to display the inducement image more prominently in a case of requesting that the occupant grip the steering wheel before the vehicle switches from an autonomous driving mode to a manual driving mode as the vehicle is continuing to drive autonomously, than in a case of requesting that the occupant continue to grip the steering wheel as the vehicle is continuing to drive autonomously.

Abstract: Provided is an improved estimation and identification device that estimates the position of a plurality of identification targets and identifies the plurality of identification targets.

Abstract: A method for determining whether a first medical image and a second medical image are medical images of the same patient, comprising selecting a first region in the first medical image; selecting a second region in the second medical image; determining a common region based on a boundary of the first region and a boundary of the second region; calculating a correlation coefficient based on image data from the first medical image in the common region and image data from the second medical image in the common region; and determining whether the first medical image and the second medical image are medical images of the same patient based on the correlation coefficient. Biological fingerprints from parts of chest radiographs such as thoracic fields, cardiac shadows, lung apices, superior mediastinum, and the right lower lung that includes the costophrenic angle, are used for the purpose of patient recognition and identification.

Abstract: A method, system and computer readable medium for a computer-automated method for identifying given image data, including obtaining template image data corresponding to said given image data; calculating correlation values between the given image data and said template image data; and identifying said image data based on the correlation values calculated in the calculating step.

Abstract: A method for determining whether a first medical image and a second medical image are medical images of the same patient, comprising selecting a first region in the first medical image; selecting a second region in the second medical image; determining a common region based on a boundary of the first region and a boundary of the second region; calculating a correlation coefficient based on image data from the first medical image in the common region and image data from the second medical image in the common region; and determining whether the first medical image and the second medical image are medical images of the same patient based on the correlation coefficient. Biological fingerprints from parts of chest radiographs such as thoracic fields, cardiac shadows, lung apices, superior mediastinum, and the right lower lung that includes the costophrenic angle, are used for the purpose of patient recognition and identification.

Abstract: A method, system and computer readable medium for a computer-automated method for identifying given image data, including obtaining template image data corresponding to said given image data; calculating correlation values between the given image data and said template image data; and identifying said image data based on the correlation values calculated in the calculating step.

Abstract: A computerized method for the detection and characterization of disease in an image derived from a chest radiograph, wherein an image in the chest radiograph is processed to determine the ribcage boundary, including lung top edges, right and left ribcage edges, and right and left hemidiaphragm edges. Texture measures including RMS variations of pixel values within regions of interest are converted to relative exposures and corrected for system noise existing in the system used to produce the image. Texture and/or geometric pattern indices are produced. A histogram(s) of the produced index (indices) is produced and values of the histograms) are applied as inputs to a trained artificial neural network, which classifies the image as normal or abnormal.

Abstract: A computerized method for the detection and characterization of disease in an image derived from a chest radiograph, wherein an image in the chest radiograph is processed to determine the ribcage boundary, including lung top edges, right and left ribcage edges, and right and left hemidiaphragm edges. Texture measures including RMS variations of pixel values within regions of interest are converted to relative exposures and corrected for system noise existing in the system used to produce the image. Texture and/or geometric pattern indices are produced. A histogram(s) of the produced index (indices) is produced and values of the histogram(s) are applied as inputs to a trained artificial neural network, which classifies the image as normal or abnormal.