Abstract: A display unit color-correction method includes: a first step of displaying a prescribed image on a display unit of a portable communication terminal that includes an imaging unit and the display unit; a second step of acquiring imaging data by imaging a color chart, which is a printed matter, and the display unit with the prescribed image displayed thereon together or individually by the imaging unit; and a third step of correcting a color of the display unit by using color information on the prescribed image in the imaging data and color information on a color in the color chart in the imaging data.

Abstract: When relation between a color of a color chip (a patch) in a color chart and a color of a color chip in an image-captured color chart is calculated, the color chip of the image-captured color chart was manually extracted by operating a keyboard or a mouse. Such an operation had a problem that an extraction error may occur, and that it takes time to manually extract all color chips in the color chart. The present invention solved the problem by a color chart in which recognition can be easily and automatically executed. The color chart includes a plurality of patches, and an automatic recognition marker configured to automatically recognize at least one of the plurality of patches.

Abstract: An object of the present invention is to acquire biometric information on small animals without being affected by the movements of the small animals and also without giving stress to the small animals. The present invention comprises the steps of: acquiring pieces of image information on a hand and/or a foot of a quadrupedal small animal; extracting a predetermined piece of signal information from among the pieces of image information that have been acquired; and processing the predetermined piece of the signal information which has been extracted so as to acquire biometric information on the small animal. The present invention further comprises the step of capturing an image of an abdominal side of the small animal, and the step of capturing an image of the small animal from below a location of the small animal.

Abstract: An object of the invention is to provide an image display system and the like useful for a remote diagnosis and treatment using color information such as a skin color and a tongue color of a patient. There is provided an image display system including: a color chart including a plurality of patches that include at least three patches selected from a group consisting of first to seventh patches having specific colors; an imaging device configured to simultaneously image the color chart and a person to be imaged and acquire image data; and a display device configured to receive the image data and display the image data as an image on a display unit.

Abstract: A manufacturing method for manufacturing a colored product, where using inkjet heads which are a plurality of color ink heads, and an inkjet head, which is a colorless ink head, a plurality of coloring regions are formed in an overlapping manner in a normal direction, a surface side colored region, which is a region located closest to the surface of the colored product is formed using at least the color ink ejected from any of the inkjet heads, and an inner colored region, which is a region located farther from the surface of the colored product than the surface side colored region is formed using at least the color ink ejected from any of the inkjet heads and the colorless ink ejected from the inkjet head.

Abstract: A layer configuration prediction method is provided and includes: a specimen production step of producing multiple specimens by depositing layers of a material in configurations different from each other; a specimen measurement step of performing, on each specimen, measurement to acquire a texture parameter corresponding to a texture; a learning step of causing a computer to perform machine learning of a relation between each of the specimens and the texture parameter; a setting parameter calculation step of calculating a setting parameter corresponding to the texture set to a computer graphics image; and a layer configuration acquisition step of providing the setting parameter as an input to the computer having been caused to perform the machine learning, and acquiring an output representing the layering pattern of layers of the material corresponding to the setting parameter.

Abstract: An image processing apparatus includes a processor including hardware. The processor is configured to: acquire video data; estimate a spatial distribution of a predetermined biological component for which a component spectrum temporally varies with respect to image data of a predetermined frame among a plurality of frames included in the video data; generate biological component video data in which the biological component is extracted from the video data based on the estimated spatial distribution of the biological component in the image data of the predetermined frame; and generate periodic variation video data of the biological component by applying, to the biological component video data, a filter for extracting a predetermined frequency component.

Abstract: A manufacturing method for manufacturing a colored product, where using inkjet heads which are a plurality of color ink heads, and an inkjet head, which is a colorless ink head, a plurality of coloring regions are formed in an overlapping manner in a normal direction, a surface side colored region, which is a region located closest to the surface of the colored product is formed using at least the color ink ejected from any of the inkjet heads, and an inner colored region, which is a region located farther from the surface of the colored product than the surface side colored region is formed using at least the color ink ejected from any of the inkjet heads and the colorless ink ejected from the inkjet head.

Abstract: A layer configuration prediction method is provided and includes: a specimen production step of producing multiple specimens by depositing layers of a material in configurations different from each other; a specimen measurement step of performing, on each specimen, measurement to acquire a texture parameter corresponding to a texture; a learning step of causing a computer to perform machine learning of a relation between each of the specimens and the texture parameter; a setting parameter calculation step of calculating a setting parameter corresponding to the texture set to a computer graphics image; and a layer configuration acquisition step of providing the setting parameter as an input to the computer having been caused to perform the machine learning, and acquiring an output representing the layering pattern of layers of the material corresponding to the setting parameter.

Abstract: An image processing apparatus includes a processor including hardware. The processor is configured to: acquire video data; estimate a spatial distribution of a predetermined biological component for which a component spectrum temporally varies with respect to image data of a predetermined frame among a plurality of frames included in the video data; generate biological component video data in which the biological component is extracted from the video data based on the estimated spatial distribution of the biological component in the image data of the predetermined frame; and generate periodic variation video data of the biological component by applying, to the biological component video data, a filter for extracting a predetermined frequency component.

Abstract: Provided is an image processing apparatus including a transformation unit that is configured to deform images so that corresponding points in a plurality of images obtained from several viewpoints with respect to the same subject are matched; a separating unit that is configured to separate specular-reflection components from the plurality of images transformed by the transformation unit and create an image from which the specular-reflection component is removed; and an image reconstructing unit that is configured to combine at least one of the specular-reflection components of the plurality of images, separated by the separating unit, and the image from which the specular-reflection component is removed.

Abstract: An image processing apparatus is intended to find, even when shooting a subject having a depth, respective reflection characteristics of portions of the subject with high accuracy, and includes a high-luminance region extraction unit which extracts high-luminance regions having a higher luminance than a predetermined threshold value in a plurality of image signals obtained by irradiating the subject with light emitted from a known light source and shooting reflected light from the subject at a plurality of viewpoints, a local region extraction unit which extracts local regions respectively most focused on in the portions of the subject using the plurality of high-luminance regions, and a reflection characteristic estimation unit which estimates a reflection characteristic of the subject using respective light source characteristics of the high-luminance regions in the local regions.

Abstract: An image processing apparatus is intended to find, even when shooting a subject having a depth, respective reflection characteristics of portions of the subject with high accuracy, and includes a high-luminance region extraction unit which extracts high-luminance regions having a higher luminance than a predetermined threshold value in a plurality of image signals obtained by irradiating the subject with light emitted from a known light source and shooting reflected light from the subject at a plurality of viewpoints, a local region extraction unit which extracts local regions respectively most focused on in the portions of the subject using the plurality of high-luminance regions, and a reflection characteristic estimation unit which estimates a reflection characteristic of the subject using respective light source characteristics of the high-luminance regions in the local regions.

Abstract: Provided is an image processing apparatus including a transformation unit that is configured to deform images so that corresponding points in a plurality of images obtained from several viewpoints with respect to the same subject are matched; a separating unit that is configured to separate specular-reflection components from the plurality of images transformed by the transformation unit and create an image from which the specular-reflection component is removed; and an image reconstructing unit that is configured to combine at least one of the specular-reflection components of the plurality of images, separated by the separating unit, and the image from which the specular-reflection component is removed.

Abstract: An image processing apparatus includes a skin information obtaining unit that extracts a skin area from an image and obtains skin information from the skin area; an independent component extracting unit that extracts independent components constituting the skin area from the skin information; and a bias calculation unit that calculates, based on the independent components, a bias representing color information of a light source used to illuminate the skin area when the image is captured.

Abstract: An image processing apparatus includes a skin information obtaining unit that extracts a skin area from an image and obtains skin information from the skin area; an independent component extracting unit that extracts independent components constituting the skin area from the skin information; and a bias calculation unit that calculates, based on the independent components, a bias representing color information of a light source used to illuminate the skin area when the image is captured.

Abstract: The imaging apparatus includes a light application unit that applies light to an object to be imaged, a first converging unit that converges diffuse reflection rays from the object to be imaged, a second converging unit that converges specular reflection rays from the object to be imaged, an image pickup unit that receives the rays converged by the first converging unit and by the second converging unit, and generates image signals corresponding to each rays, an image data generating unit that generates image data by combining first image signals corresponding to the rays converged by the first converging unit with second image signals corresponding to the rays converged by the second converging unit, and an image data output unit that outputs the image data generated by the image data generating unit.

Abstract: An enhancing degree for sharpness enhancing is determined as a parameter for enhancing the sharpness of the input image by using at least one of information about a subject included in the input image and information about the display for displaying the input image. By using the enhancing degree determined, color distribution is controlled on a first principal component analysis axis obtained by analysis based on a principal component analysis in a color space.

Abstract: An information display device and an information processor for performing, in the case where an image is delivered to information terminals such as a mobile phone varied in display, optimum image processing in accordance with performance of the display of each of the information terminals and content information including a material and an object distance contained in the image in addition to a framework of a color conversion by a color management technology. The information display device and the information processor are also capable of changing parameters of the image processing in accordance with a user image preference and an image texture input by the user.

Abstract: When an image inputted is displayed on the display, determined is an enhancing degree as a parameter for enhancing the sharpness of the input image by using at least one of information about a subject included in the input image and information about the display for displaying the input image. By using the enhancing degree determined, color distribution is controlled on a first principal component analysis axis obtained by analysis based on a principal component analysis in a color space. Furthermore, the texture of the subject is magnified in size and again mapped onto the subject. Due to this, the input image can be enhancement-processed to a well texture feeling easy to view.