Abstract: Novel images may be generated using an image generator implemented on a processor. The image generator may receive as input neural features selected from a neural texture atlas. The image generator may also receive as input one or more position guides identifying position information for a plurality of input image pixels. The novel images may be evaluated using an image discriminator to determine a plurality of optimization values by comparing each of the plurality of novel images with a respective one of a corresponding plurality of input images. Each of the novel images may be generated from a respective camera pose relative to an object identical to that of the respective one of the corresponding plurality of input images. The image generator and the neural features may be updated based on the optimization values and stored on a storage device.

Abstract: Novel images may be generated using an image generator implemented on a processor. The image generator may receive as input neural features selected from a neural texture atlas. The image generator may also receive as input one or more position guides identifying position information for a plurality of input image pixels. The novel images may be evaluated using an image discriminator to determine a plurality of optimization values by comparing each of the plurality of novel images with a respective one of a corresponding plurality of input images. Each of the novel images may be generated from a respective camera pose relative to an object identical to that of the respective one of the corresponding plurality of input images. The image generator and the neural features may be updated based on the optimization values and stored on a storage device.

Abstract: Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. In particular, a multi-view interactive digital media representation (MVIDMR) of a person can be generated from live images of a person captured from a hand-held camera. Using the image data from the live images, a skeleton of the person and a boundary between the person and a background can be determined from different viewing angles and across multiple images. Using the skeleton and the boundary data, effects can be added to the person, such as wings. The effects can change from image to image to account for the different viewing angles of the person captured in each image.

Abstract: Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. In particular, a multi-view interactive digital media representation (MVIDMR) of a person can be generated from live images of a person captured from a hand-held camera. Using the image data from the live images, a skeleton of the person and a boundary between the person and a background can be determined from different viewing angles and across multiple images. Using the skeleton and the boundary data, effects can be added to the person, such as wings. The effects can change from image to image to account for the different viewing angles of the person captured in each image.

Abstract: Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. In particular, a multi-view interactive digital media representation (MVIDMR) of a person can be generated from live images of a person captured from a hand-held camera. Using the image data from the live images, a skeleton of the person and a boundary between the person and a background can be determined from different viewing angles and across multiple images. Using the skeleton and the boundary data, effects can be added to the person, such as wings. The effects can change from image to image to account for the different viewing angles of the person captured in each image.

Abstract: Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. In particular, a multi-view interactive digital media representation (MVIDMR) of a person can be generated from live images of a person captured from a hand-held camera. Using the image data from the live images, a skeleton of the person and a boundary between the person and a background can be determined from different viewing angles and across multiple images. Using the skeleton and the boundary data, effects can be added to the person, such as wings. The effects can change from image to image to account for the different viewing angles of the person captured in each image.

Abstract: Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. In particular, a multi-view interactive digital media representation (MVIDMR) of a person can be generated from live images of a person captured from a hand-held camera. Using the image data from the live images, a skeleton of the person and a boundary between the person and a background can be determined from different viewing angles and across multiple images. Using the skeleton and the boundary data, effects can be added to the person, such as wings. The effects can change from image to image to account for the different viewing angles of the person captured in each image.

Abstract: Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. In particular, a multi-view interactive digital media representation (MVIDMR) of a person can be generated from live images of a person captured from a hand-held camera. Using the image data from the live images, a skeleton of the person and a boundary between the person and a background can be determined from different viewing angles and across multiple images. Using the skeleton and the boundary data, effects can be added to the person, such as wings. The effects can change from image to image to account for the different viewing angles of the person captured in each image.

Abstract: An image processing apparatus includes a feature value calculating section that calculates a feature value from an image picked up of a living mucous membrane, an extraction section that extracts a structure corresponding to the feature value, and a region division section that divides the structure into partial regions according to a predetermined condition.

Abstract: A medical image processing device includes an input section to which a biological mucous membrane image obtained by picking up an image of a biological mucous membrane is inputted, a region extracting section that extracts a mucous membrane microstructure region corresponding to a mucous membrane microstructure from the inputted biological mucous membrane image, a closed region identifying section that identifies at least one closed region regarded as being surrounded by the mucous membrane microstructure region, and a unit region setting section that sets a biologically histological unit region on the basis of the mucous membrane microstructure region and the closed region.

Abstract: An image processing apparatus includes a feature value calculating section that calculates a feature value from an image picked up of a living mucous membrane, an extraction section that extracts a structure corresponding to the feature value, and a region division section that divides the structure into partial regions according to a predetermined condition.

Abstract: A medical image processing device includes an input section to which a biological mucous membrane image obtained by picking up an image of a biological mucous membrane is inputted, a region extracting section that extracts a mucous membrane microstructure region corresponding to a mucous membrane microstructure from the inputted biological mucous membrane image, a closed region identifying section that identifies at least one closed region regarded as being surrounded by the mucous membrane microstructure region, and a unit region setting section that sets a biologically histological unit region on the basis of the mucous membrane microstructure region and the closed region.

Abstract: An image processing apparatus includes: a basic shape matching section that extracts, as a structure region, a predetermined structural object included in an image obtained by picking up an image of a mucosal surface of a living body, and matches each of regions resulting from the structure region being divided, the regions each including at least one pixel, with a first region having a first basic shape or a second region having a second basic shape; a feature value calculating section that sequentially sets regions of interest from among the regions matched by the basic shape matching section, and calculates counts of the first regions and the second regions adjacent to each of the regions of interest; and a classification section that classifies the structure region based on a result of the calculation by the feature value calculating section.

Abstract: An image processing apparatus includes: a basic shape matching section that extracts, as a structure region, a predetermined structural object included in an image obtained by picking up an image of a mucosal surface of a living body, and matches each of regions resulting from the structure region being divided, the regions each including at least one pixel, with a first region having a first basic shape or a second region having a second basic shape; a feature value calculating section that sequentially sets regions of interest from among the regions matched by the basic shape matching section, and calculates counts of the first regions and the second regions adjacent to each of the regions of interest; and a classification section that classifies the structure region based on a result of the calculation by the feature value calculating section.

Abstract: An image processing apparatus includes: an image input section via which a medical image picked up of a mucous surface of a living body is inputted; a setting section that sets a region of interest as a first region and a second region including a unit region in the medical image; a feature value calculating section that calculates a first feature value relating to a shape of the region of interest based on pixel values of the region of interest and a region in a periphery thereof and a second feature value relating to a shape of the unit region based on pixel values of the unit region and a region in a periphery thereof; and an matching section that matches the region of interest with a predetermined shape based on distributions of the first feature value and the second feature value calculated by the feature value calculating section.

Abstract: An image processing apparatus of the invention includes a first feature value calculation unit adapted to calculate a first feature value for each pixel in an image picked up of living tissue, where the first feature value represents a value of an index which indicates what shape a local region; a second feature value calculation unit adapted to calculate a degree of concentration of a gradient vector as a second feature value based on the gradient vector for each pixel in the image; an evaluation value calculation unit adapted to calculate a geometric evaluation value for each pixel, based on calculation results of the first feature value and the second feature value; and a region extraction unit of separately extracting a candidate region of the linear structure and a candidate region of the massive structure based on a calculation result of the geometric evaluation value.

Abstract: An image processing apparatus of the invention includes a first feature value calculation unit adapted to calculate a first feature value for each pixel in an image picked up of living tissue, where the first feature value represents a value of an index which indicates what shape a local region; a second feature value calculation unit adapted to calculate a degree of concentration of a gradient vector as a second feature value based on the gradient vector for each pixel in the image; an evaluation value calculation unit adapted to calculate a geometric evaluation value for each pixel, based on calculation results of the first feature value and the second feature value; and a region extraction unit of separately extracting a candidate region of the linear structure and a candidate region of the massive structure based on a calculation result of the geometric evaluation value.

Abstract: A medical image processing apparatus is a medical image processing apparatus performing image segmentation processing for a medical image including: a feature value calculating unit that calculates at least one feature value from the medical image; a category setting unit that sets, on the basis of the feature value calculated by the feature value calculating unit, plural categories in the medical image; a parameter setting unit that sets, on the basis of the feature value calculated by the feature value calculating unit, a parameter for each of areas of the medical image respectively classified into the categories set by the category setting unit; and an image segmenting unit that performs the image segmentation processing for the medical image according to an arithmetic operation of employing a dynamic contour model specified according to the number of categories set by the category setting unit and parameters set by the parameter setting unit.

Abstract: An image processing apparatus according to the present invention includes a pixel selection unit adapted to select a pixel of interest from an image; a contrast feature value calculation unit adapted to calculate, as a contrast feature value, a value which represents an amount of change in contrast in a local region including the pixel of interest; a geometric feature value calculation unit adapted to calculate, as a geometric feature value, a value of an index which indicates whether or not a linear or massive structure is contained in the local region; an evaluation value calculation unit adapted to calculate an evaluation value by weighting each of the contrast feature value and the geometric feature value; and a region extraction unit adapted to extract a candidate region estimated to contain the linear or massive structure from the image based on a calculation result of the evaluation value.