An image processing apparatus processes a first image and a second image so as to detect a corresponding pixel in the second image which corresponds to a target pixel in the first image. The first image has a first parameter value, and the second image has a second parameter value different from the first parameter value. The first parameter value and the second parameter value are values of optical parameters of image capturing systems used to capture the first image and the second image. The image processing apparatus includes an area setter that sets a two-dimensional search area as a partial area in which the corresponding pixel is to be searched in the second image, based on a predetermined range in which each of the first and second parameter values can change, and a detector that detects the corresponding pixel by searching the two-dimensional search area.

A 3D information calculation apparatus includes processing circuitry that may receive first and second images of different first and second wavelength bands, respectively, at a same time and angle of view based on a subject being imaged while structured light of the first wavelength band is projected on to subject, receive third and fourth images of the first and second wavelength bands, respectively, at a same time and angle of view based on the subject being imaged while the structured light is not projected on the subject, calculate a first difference image of the first wavelength band based on subtracting the first and third images, calculate a second difference image of the second wavelength band based on subtracting the second and fourth images, calculate an extraction image based on subtracting the first and second difference images, and calculate a distance to the subject based on the extraction image.

The disclosure relates to an image processing apparatus for determining a depth of a pixel of a reference image of a plurality of images representing a visual scene relative to a plurality of locations, wherein the plurality of locations define a two-dimensional grid with rows and columns and wherein the location of the reference image is associated with a reference row and a reference column of the grid. The image processing apparatus comprises a depth determiner configured to determine a first depth estimate on the basis of the reference image and a first subset of the plurality of images for determining the depth of the pixel of the reference image, wherein the images of the first subset are associated with locations being associated with a row of the grid different than the reference row and with a column of the grid different than the reference column.

Enhanced methods and systems for generating both a geometry model and an optical-reflectance model (an object reconstruction model) for a physical object, based on a sparse set of images of the object under a sparse set of viewpoints. The geometry model is a mesh model that includes a set of vertices representing the object's surface. The reflectance model is SVBRDF that is parameterized via multiple channels (e.g., diffuse albedo, surface-roughness, specular albedo, and surface-normals). For each vertex of the geometry model, the reflectance model includes a value for each of the multiple channels. The object reconstruction model is employed to render graphical representations of a virtualized object (a VO based on the physical object) within a computation-based (e.g., a virtual or immersive) environment. Via the reconstruction model, the VO may be rendered from arbitrary viewpoints and under arbitrary lighting conditions.

A method for manufacturing a model of an anatomical feature comprises utilizing a stereographic digital image camera at a plurality of different vantage points relative to a reference anatomical feature to capture a plurality of three dimensional digital image pairs of a corresponding plurality of external surface portions of the reference anatomical feature including concavities and convexities of complex anatomical surfaces, and processing the plurality of three dimensional digital image pairs to produce a three dimensional digital surface image of the reference anatomical feature and a corresponding data file. A modified three dimensional digital surface image and corresponding modified data file may be produced by utilizing one or more of selecting a portions of said three dimensional digital surface image, scaling the three dimensional digital surfaced image, and generating a mirror image of the three dimensional digital surface image.

Apparatus is provided to capture three-dimensional images of a subject's head. The apparatus comprises a plurality of stereographic digital cameras that are operable simultaneously and are disposed in a predetermined vertical planar relationship to each other. The plurality of stereographic digital cameras are positioned to capture a group of stereographic digital image pairs of a corresponding vertical hemispherical surface portion of the head of the subject when the subject is positioned in a predetermined location in front of the plurality of stereographic digital cameras. The apparatus further comprises a processing apparatus coupled to the plurality of stereographic digital cameras. The processing apparatus operates on the group of stereographic digital image pairs to generate a three-dimensional digital image file of at least a full vertical hemispheric portion of the head of the subject.

A method and system are provided for processing image content. The method comprises receiving information about a content image captured at least by one camera. The content includes multi-view representation of an image including both distorted and undistorted areas. The camera parameters and image parameters are then obtained and used to determine to which areas are undistorted and which areas are distorted in said image. This is used to calculate depth map of the image using the determined undistorted and distorted information. A final stereoscopic image is then rendered that uses the distorted and undistorted areas and calculation of depth map.

A method and apparatus for sorting is described, and which includes providing a product stream formed of individual objects of interest having feature aspects which can be detected; generating multiple images of each of the respective objects of interest; classifying the feature aspects of the objects of interest; identifying complementary images by analyzing some of the multiplicity of images; fusing the complementary images to form an aggregated region representation of the complementary images; and sorting the respective objects of interest based at least in part upon the aggregated region representation which is formed.

This disclosure describes a configuration of an aerial vehicle, such as an unmanned aerial vehicle (“UAV”), that includes a plurality of cameras that may be selectively combined to form a stereo pair for use in obtaining stereo images that provide depth information corresponding to objects represented in those images. Depending on the distance between an object and the aerial vehicle, different cameras may be selected for the stereo pair based on the baseline between those cameras and a distance between the object and the aerial vehicle. For example, cameras with a small baseline (close together) may be selected to generate stereo images and depth information for an object that is close to the aerial vehicle. In comparison, cameras with a large baseline may be selected to generate stereo images and depth information for an object that is farther away from the aerial vehicle.

The photo-video based spatial-temporal volumetric capture system more efficiently, produces high frame rate and high resolution 4D dynamic human videos, without a need for 2 separate 3D and 4D scanner systems, by combining a set of high frame rate machine vision video cameras with a set of high resolution photography cameras. It reduces a need for manual CG works, by temporally up-sampling shape and texture resolution of 4D scanned video data from a temporally sparse set of higher resolution 3D scanned keyframes that are reconstructed both by using machine vision cameras and photography cameras. Unlike typical performance capture system that uses single static template model at initialization (e.g. A or pose), the photo-video based spatial-temporal volumetric capture system stores multiple keyframes of high resolution 3D template models for robust and dynamic shape and texture refinement of 4D scanned video sequence. For shape up-sampling, the system can apply mesh-tracking based temporal shape super resolution. For texture up-sampling, the system can apply machine learning based temporal texture super resolution.