Abstract: A method for generating a 3D model from at least one image data input (P1, R1(cam1); . . . ; Pn, Rn(camn)) comprising 2D+z+reliability information comprises the steps of building a 3D surface from said at least one image data input, followed by a meshing operation in 3D on said 3D surface to thereby generate said 3D model.

Abstract: The invention pertains to a method for segmenting an image from a sequence of video images into a foreground and a background, the image being composed of pixels, the method includes assigning an initial foreground probability to each of the pixels; assigning a set of probability propagation coefficients to each of the pixels; and applying a global optimizer to the initial foreground probabilities of the pixels to classify each of the pixels as a foreground pixel or a background pixel, to obtain a definitive foreground map; wherein the global optimizer classifies each processed pixel in function of the initial foreground probability of the processed pixel and the initial foreground probability of neighboring pixels, the relative weight of the initial foreground probability of neighboring pixels being determined by the probability propagation coefficients of the neighboring pixels.

Abstract: An exemplary technique is provided for obtaining an improved depth precision for a stereo image of an object of interest. The technique includes imaging a first image of the object of interest from a first position, imaging a second image of the object of interest from a second position different from the first position, determining a rough estimate of a depth for the object of interest based on the first image and the second image, determining an improved set of imaging positions corresponding to an improved depth precision for the object of interest based on the rough estimate of the depth, imaging improved images from the improved set of imaging positions, and determining an improved depth based on the improved images.

Abstract: A system and method for three-dimensional (3D) model morphing is disclosed. Morphing a standard 3D model based on 2D image data input includes the steps of performing an initial morphing of said standard 3D model using a detection model and a morphing model, thereby obtaining a morphed standard 3D model; determining the optical flow between the 2D image data input and the morphed standard 3D model, and applying the optical flow to said morphed standard 3D model, thereby providing a fine tuned 3D standard model.

Abstract: Method for adapting a 3D model (m) of an object, said method comprising the steps of performing at least one projection of said 3D model to obtain at least one 2D image model projection (p1) with associated depth information (d1), performing at least one state extraction operation on said at least one 2D image model projection (p1), thereby obtaining at least one state (s1) adapting said at least one 2D image model projection (p1) and said associated depth information (d1) in accordance with said at least one state (s1) and with a target state (s), thereby obtaining at least one adapted 2D image model (p1?) and an associated adapted depth (d1?) back-projecting said at least one adapted 2D image model (p1?) to 3D, based on said associated adapted depth (d1?) for thereby obtaining an adapted 3D model (m?).

Abstract: A method for constructing an image model (M1; M) from at least one image data input (IV1; IV1-IVn), comprises the steps of, in an iterative way, determining at least one state (PS1; PS1-PSn) of said at least one image data input (IV1; IV1-IVn), and a state (PSMF) of an intermediate learning model (MF; MIF) determining a target state (TSP) from said at least one state (PS1; PS1-PSn) of said at least one image data input, and from the state (PSMF) of said intermediate learning model (MF; MIF), performing at least one transformation in accordance with the determined target state (TSP) on said at least one image data input (IV1; IV1-IVn), thereby generating at least one transformed image (IV1T; IV1T-IVnT), aggregating said at least one transformed image (Iv1T; IV1T-IVnt) with intermediate learning model (MF; MIF; MIT; MFT) information, thereby generating an updated estimate of said image model (M1; M), providing said updated estimate of said image model (M1; M) as said image model (M1; M) while also providing

Abstract: A feedback-system for managing the position of a frontal camera provided on a portable multimedia device during video communication. The feedback-system comprises a camera image analyzer coupled to the frontal camera and adapted to detect and analyze the pose of a user facing the frontal camera, an optimal video estimator coupled to the camera image analyzer and adapted to calculate a 6-dimentional error-vector of the current position of the frontal camera with respect to an optimal position of the frontal camera, an intuitive feedback manager coupled to the optimal video estimator and adapted to generate a transformation matrix translating the error-vector into an error message for the image displayed on a screen of the portable multimedia device, the intuitive feedback manager being further coupled to the portable multimedia device which is adapted to use the error message for modifying the image displayed on the screen.

Abstract: A method for identifying virtual visual information in at least two images from a first sequence of successive images of a visual scene comprising real visual information and said virtual visual information is disclosed. Feature detection is performed on at least one of said at least two images. The movement of the detected features between said at least two images is determined, thereby obtaining a set of movements. Movements of said set which pertain to movements in a substantially vertical plane are identified, thereby obtaining a set of vertical movements. The features pertaining to said vertical movements are related to said virtual visual information in said at least two images, such as to identify the virtual visual information. Arrangements for performing embodiments of the method are disclosed as well.

Abstract: The invention pertains to a method for segmenting an image from a sequence of video images into a foreground and a background, said image being composed of pixels, the method comprising: assigning an initial foreground probability to each of said pixels; assigning a set of probability propagation coefficients to each of said pixels; and applying a global optimizer to the initial foreground probabilities of said pixels to classify each of said pixels as a foreground pixel or a background pixel, to obtain a definitive foreground map; wherein said global optimizer classifies each processed pixel in function of the initial foreground probability of said processed pixel and the initial foreground probability of neighboring pixels, the relative weight of the initial foreground probability of neighboring pixels being determined by the probability propagation coefficients of said neighboring pixels.

Abstract: An exemplary method of providing a virtual representation includes determining a plurality of position constraints that correspond to physical positions of individuals relative to each other. A plurality of determined viewing constraints each correspond to a position of one of the individuals relative to a display used by that individual for viewing the virtual representation. Relative positions of virtual representations of the individuals in the virtual representation are determined to correspond to the determined position and viewing constraints.

Abstract: A method for determining calibration data for at least two cameras (camera1, camera2) in a multi view position, includes a step of determining respective parameters ((h100, . . . , h122), (h200, . . . , h222)) for identifying at least one respective homographic transformation on respective images (image1,image2) taken by said cameras of a same scene, by performing respective geometry analyses on said respective images (image1, image2), a step of performing at least one respective combined homographic transformation/feature detection step on said respective images thereby obtaining respective sets (feature set1, feature set2) of features on respective transformed images, such that said calibration data are obtained from matches (m1, . . . , mk) determined between said respective sets of features.

Abstract: A method for obtaining an improved depth precision for a stereo image of an object of interest is disclosed, comprising: imaging a first image of the object of interest from a first position; imaging a second image of the object of interest from a second position different from the first position; wherein the method comprises determining a rough estimate of the depth for the object of interest, based on the first image and the second image; determining an improved set of imaging positions corresponding to an improved depth precision for the object of interest, based on the rough estimate of the depth; imaging improved images from the improved set of imaging positions; determining an improved depth based on the improved images. Also associated systems have been described.

Abstract: A method and device for determining at least one set of matching attributes between a plurality of images is disclosed. Two dimensional images are projected to a three dimensional space then searched for matching attributes. Searching for corresponding attributes is much easier and computationally less intensive in the 3D space compared to a search in the 2D space. The method includes steps of projecting at least part of the images of said plurality to a 3-dimensional space resulting in a plurality of 3-D projected images (image1_3D, image2_3D), searching for at least one corresponding set of elements within the 3D projected images of the plurality of 3-D projected images, and calculating back said corresponding elements within the original images of the plurality and providing said corresponding elements within said original images as said at least one set of matched attributes.