Abstract: A method for detecting forgery in facial recognition is disclosed that comprises selecting (110) an illumination pattern including at least three illumination intensities; controlling (120) a light-emitting device (220) to emit light pulses, according to the illumination pattern; capturing (130) with a camera (210) a respective image of an eye region (15) of a face (10) during each emitted light pulse; determining (140) whether a glint (20) is present in the eye region (15) of each captured image; measuring (150) a glint intensity of each determined glint (20); extracting (160) a respective numerical feature for each captured image, the numerical feature representing a curve of all measured glint intensities at the glint intensity of the respective image; and outputting (180) a signal that the face (10) is forged, if one or more of the extracted numerical features does not correspond to a reference numerical feature.

Abstract: A method is disclosed for segmentation of a calcified blood vessel in image data. An embodiment of the method includes providing a vesseltree representation of the blood vessel; providing a number of preliminary boundary representations of a number of cross-sections of the blood vessel; providing a number of intensity profiles in the image data in the number of cross-sections; determining a calcification in the cross-section based on the intensity profile; and correcting each preliminary boundary representation into a corrected boundary representation which excludes the calcification from an inner part of the blood vessel. A segmentation system is also disclosed.

Abstract: A method and a segmentation system are disclosed. An embodiment of the method includes providing an image representation of the structure; providing a start surface model, including a mesh with a plurality of vertices connected by edges; defining for each vertex a ray normal to the surface model at the position of the vertex; assigning more than two labels to each vertex, each label representing a candidate position of the vertex on the ray; providing a representation of likelihoods for each candidate position the likelihood referring to whether the candidate position corresponds to a surface point of the structure in the image representation; and defining a first order Markow Random Field with discrete multivariate random variables, the random variables including the labels of the candidate positions and the representation of likelihoods, finding an optimal segmentation of the structure by using an maximum a posteriori estimation in this Markow Random Field.

Abstract: A method is disclosed for segmentation of a calcified blood vessel in image data. An embodiment of the method includes providing a vesseltree representation of the blood vessel; providing a number of preliminary boundary representations of a number of cross-sections of the blood vessel; providing a number of intensity profiles in the image data in the number of cross-sections; determining a calcification in the cross-section based on the intensity profile; and correcting each preliminary boundary representation into a corrected boundary representation which excludes the calcification from an inner part of the blood vessel. A segmentation system is also disclosed.

Abstract: A method and a segmentation system are disclosed. An embodiment of the method includes providing an image representation of the structure; providing a start surface model, including a mesh with a plurality of vertices connected by edges; defining for each vertex a ray normal to the surface model at the position of the vertex; assigning more than two labels to each vertex, each label representing a candidate position of the vertex on the ray; providing a representation of likelihoods for each candidate position the likelihood referring to whether the candidate position corresponds to a surface point of the structure in the image representation; and defining a first order Markow Random Field with discrete multivariate random variables, the random variables including the labels of the candidate positions and the representation of likelihoods, finding an optimal segmentation of the structure by using an maximum a posteriori estimation in this Markow Random Field.