Abstract: Methods and apparatus for compressing and decompressing genetic information from an individual. In one arrangement, a data compression method generates a compressed representation of at least a portion of an individual's genome by receiving an input file having a representation of the genome as a sequence of variants defined relative to a reference genome. A reference database having a plurality of reference lists of genetic variants from other individuals is accessed. Each reference list has a sequence of genetic variants from a single, phased haplotype. Two mosaics of segments from the reference lists are identified which match the genome to within a threshold accuracy. Each mosaic represents a single one of the two haplotypes of the individual's genome and includes a portion of the sequence of genetic variants from one of the reference lists. The compressed representation is generated by encoding the two mosaics and deviations from the two mosaics.

Abstract: Methods are disclosed for analysing genetic data about an organism. In one arrangement, input units are derived from studies that provide information about the association between genetic variants and phenotypes. Input units are assigned to one of a plurality of clusters, based on an assessment of the extent to which input units share genetic variants that affect any aspect of the phenotype corresponding to each input unit or any of the underlying biological mechanisms of the phenotype, thereby identifying phenotypes that share underlying biological mechanisms.

Abstract: Methods and apparatus for compressing and decompressing genetic information from an individual. In one arrangement, a data compression method generates a compressed representation of at least a portion of an individual's genome by receiving an input file having a representation of the genome as a sequence of variants defined relative to a reference genome. A reference database having a plurality of reference lists of genetic variants from other individuals is accessed. Each reference list has a sequence of genetic variants from a single, phased haplotype. Two mosaics of segments from the reference lists are identified which match the genome to within a threshold accuracy. Each mosaic represents a single one of the two haplotypes of the individual's genome and includes a portion of the sequence of genetic variants from one of the reference lists. The compressed representation is generated by encoding the two mosaics and deviations from the two mosaics.

Abstract: The motion estimation unit (100) comprises a first summation means (106) for calculating match errors of a number of candidate motion vectors of a segment (116) of a first image (118). The motion vector with the lowest match error can be assigned to the segment (116) as the estimated motion vector. The match error is based on summation of absolute differences between pixel values of the segment (116) and pixel values of a second image (120). In order to estimate the deviation of the estimated motion from the true motion vector, i.e. the motion vector error (130), the motion estimation unit (100) further comprises a second summation means (108) for calculating a variance parameter by summation of absolute differences between pixel values of the segment (116) and pixel values of the first image (118) and estimation means (110) for estimating a motion vector error (130) by comparing the match error with the variance parameter.

Abstract: A method of determining segments in a series of images based on previous segmentation results and on motion estimation. A second segment (108) of a second image (106) is determined based on a first segment (102) of a first image (100), with the first segment (102) and the second segment (108) corresponding to one object (104). The method comprises a calculation step to compare values of pixels of the first image (100) and the second image (106) in order to calculate a motion model which defines the transformation of the first segment (102) into the second segment (108). An example of such a motion model allows only translation. Translation can be described with one motion vector (110).

Abstract: The motion estimation unit (100) comprises a block-matcher (102) for calculating a start motion vector (110) by minimizing a predetermined cost function as a matching criterion for the block (116) of pixels with a further block of pixels (122) of a further image (120). The motion estimation unit (100) further comprises an optical flow analyzer (104) for calculating an update motion vector (111) based on the start motion vector (110) and which is designed to find the most appropriate set of optical flow equations corresponding to respective pixels of the block (116) of pixels. This is achieved by analyzing gradient vectors of optical flow equations for pixels of the block (116) of pixels. Finally the selector 106 of the motion estimation unit (100) selects the motion vector (126) by comparing the start motion vector (110) with the update motion vector (111).

Abstract: The motion estimation unit (100) comprises a first summation means (106) for calculating match errors of a number of candidate motion vectors of a segment (116) of a first image (118). The motion vector with the lowest match error can be assigned to the segment (116) as the estimated motion vector. The match error is based on summation of absolute differences between pixel values of the segment (116) and pixel values of a second image (120). In order to estimate the deviation of the estimated motion from the true motion vector, i.e. the motion vector error (130), the motion estimation unit (100) further comprises a second summation means (108) for calculating a variance parameter by summation of absolute differences between pixel values of the segment (116) and pixel values of the first image (118) and estimation means (110) for estimating a motion vector error (130) by comparing the match error with the variance parameter.

Abstract: Nowadays, continuous media data processing is performed more and more by programmable components, rather than dedicated single-function components. To handle such data appropriately, a system must guarantee sufficient system resources for processing. The method according to the invention proposes scalable motion compensated up-conversion of the frame rate of video sequences. By computing suitable quality measures for the algorithms (404) that perform the up-conversion (402), the method according to the invention allows to predict the required resources given an input video (406). Furthermore, the visual quality of the output video sequence is predicted. Based upon these predictions, the method selects the best algorithm to perform the up-conversion thereby allowing an optimal resource utilization of the system, for example a programmable platform for media processing.

Abstract: The motion estimation unit (100) comprises a block-matcher (102) for calculating a start motion vector (110) by minimizing a predetermined cost function as a matching criterion for the block (116) of pixels with a further block of pixels (122) of a further image (120). The motion estimation unit (100) further comprises an optical flow analyzer (104) for calculating an update motion vector (111) based on the start motion vector (110) and which is designed to find the most appropriate set of optical flow equations corresponding to respective pixels of the block (116) of pixels. This is achieved by analyzing gradient vectors of optical flow equations for pixels of the block (116) of pixels. Finally the selector 106 of the motion estimation unit (100) selects the motion vector (126) by comparing the start motion vector (110) with the update motion vector (111).

Abstract: The motion estimation unit (100) comprises a block-matcher (102) for calculating a start motion vector (110) by minimizing a predetermined cost function as a matching criterion for the block (116) of pixels with a further block of pixels (122) of a further image (120). The motion estimation unit (100) further comprises an optical flow analyzer (104) for calculating an update motion vector (111) based on the start motion vector (110) and which is designed to minimize a sum of errors associated with a set of optical flow equations corresponding to respective pixels of the block (116) of pixels. Finally the selector 106 of the motion estimation unit (100) selects the motion vector (126) by comparing the start motion vector (110) with the update motion vector (111).

Abstract: A method of determining segments in a series of images based on previous segmentation results and on motion estimation. A second segment (108) of a second image (106) is determined based on a first segment (102) of a first image (100), with the first segment (102) and the second segment (108) corresponding to one object (104). The method comprises a calculation step to compare values of pixels of the first image (100) and the second image (106) in order to calculate a motion model which defines the transformation of the first segment (102) into the second segment (108). An example of such a motion model allows only translation. Translation can be described with one motion vector (110).