Abstract: A method of determining alignment data for a target frame and a reference frame. Patch data is received for each of a plurality of reference patches of the reference frame. The patch data comprises a reference direction of a feature in an associated reference patch and a reference signal corresponding to a projection of reference patch image data in a direction substantially perpendicular to the reference direction. A shift between at least one of said reference patches and a corresponding target patch in the target frame is determined using the reference signal associated with the reference patch and a target signal corresponding to a projection of the target patch image data in the direction substantially perpendicular to the reference direction associated with said reference patch. The alignment data for the target frame is determined using the determined shift for the reference patch.

Abstract: A method of determining alignment data for a target frame and a reference frame. Patch data is received for each of a plurality of reference patches of the reference frame. The patch data comprises a reference direction of a feature in an associated reference patch and a reference signal corresponding to a projection of reference patch image data in a direction substantially perpendicular to the reference direction. A shift between at least one of said reference patches and a corresponding target patch in the target frame is determined using the reference signal associated with the reference patch and a target signal corresponding to a projection of the target patch image data in the direction substantially perpendicular to the reference direction associated with said reference patch. The alignment data for the target frame is determined using the determined shift for the reference patch.

Abstract: A method for selecting a representative image of an eye from a plurality of captured images, comprises associating neighboring images from the plurality of images with each other to determine a spatial relationship (shift) of the images and an initial quality value for each image. The method determines an accumulated quality value for at least one portion of each image using the initial quality values corresponding to those other images of the plurality of images that are spatially related with the portion. The spatial relationship between a portion of an image and the other images is identified based of the determined spatial arrangement of the images. The method selects a representative image from the plurality of images using, for each image, at least the initial quality value for the image and the accumulated quality value of at least one portion of the image.

Abstract: A method of producing an image with a blurred background (151) from an initial image (111) and a depth map (112) identifying a foreground region (236) and a background region (237) of the image, said method comprising determining (670) an edge visibility measure (671) for one edge (231) in the depth map (112) by comparing (820) a pixel value (233) in the foreground with a pixel value (234) in the background, said edge visibility measure (671) distinguishing between the foreground (236) and the background (237); applying (680) a spatially variable blur to the depth map (112) to produce a compositing matte (681), dependent on the edge visibility measure (671); applying (530) a blur to the background region (521) of the depth map (112); and compositing (150) the blurred background image data (531) with the initial image (111) to produce the rendered image with the blurred background (151).

Abstract: A method for selecting a representative image of an eye from a plurality of captured images, comprises associating neighbouring images from the plurality of images with each other to determine a spatial relationship (shift) of the images and an initial quality value for each image. The method determines an accumulated quality value for at least one portion of each image using the initial quality values corresponding to those other images of the plurality of images that are spatially related with the portion. The spatial relationship between a portion of an image and the other images is identified based of the determined spatial arrangement of the images. The method selects a representative image from the plurality of images using, for each image, at least the initial quality value for the image and the accumulated quality value of at least one portion of the image.

Abstract: A method of producing an image with a blurred background (151) from an initial image (111) and a depth map (112) identifying a foreground region (236) and a background region (237) of the image, said method comprising determining (670) an edge visibility measure (671) for one edge (231) in the depth map (112) by comparing (820) a pixel value (233) in the foreground with a pixel value (234) in the background, said edge visibility measure (671) distinguishing between the foreground (236) and the background (237); applying (680) a spatially variable blur to the depth map (112) to produce a compositing matte (681), dependent on the edge visibility measure (671); applying (530) a blur to the background region (521) of the depth map (112); and compositing (150) the blurred background image data (531) with the initial image (111) to produce the rendered image with the blurred background (151)

Abstract: A method (400) and system (200) for classifying a audio signal are described. The method (400) operates by first receiving a sequence of audio frame feature data, each of the frame feature data characterising an audio frame along the audio segment. In response to receipt of each of the audio frame feature data, statistical data characterising the audio segment is updated with the received frame feature data. The received frame feature data is then discarded. A preliminary classification for the audio segment may be determined from the statistical data. Upon receipt of a notification of an end boundary of the audio segment, the audio segment is classified (410) based on the statistical data.

Abstract: Disclosed herein is a method for identifying a constellation of alignment marks within an arrangement of computer readable marks in an image (1412), the arrangement of computer readable marks (1412) including alignment marks (1401-1406) and data carrying marks (1407-1410). The alignment marks (1401-1406) define a reference grid within the arrangement of computer readable marks and the data carrying marks (1407-1410) are modulated with respect to the reference grid to encode data. The method selects at least two marks (1501, 1502) from the arrangement of computer readable marks and determines a rotation center with reference to the selected marks (1501, 1502). The method then determines rotated positions for the selected marks by rotating each of the selected marks by a predetermined angle about the rotation center.

Abstract: Disclosed herein is a method for identifying a constellation of alignment marks within an arrangement of computer readable marks in an image (1412), the arrangement of computer readable marks (1412) including alignment marks (1401-1406) and data carrying marks (1407-1410). The alignment marks (1401-1406) define a reference grid within the arrangement of computer readable marks and the data carrying marks (1407-1410) are modulated with respect to the reference grid to encode data. The method selects at least two marks (1501, 1502) from the arrangement of computer readable marks and determines a rotation centre with reference to the selected marks (1501, 1502). The method then determines rotated positions for the selected marks by rotating each of the selected marks by a predetermined angle about the rotation centre.

Abstract: A method (400) and system (200) for classifying a audio signal are described. The method (400) operates by first receiving a sequence of audio frame feature data, each of the frame feature data characterising an audio frame along the audio segment. In response to receipt of each of the audio frame feature data, statistical data characterising the audio segment is updated with the received frame feature data. The received frame feature data is then discarded. A preliminary classification for the audio segment may be determined from the statistical data. Upon receipt of a notification of an end boundary of the audio segment, the audio segment is classified (410) based on the statistical data.