Abstract: Disclosed is a method of detecting a predetermined mark embedded in an image. The mark (29) comprises a predetermined arrangement of a plurality of elements (30), each element having a predetermined colour characteristic and predetermined shape. The method processes (20) the image to provide an encoded representation of the image at a predetermined resolution (eg. 200 dpi). The coordinate positions for substantially each of the elements of the mark embedded in the image are detected (22), wherein the detection is characterised by applying at least one mask (80) to substantially each pixel of the encoded representation. From the coordinate positions, a set of spatial features representing a spatial arrangement of the detected elements are then determined (23). The determined set of spatial features is then compared (24) to a known set of spatial features to provide a confidence level measure for a degree of matching between the known set and the determined set of spatial features.

Abstract: A method of arranging a plurality of images on a page includes the step of arranging the images on the page in accordance with a first predetermined layout pattern. The images are then rearranged in accordance with a “controlled overlapping” rule characterized by a predetermined cost function. Finally, the predetermined cost function is optimized to determine a final coordinate position for each image, thereby to generate a final layout pattern.

Abstract: The present invention provides a method and apparatus for detecting in an image text-like portions and non-text-like portions. The method includes the steps of establishing a set of fuzzy detection rules for distinguishing text-like portions of an image from non-text-like portions of an image, dividing the test image into a plurality of segment, and applying the set of fuzzy detection rules to each segment of the test image to thereby classify each segment as text-like or non-text-like. Preferably, the establishing step includes the sub-steps of identifying a plurality of image features that distinguish different portions of an image, generating a plurality of fuzzy detection rules by applying different combinations of the features to a text-like learning image and to a non-text-like learning image, and minimizing the rules to exclude those rules not supported by a predetermined amount of the learning images, and allocating the non-excluded rules to the set.