TL;DR: In this article, a method for extracting linear features from images is described, where a series of lower-resolution versions of the original image (a pyramid) are constructed and a threshold is found which, when applied in the neighborhood of the feature in the high-resolution image, segments the linear feature from its background.
Abstract: : A method is described of extracting linear features from images. The approach is to construct a series of lower-resolution versions of the original image (a pyramid), and to look for lines in these images. A line in a low- resolution image corresponds to a thicker linear feature in a high-resolution image. The position and extent of this linear feature is calculated from the low-resolution image, and a threshold is found which, when applied in the neighborhood of the feature in the high-resolution image, segments the linear feature from its background. Advantages of the method are that only the parts of the image in the neighborhood of linear features need be thresholded, and that different thresholds may be used to extract the various linear features in the image.
TL;DR: A system of bandpass filters, modelled on the early mechanisms of human vision, is described which provides a simple means to eliminate blur and noise from an image.
Abstract: A system of bandpass filters, modelled on the early mechanisms of human vision, is described which provides a simple means to eliminate blur and noise from an image. The system automatically adjusts itself to suit local signal conditions in an image, without prior knowledge of signal statistics.
TL;DR: The design of a pattern database system and the economy that it provides for the matching problem are discussed and the advantage of this organization is that the matching algorithms presented reject most of the patterns in the database by utilizing the relatively small in size index tables and thus avoid the overhead of unnecessary CPU time and I O operation between main memory and secondary storage.