Method and apparatus for automatically detecting patterns in digital point-ordered signals

The present invention is a method and system for detecting a physical feature of a test piece by detecting a pattern in a signal representing data from inspection of the test piece. The pattern is detected by automated additive decomposition of a digital point-ordered signal which represents the data. The present invention can properly handle a non-periodic signal. A physical parameter of the test piece is measured. A digital point-ordered signal representative of the measured physical parameter is generated. The digital point-ordered signal is decomposed into a baseline signal, a background noise signal, and a peaks/troughs signal. The peaks/troughs from the peaks/troughs signal are located and peaks/troughs information indicating the physical feature of the test piece is output

Application of a New Method for Analyzing Images: Two-Dimensional Non-Linear Additive Decomposition NOTICE Application of a New Method for Analyzing Images: Two-Dimensional Non-Linear Additive Decomposition

ABSTRACT This paper documents the application of a new image processing algorithm, two-dimensional non-linear additive decomposition (NLAD), which is used to identify regions in a digital image whose gray-scale (or color) intensity is different than the surrounding background. Standard image segmentation algorithms exist that allow users to segment images based on gray-scale intensity and/or shape. However, these processing techniques do not adequately account for the image noise and lighting variation that typically occurs across an image. NLAD is designed to separate image noise and background from artifacts thereby providing the ability to consistently evaluate images. The decomposition techniques used in this algorithm are based on the concepts of mathematical morphology. NLAD emulates the human capability of visually separating an image into different levels of resolution components, denoted as 'coarse', 'fine', and 'intermediate.' Very little resolution information overlaps any two of the component images. This method can easily determine and/or remove trends and noise from an image. NLAD has several additional advantages over conventional image processing algorithms, including no need for a transformation from one space to another, such as is done with Fourier transforms, and since only finite summations are required, the calculational effort is neither extensive nor complicated