Structuring element

About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.

Training of object classes using mathematical morphology

Abstract: In some industrial optical character recognition applications, the background of the image surrounding the characters is very confusing and contains clutter that often overlays and connects the characters. Characters are found amidst the clutter by applying a number of morphological structuring elements to the image. Each structuring element is responsible for locating a specific class of characters where all characters in that class are similar to each other. The set of all structuring elements efficiently covers the entire character set. To further reduce noise, other checks such as colinearity and equidistance of the characters in the string are applied. This paper describes an automatic training method for defining efficient classes of structuring elements.

Algorithms for Mathematical Morphological Operations with Flat Top Structuring Elements

Abstract: The mathematical morphological operations on grey scale images require the selections of the minimum or the maximum value within the windows set by structuring elements. This paper deals with the structuring elements which are three dimensional with flat top and infinite height. The flat top region can be various shapes of one or two dimensions such as line segment, hexagon, octagon or circle. This paper describes the optimal implementation of the morphological operations with such structuring elements, an iterative method which combines controlled image shiftings and comparisons between the original and shifted images. It is an effective image processing method without a cytocomputer architecture [1,2].

Extending Morphological Signatures for Visual Pattern Recognition

Abstract: Morphological signatures are powerful descriptions of the image content which are based on the framework of mathematical morphology These signatures can be computed on a global or local scale: they are called pattern spectra (or granulometries and antigranulometries) when measured on the complete images and morphological profiles when related to single pixels Their goal is to measure shape distribution instead of intensity distribution, thus they can be considered as a relevant alternative to classical intensity histograms, in the context of visual pattern recognition A morphological signature (either a pattern spectrum or a morphological profile) is defined as a series of morphological operations (namely openings and closings) considering a predefined pattern called structuring element Even if it can be used directly to solve various pattern recognition problems related to image data, the simple definitions given in the binary and grayscale cases limit its usefulness in many applications In this paper, we introduce several 2-D extensions to the classical 1-D morphological signature More precisely, we elaborate morphological signatures which try to gather more image information and do not only include a dimension related to the object size, but also consider on a second dimension a complementary information relative to size, intensity or spectral information Each of the 2-D morphological signature proposed in this paper can be defined either on a global or local scale and for a particular kind of images among the most commonly ones (binary, grayscale or multispectral images) We also illustrate these signatures by several real-life applications related to object recognition and remote sensing

A gradient-based morphological method to produce planar curve offsets

Abstract: Two-dimensional curve offsets have a wide application area ranging from manufacturing to medical imaging. To that end, this paper concentrates on two novel techniques to produce planar curve offsets. Both methods, which are based on mathematical morphology, employ the concept that the boundaries formed by a circular structuring element whose center moves across the points on a base curve comprise the entire offsets of the progenitor. The first technique titled IMOBS was introduced in our former paper and was shown to have superior properties in terms of its high accuracy, low computational complexity, and its ability to handle complex curves if compared to the techniques available in the literature. Consequently, an all-purpose algorithm titled AMOBS is introduced to enhance further the performance of the former technique by making good use of gradient information to find globally the most suitable candidate points in the boundary data set via grid search techniques. Thus, the new paradigm is demonstrated to overcome some of the problems (like orphan curve offsets) encountered in extreme cases. Both algorithms, which have similar attributes in terms of run-time complexity and memory cost, are comparatively tested via two experimental cases where most CAD/CAM packages fail to yield acceptable results.