Short critical area model and extraction algorithm based on defect characteristics in integrated circuits

In this investigation, we present an alternative technique to characterize the vibration signals by utilizing the morphological pattern spectrum (MPS) based on the mathematical morphology theory.Different from the conventional morphological pattern spectrum which is based on morphological open operator, we extend the morphological pattern spectrum with other morphological operators.Vibration signals measured from a gearbox with three operating states are employed to evaluate the effectiveness of MPS.Experimental results have demonstrated the extended MPS calculated based on morphological erosion operator to be very effective on characterizing vibration signals for gear fault diagnosis.

Edge detection is an important work for object recognition and is also an essential pre-processing step in image segmentation and visual analysis. This paper deeply studies the edge detection methods of the noisy images and concentrates on the mathematical morphology methods. By analyzing and studying the theory of mathematical morphology, the paper constructs edge detection operations and analyzes the specialties of the operator and the structure elements. Based on this, the paper proposes a new method of edge detection using mathematical morphology and image fusion. The operations can obtain clear and exact edge of the noisy images. By simulation and comparing with the traditional edge detection operations, the proposed method is more effective on noise restraining and retaining the image details.

By concentrating on the analysis of the spatial relationships between groups of pixels, mathematical morphology provides us with an image processing strategy complementary to those based on the analysis of the spectral signature of single pixels. A wide variety of morphological transformations are available for extracting structural information in spatial data. Accordingly, a stream of successful applications in geoscience and remote sensing have been reported since the mid-1980s as highlighted in a brief survey. However, recent advances in the theory of mathematical morphology still remain largely unexplored. We show in this paper that they can enhance methodologies for the processing and analysis of Earth observation data for tasks as diverse as filtering, simplification, directional segmentation and crest line extraction. We also address important issues overlooked in the past and concerning the applicability of a given morphological filter to Earth observation data. In particular, we point out that self-dual or even self-complementary filters are required in many applications to produce results independent of the local contrast of the searched image structures.

Rule extraction using Recursive-Rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the Pima Indian dataset

Mathematical morphology (MM) is a very efficient tool for image processing, based on non- linear operators.In this paper MM is applied to extract the image's features. As a feature we understand specific information about the image i.e. location, size, orientation of certain image elements. Morphological operators are applied to find and measure objects on the image's surface. Two practical examples are considered. First is devoted to analysis of binary images, containing printed characters. Characters are separated and MM is used to extract some information from each character. These features are later measured and included in a feature vector. It contains the special kind of information - the number of elements of the character with its shape modified in different ways. Second examples shows how feature extraction by MM works on graytone images. Images for analysis contain human faces. Morphological operators extract some important elements of human face. This information is very important to identify the human face. Experiments show us how the morphological operators can be applied to the feature detection. The simplest operators as erosion and dilation, as well as more sophisticated tools like: morphological filtering, geodesic transformations are used for that purpose. Also directional operations are applied to extract some areas. This paper includes algorithms for feature extraction by MM, as well as the brief description of morphological tools, explication of experiments and the results of them.

An automated mathematical morphology driven algorithm for water body extraction from remotely sensed images

This article describes the issue of geometric objects generalization in spatial databases with methods of mathematical morphology. The authors studied the impact of exemplary operations, i.e.: erosion, dilatation, opening, closing on the level of the data details and time efficiency of queries. It has been shown that the mathematical morphology methods can be successfully treated as a novel approach in spatial databases optimization.

An improved active contour model is proposed for image segmentation in this paper based on the theory of mathematical morphology. With a structure element shifting on contour points, the pull and restraint force to contour points can be calculated through morphology operations. The contour points move toward and locate on the boundary of the detected objects with the control forces. The experimental results show that this improved active contours model can overcome some shortages of highly sensitivity to initial curve position, hardly convergence to concave shapes. More importantly, Image segmentation based on the improved model can extract more accuracy contour shape and texture feature in application of surface defects detection, and significantly improve the accuracy of defects classification.

This paper describes the development of an automatic segmentation method and a novel navigation system that detect polyps using advanced image processing techniques. The segmentation method described here outlines the colon wall of the CT data set of abdomen by combining the active contouring model - level set method and the minima detection using mathematical morphology theory. The polyp detection is achieved by analysing the surface curvature and the texture information surrounded on the colon wall. As such, the polyp candidates are examined not only by their shape, size but also by their texture appearance which improves the current existing methods. The major contribution of our method is the detailed information analysis using the active contour model assisted by mathematical morphology operations and texture analysis.

An automatic aneurysm extraction algorithm in fused brain digital subtraction angiography images

The main purpose of segmentation in MRI images is to diagnose the problems in the normal brain anatomy and to find the location of tumour. Many of the algorithms have been found in recent years which aid to segment the medical images and identify the diseases. This paper proposes a novel 3D brain extraction algorithm (3D-BEA) for segmentation of MRI images to extract the exact brain region. Transverse relaxation time (T2) weighted images are used as an input for the development of algorithm as these images provide bright compartments and dark fat tissues in the MRI brain region. The images are first denoised and smoothed for further processing. The final brain volume has been generated using this 3D-BEA process. The result of this developed proposed algorithm is validated by comparing proposed algorithm with the results of the existing segmentation algorithm.

In this paper, we present a general theory of adaptive mathematical morphology (AMM) in the Euclidean space. The proposed theory preserves the notion of a structuring element, which is crucial in the design of geometrical signal and image processing applications. Moreover, we demonstrate the theoretical and practical distinctions between adaptive and spatially-variant mathematical morphology. We provide examples of the use of AMM in various image processing applications, and show the power of the proposed framework in image denoising and segmentation.

While it is important to exhaustively verify IC designs for their functional performance, it is equally important to verify their robustness against spot defects, that is, to foresee what will happen to the design when it is exposed to defect conditions in a real manufacturing environment. One such verification is done by extracting the layout sites where defects can induce a functional failure of the design. Initial attempts to perform this verification task were based on a 'critical area extraction' of one layer at a time, neglecting the electrical significance of interrelationships between layers. A novel method to construct deterministically multilayer critical areas is presented. These critical areas are established on the theoretical basis of defect semantics and on the new concept of 'susceptible sites'. A system comprising several algorithms which in principle maintain simultaneously as many scan lines as the number of layers, in such a way that it is possible to keep track of the vertical and horizontal effects of defects, is developed. >

Machine vision based online detection of PCB defect

Edge detection is one of the fundamental issues of digital image, in this paper, mathematical morphology method and several classical edge detection operators are reviewed. This paper provides two methods: Canny operator and mathematical morphology, which summaries relatively good image edge detection methods, and provides a reference for some detection occasions where requires smaller edge width in practical application.