Mathematical Morphology Algorithm Research Articles

IDENTIFYING DESERT THRESHOLDS BY MAPPING INVERSE ERODIBILITY AND RECOVERY POTENTIALS IN PATCH PATTERNS USING SPECTRAL AND MORPHOLOGICAL ALGORITHMS

ABSTRACTDesert thresholds are narrow zones of spatial instability between semi‐arid and arid regions. Identifying and mapping such thresholds is difficult because of the high diversity of vegetation, soil and bare rock patterns in these regions. Here, we suggest incorporating pattern information from remote sensing imagery in the assessment of eco‐geomorphic transitions. A new methodology was developed for this purpose based on the analysis of potential inverse trends of erosion and recovery embedded in these patterns. The methodology applies spectral mixing analysis to map surface conditions and uses mathematical morphology algorithms of dilation and erosion to identify potential erodibility and recovery trends. Boundary lines are then delineated by classifying combinations of these inverse trends and assessed with reference to their separability. This approach is implemented along the climatic gradient between the Judean Mountains and the Dead Sea in central Israel using mid‐resolution Landsat TM data. The identified threshold lines fit well with lithological, soil and phytogeographical boundaries as delineated in low‐scale maps, yet add significant explicitness to their spatial definition. Such explicitness is instrumental for assessing future desert shifts because of global warming and extended overgrazing. Copyright © 2012 John Wiley & Sons, Ltd.

De-Noising Algorithm for Magnetotelluric Signal Based on Mathematical Morphology Filtering

In this paper, an effective de-noising algorithm based on mathematical morphology filtering for magnetotelluric sounding data is presented. Magnetotelluric signals are nonlinear, non-stationary, non-minimum phase, they do not meet the basic requirements of the Fourier transform based on the traditional power spectrum estimation. Mathematical morphology filtering is a new signal analysis method developed in recent years for dealing with non-linear, non-stationary signal. This paper briefly introduce the mathematical morphology filtering basic principles and algorithms. According to the properties of structuring elements, the mathematical morphology filtering is designed. Analysis structuring elements type selection program by filtering performance. Based on the measured signal processing, we discussed its application in magnetotelluric sounding data processing and strong interference separation. Experimental results indicate that the proposed method is feasible and can effectively eliminate larger scale disturbance and baseline drift of magnetotelluric sounding data. In addition, the method is efficient to keep the main characteristics of the original signals, and is helpful to improve signal quality and information interpretability for magnetotelluric sounding data.

Log Defect Detection Based on Wavelet De-Noising and Mathematical Morphological Optimization Algorithm

Log Defect Detection Based on Wavelet De-Noising and Mathematical Morphological Optimization Algorithm

Subpixel Edge Detection of Autofocus for Micro-Machine Vision System

Aiming at the subpixle edge detection of speckle in autofocus for micro-machine vision, a novel accurate subpixel edge detection algorithm was proposed. The image of the part to be inspected was binaried by simple threshold algorithm. The noise in image was eliminated by blob area threshold algorithm. The pixel level edge detection was done and the single-pixel width connected pixel level contour was acquired by binary mathematical morphological algorithm. The subpixel level edge detection was completed and the subpixel level contour was obtained by 9×9 pixel rectangular lens algorithm based on cubic spline interpolation. The example result indicate that calculation speed of the algorithm proposed herein is fast, anti-noise performance is high, inspection accuracy is high, and subpixel edge location accuracy can reach to μm level.

A MATHEMATICAL MORPHOLOGY-BASED CURRENT TRANSFORMER SATURATION IDENTIFICATION CRITERION USED FOR THE DIFFERENTIAL PROTECTION

A new saturation-blocking scheme of current transformer implemented with mathematical morphology algorithm for differential protections is presented. According to the studies of the current transformer used for the differential protection, in the event of high through fault accompanied by the current transformer saturation, the time instant that the false differential current emerges is always later than the change of the through currents. On the contrary, this time interval will not exist if it is an internal fault and one current transformer saturates. Therefore, the external fault can be identified from the internal fault by this time interval. It should be noted that this time interval may be too short when detecting very fast current transformer saturation. In this case, the current transformer blocking scheme using this interval may fail. The multi-resolution morphological gradient highlights this time interval by means of outstanding abilities of singularity detection and noise suppression. The time difference between the occurrence of fault and the emergence of differential current can be extracted in real time in terms of high accuracy. Therefore, we can set the time difference by a reasonable threshold for detecting saturation of current transformer because of very high through current during external faults. The results of simulation tests based on real-time dynamic simulater (RTDS) show that the method can avoid improper operations of the differential protection due to high through current. At the same time, it is able to guarantee the operating speed of the protection.

Segmentation of pores in wood microscopic images based on mathematical morphology with a variable structuring element

A set of pores is one of the key features in the process of wood identification. A novel method to segment pores in a wood microscopic image is proposed in this paper. The method has three steps. In the first step, structuring elements with several sizes are employed in a mathematical morphology algorithm to enhance the pores and remove other tissues in a wood microscopic image. Then the best structuring element size is determined according to the results obtained from the previous step. Finally, a binary image that includes only pores is obtained by an adaptive thresholding. Experimental results show the efficiency of the method.

Optimization of Zero-Reference Grating Considering Tip Distortions for Scanning Probe Microscopy Drift Measurement

A nonperiodic grating composed of unevenly distributed step elements on a flat substrate can serve as the calibration grating for scanning probe microscopy (SPM) drift measurements. The grating named zero-reference grating (ZRG) is characterized by the number of step elements t, the number of total grating elements n and the maximum correlation peak contrast D. To optimize the ZRG structures considering tip distortions in SPM images, mathematical morphology and genetic algorithms are adopted. Results show that the optimal step elements tend to distribute orderly under the influence of large tip radius and the contrast D decreases with the increase of tip radius when t < or = n/2. On the contrary, the optimal ZRG structures and the contrast D are insensitive to tip distortions when t > n/2. These differences can be interpreted by the effective code mechanism due to tip dilation. For quantitative drift measurements, the parameter t is suggested to be larger than n/2 since the performances of such optimal ZRGs are more robust to tip distortions.

Rock joint image segmentation based on fractional differential and multi-grade combination in mathematical morphology

Rock joint network is very complex and there is much noise in a rock image.The traditional image segmentation methods cannot obtain satisfactory results.A new edge detection algorithm was proposed based on fractional differential and multi-grade synthesis of mathematical morphology.It needs to pretreat the rock fracture image first by image processing operation such as noise filtering,image segmentation,cavity filling,spur removal,etc.Then fractional differential algorithm and multi-grade synthesis of mathematical morphology were used to get the results.The experimental results,compared with the traditional morphological methods,show that the studied algorithm maintains good detected edges of rock joint images and can increase the denoising power as well.

Quantification of the microstructures of hypoeutectic white cast iron using mathematical morphology and an artificial neural network

This paper describes an automatic system for segmentation and quantification of the microstructures of white cast iron. Mathematical morphology algorithms are used to segment the microstructures in the input images, which are later identified and quantified by an artificial neuronal network (ANN). A new computational system was developed because ordinary software could not segment the microstructures of this cast iron correctly, which is composed of cementite, pearlite and ledeburite. For validation purpose, 30 samples were analysed. The microstructures of the material in analysis were adequately segmented and quantified, which did not happen when we used ordinary commercial software. Therefore, the proposed system offers researchers, engineers, specialists and others, a valuable and competent tool for automatic and efficient microstructural analysis from images.

A Self-organizing Fuzzy Segmentation (SOFS) Method for Road Detection from High Resolution Satellite Images

This paper proposes an image segmentation algorithm based on fuzzy logic to detect road pixels in high-resolution satellite images. Other image classification and segmentation approaches require that the number of existing classes in the image scene be specified in advance. Additionally, supervised classification algorithms require sufficient samples from an image to identify the parameters of each class. The proposed method does not require specification of the number of classes. The mean and standard deviation of road pixels are automatically calculated using from 1 to 3 samples from its surface. This is an improvement on existing classification and segmentation methods. The proposed approach is tested on both simulated and real satellite images. A QuickBird color image is segmented into road and non-road zones using mean and standard deviation values calculated using the proposed method. Lastly, advanced mathematical morphology algorithms are used to extract main road centerlines from the segmented image.

A 1,000 Frames/s Programmable Vision Chip with Variable Resolution and Row-Pixel-Mixed Parallel Image Processors

A programmable vision chip with variable resolution and row-pixel-mixed parallel image processors is presented. The chip consists of a CMOS sensor array, with row-parallel 6-bit Algorithmic ADCs, row-parallel gray-scale image processors, pixel-parallel SIMD Processing Element (PE) array, and instruction controller. The resolution of the image in the chip is variable: high resolution for a focused area and low resolution for general view. It implements gray-scale and binary mathematical morphology algorithms in series to carry out low-level and mid-level image processing and sends out features of the image for various applications. It can perform image processing at over 1,000 frames/s (fps). A prototype chip with 64 × 64 pixels resolution and 6-bit gray-scale image is fabricated in 0.18 μm Standard CMOS process. The area size of chip is 1.5 mm × 3.5 mm. Each pixel size is 9.5 μm × 9.5 μm and each processing element size is 23 μm × 29 μm. The experiment results demonstrate that the chip can perform low-level and mid-level image processing and it can be applied in the real-time vision applications, such as high speed target tracking.

A MATHEMATICAL MORPHOLOGICAL FOR EDGE DETECTION IN MILLIMETER WAVE RADIOMETRIC IMAGES

Capable of working all day and under various weather conditions,the radiometer can provide the specific information which infrared and optical detectors fail to provide.This paper describes a mathematical morphological algorithm for edge detection in millimeter wave radiometric image.It is demonstrated that this algorithm operates more effectively than the traditional algorithms and the common morphological methods in edge detection.

Edge detection method based on mathematical morphology and canny algorithm

A new edge detection method was put forward based on mathematical morphology and Canny algorithm.The background of the image was estimated by morphological opening and then calculated together with the original image by geometric operations.Finally,the Canny edge detection algorithm was applied to the processed image.The experimental results show that the proposed approach is obviously superior to the traditional Canny edge detection method with median filter,and provides good foundation for feature extraction and object recognition.

Cucumber image segmentation algorithm based on rough set theory

In this paper a segmentation algorithm for cucumber images is presented to solve the problems of target identification and position for a greenhouse cucumber‐harvesting robot. Amethod of combining the three basic colours, branch‐quantity and threshold processing is used to enhance the target im ages, increasing the contrast between background and target and reducing the information content of the colour image. The strengthened image is considered as a knowledge system. The cucumber images are then segmented by a classification decision table which is based on a knowledge‐reduction algorithm from rough set theory, choosing the image colour attribute as a condition attribute and standard colour as a decision attribute. The image noise is removed using an expansion algorithm of mathematical morphology. The experimental results show that the proposed algorithm can enhance the image of greenhouse cucumbers and segment the profile of the cucumber image effectively. There is less noise in the image after segmentation.

Characterization of objects arrangement in image analysis of physical systems

In some fields of physics the systems consisting of individual objects are studied. One of the most important tasks of image analysis of such systems is the quantitative characterization of the degree of objects arrangement. For the description of spatial distribution of objects several morphological methods are typically used. However, the interpretation of derived characteristics is often difficult. In order to contribute to proper interpretation of standard morphological characteristics and to test less known algorithms for the quantitative description of the objects arrangement, a computer experiment was prepared. Several types of simulated structures corresponding to various types of experimental data in physics were generated and analysed by six algorithms of mathematical morphology and Fourier optics. The results of sensitivity analysis of these algorithms were applied to the transmission electron microscope images of thin metal films.

Digital Image Processing and MEB (BSE) Techniques in the Identification and Quantification of Minerals Phases Present in Cement and Concrete

AbstractThe generation processes of microscopic images in as Backscattered Scanning Electrons (BSE) are produced by complex interaction between incident electrons beam within the specimen. Different authors have correlated the energy of impact released with the mean atomic number (Z) of the phase to study, and deducing that the nature of the scattering events induced by the electron beam is strongly controlled by the Z value of them. If we compared the amount of electron backscattering with respect to response of the beam energies and so deduced a mathematical functions, for each specimen studied, and respectively by to digital images processing (based on the linear transformation of levels grey range in those energies) allows to sharpen in the discrimination and quantification of some phases in cements that, due to overlap processes, until now, could not be isolated. By means theses theoretical studios it is possible, for example, to identify interstitial cations that impinge on Z factor of phase deducing for each types of energies answer a series of mathematical functions, fitting a coefficient and across digital image techniques allows to sharpen in the discrimination and quantification of certain phases in cements that, as well, is noted in grey levels of digital image. In electron microscope have to settle down a strict work conditions (DW=10 mm, V0=20 KeV), and from a pattern specimen, found the grey level of image histogram for those working conditions, and fitting to the linear function, even if operated with BSE, the theoretic phases deduced from the EDX and the quantitative analysis of the stoichiometric formula. Given a coefficient (ç) its effective Z it's calculated; the deviation, therefore, gives the interstitial cations presence. With this tool, for example, the ettringite of gypsum is discriminates; or C-S-H products and the C3S. Once obtained the greys levels image of surface to examine and by means of threshold images process (a value of limit grey level or phase identification) he is immediate to obtain stereological relations (working with planar surfaces, which facilitate the treatment and the development of algorithms of mathematical morphology) and quantitative character phases of each phase

Parallelisation of Algorithms of Mathematical Morphology

The tools of mathematical morphology developed within the framework of the image processing, require of big capacity of data and the very high costs of execution. So today, the limits of the sequential machines are not to be any more to show, the passage in the new parallel machines of type Simd, Mimd, clusters or grids is imperative. This paper deals with problems related to the parallelisation of the algorithm of mathematical morphology and highlights the resources influencing over the computing time. This study leans on the various levels of parallelisable calculation to evaluate the awaited profits then in term of processing time. An implementation of a whole of algorithms of reference is carried out on a cluster and a simd computer.

Morphological algorithm design for binary images using genetic programming

This paper presents a Genetic Programming (GP) approach to the design of Mathematical Morphology (MM) algorithms for binary images. The algorithms are constructed using logic operators and the basic MM operators, i.e. erosion and dilation, with a variety of structuring elements. GP is used to evolve MM algorithms that convert a binary image into another containing just a particular feature of interest. In the study we have tested three fitness functions, training sets with different numbers of elements, training images of different sizes, and 7 different features in two different kinds of applications. The results obtained show that it is possible to evolve good MM algorithms using GP.

A Hybrid Fuzzy Neural Networks for the Detection of Tumors in Medical Images

In this study, we propose an approach to detect suspect zones or tumors in medical images. The idea is to define with precision the existence of different kinds of lesions using a hybrid system, which combines Fuzzy Neural Networks and Expert System. After applying a method of image segmentation to extract regions (by region growing algorithm or by mathematical morphology algorithm), the fuzzy neural networks detect the suspect regions, which are validated by an expert system to determine the nature of lesions. Some of experimental results on brain images show the feasibility of the proposed approach.

Identifying, visualizing, and comparing regions in irregularly spaced 3D surface data

Image segmentations have been performed to identify the surface fragmentation of rock piles using 3D surface data, and quantified. The advantages for fragmentation measurement using image analysis are significant and include: quantifying image segmentation performance in isolation of the downstream processes of fragment classification and size distribution calculation, utilization of 3D data to overcome various limitations of photographic-based image analysis, and the capacity to use 3D fragment data to eliminate the misclassification of partially visible fragments as smaller entirely visible fragments. The segmentation results have been quantified by comparison with the 3D surface data of each individual rock fragment. Mathematical morphology and image segmentation algorithms have been extended from greyscale image-based definitions and applied to irregularly spaced 3D coordinate surface data. 3D coordinate surface data can now be morphologically processed directly in 3D, segmented, visualized, and directly compared to the actual surface fragmentation in order to quantify the results.