End-to-end analysis of hexagonal vs rectangular sampling in digital imaging systems

The purpose of this study was to compare two common methods for image sampling in digital image processing: hexagonal sampling and rectangular sampling. The two methods differ primarily in the arrangement of the sample points on the image focal plane. In order to quantitatively compare the two sampling methods, a mathematical model of an idealized digital imaging system was used to develop a set of mean-squared-error fidelity loss metrics. The noiseless continuous/discrete/continuous end-to-end digital imaging system model consisted of four independent components: an input scene, an image formation point spread function, a sampling function, and a reconstruction function. The metrics measured the amount of fidelity lost by an image due to image formation, sampling and reconstruction, and the combined loss for the entire system

Application of mathematical morphology to the analysis of X-ray NDE images

Ever since the beginning, man has been in the relentless pursuit of perfection. From stone age to space age, from caves to condominiums, from carts to planes, trains and automobiles, his drive for consummation has grown considerably. The high quality products that are available in the market at the turn of the twenty first century are living legacies of his unyielding endeavor for excellence. But one fact that most people do not realize is the amount of time and money devoted to quality control and non- destructive evaluation (NDE) that is responsible for the high quality of products. In the past, people used to tap earthenware and other materials as a means of non destructive testing for defects in the material. They could sense the defects by the nature of the sound propagated through the material. The ultrasonic method of NDE is an extension of this principle

Eco-ISEA3H, a machine learning ready spatial database for ecometric and species distribution modeling

We present the Eco-ISEA3H database, a compilation of global spatial data characterizing climate, geology, land cover, physical and human geography, and the geographic ranges of nearly 900 large mammalian species. The data are tailored for machine learning (ML)-based ecological modeling, and are intended primarily for continental- to global-scale ecometric and species distribution modeling. Such models are trained on present-day data and applied to the geologic past, or to future scenarios of climatic and environmental change. Model training requires integrated global datasets, describing species' occurrence and environment via consistent observational units. The Eco-ISEA3H database incorporates data from 17 sources, and includes 3,033 variables. The database is built on the Icosahedral Snyder Equal Area (ISEA) aperture 3 hexagonal (3H) discrete global grid system (DGGS), which partitions the Earth's surface into equal-area hexagonal cells. Source data were incorporated at six nested ISEA3H resolutions, using scripts developed and made available here. We demonstrate the utility of the database in a case study analyzing the bioclimatic envelopes of ten large, widely distributed mammalian species.Peer reviewe

Computer image processing with application to chemical engineering

A literature survey covers a wide range of picture processing topics from the general problem of manipulating digitised images to the specific task of analysing the shape of objects within an image field. There follows a discussion and development of theory relating to this latter task. A number of shape analysis techniques are inapplicable or computationally untenable when applied to objects containing concavities. A method is proposed and implemented whereby any object may be divided into convex components the algebraic sum of which constitute the original. These components may be related by a tree structure. It is observed that properties based on integral measurements, e.g. area, are less susceptible to quantisation errors than those based on linear and derivative measurements such as diameters anti slopes. A set of moments invariant with respect to size, position and orientation are derived and applied to the study of the above convex components. An outline of possible further developments is given

Image thinning by hexagonal grid

One of the tasks of image processing systems is to characterize images by thinning, or skeletonizing techniques. The standard method, based on a disk growing technique, often results in disconnected skeletons. Since the standard technique is based on a square grid system, the disk shape is also square. The. purpose of this research has been to determine whether or not a hex based grid will provide better connected skeletons for images originally represented in a square grid system. The algorithms to implement this project have been implemented in the X-Window environment and also make use of Dataparallel C

An analysis of surface area estimates of binary volumes under three tilings

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 77-79).by Erik G. Miller.M.S

Development of mathematical morphology systems for signal feature extraction and detection

This thesis describes a set of algorithms and systems that were developed, using signal processing techniques based on mathematical morphology (MM), for neonatal electrocardiogram (ECG) signal analysis and power transformer inrush current identification. MM methodologies are founded on set-theoretic concepts and nonlinear superpositions of signals and images. Morphological operations have been applied successfully to a wide range of problems including image processing and analysis tasks, noise suppression, feature extraction and pattern recognition etc. This approach seems very appropriate for dealing with objects which share common features, and has thus attracted attention for solving problems similar to those described in this thesis, which are closely related to feature extraction and identification. This thesis begins with a systematic introduction to MM. It explains the historical background and the concept of MM, highlights the advantages ofMM as an advanced nonlinear signal/image processing technique. A brief comparison between MM and traditional filtering techniques is then given, followed by the descriptions of various morphological operations, from basic operators defined for binary images, to the elaborate generalised framework for sets in a generic mathematical space, the complete lattice. The development of a morphological method to discriminate magnetising inrush current waveform from internal fault conditions of large power transv formers is then described. A morphological signal decomposition scheme is proposed to allow the unique feature associated with the inrush current waveform to be separated and identified in the time domain, to avoid the problems of sensitivity and robustness that may occur in the traditional Fourier analysis based approaches. The performance of the proposed method is assessed and discussed, based on signals derived from various operating conditions of the transformer. The second application presented is a morphological scheme for neonatal ECG signal processing and analysis, aiming to facilitate the investigation of the relationship between the clinical pattern of asphyxiated newborn infants and alterations of the ECG pattern. Neonatal ECGs are not routinely used to achieve a detailed analysis as these measurements would usually involve the time consuming act of manual interpretation and measurement. Existing technologies have also not yet been able to accurately monitor these parameters due to the rapid heart rate and the variation of waveform morphology of babies. In the proposed scheme, a morphological filtering method that incorporates subject specific information is developed, to remove the interferences introduced by recording environments and subjects without much distortion to the ECG pattern of interest. The performance of the proposed algorithms is examined using simulated neonatal ECGs and experimental signals acquired from infants. The possibility of extending this study to the fetuses is also considered, in which the fetal ECG would be obtained from the composite maternal signal, to allow intervention at an early stage for fetuses at a high risk of asphyxia. The implementation and integration of the morphological system for neonatal ECG analysis is then described. A prototype of the morphological ECG analyser is developed, which allows the system to be used in clinics by persons without a detailed knowledge of the technology. The optimisation of basic morphological operators, code design, hardware integration and optimisation are discussed, with emphasis on a generic architecture that can accommodate future improvement and extension without major revision of the code. The results obtained from the pilot trial on the ward of Liverpool Women's Hospital are then given and investigated, focusing on the accuracy of the ECG measurements and the relationship between the waveform morphology and the gestational ages of the babies. The major contributions of this work are the utilisation of the advanced performance of MM for feature enhancement, extraction, noise suppression and background normalisation. The studies include the development of morphological algorithms for the decomposition and representation of the power transformer inrush current waveform, and further to enhance its features of interest and to allow them to be identified; introduction of a novel approach for neonatal ECG signal processing and analysis; development of an integrated morphological system for medical research on the neonatal ECG, and investigation of the results obtained from this system with experiments carried out in a clinical environment

The deep structure of Gaussian scale space images

In order to be able to deal with the discrete nature of images in a continuous way, one can use results of the mathematical field of 'distribution theory'. Under almost trivial assumptions, like 'we know nothing', one ends up with convolving the image with a Gaussian filter. In this manner scale is introduced by means of the filter's width. The ensemble of the image and its convolved versions at al scales is called a 'Gaussian scale space image'. The filter's main property is that the scale derivative equals the Laplacean of the spatial variables: it is the Greens function of the so-called Heat, or Diffusion, Equation. The investigation of the image all scales simultaneously is called 'deep structure'. In this thesis I focus on the behaviour of the elementary topological items 'spatial critical points' and 'iso-intensity manifolds'. The spatial critical points are traced over scale. Generically they are annihilated and sometimes created pair wise, involving extrema and saddles. The locations of these so-called 'catastrophe events' are calculated with sub-pixel precision. Regarded in the scale space image, these spatial critical points form one-dimensional manifolds, the so-called critical curves. A second type of critical points is formed by the scale space saddles. They are the only possible critical points in the scale space image. At these points the iso-intensity manifolds exhibit special behaviour: they consist of two touching parts, of which one intersects an extremum that is part of the critical curve containing the scale space saddle. This part of the manifold uniquely assigns an area in scale space to this extremum. The remaining part uniquely assigns it to 'other structure'. Since this can be repeated, automatically an algorithm is obtained that reveals the 'hidden' structure present in the scale space image. This topological structure can be hierarchically presented as a binary tree, enabling one to (de-)select parts of it, sweeping out parts, simplify, etc. This structure can easily be projected to the initial image resulting in an uncommitted 'pre-segmentation': a segmentation of the image based on the topological properties without any user-defined parameters or whatsoever. Investigation of non-generic catastrophes shows that symmetries can easily be dealt with. Furthermore, the appearance of creations is shown to be nothing but (instable) protuberances at critical curves. There is also biological inspiration for using a Gaussian scale space, since the visual system seems to use Gaussian-like filters: we are able of seeing and interpreting multi-scale