On Multifractal Structure in Non-Representational Art

Multifractal analysis techniques are applied to patterns in several abstract expressionist artworks, paintined by various artists. The analysis is carried out on two distinct types of structures: the physical patterns formed by a specific color (``blobs''), as well as patterns formed by the luminance gradient between adjacent colors (``edges''). It is found that the analysis method applied to ``blobs'' cannot distinguish between artists of the same movement, yielding a multifractal spectrum of dimensions between about 1.5-1.8. The method can distinguish between different types of images, however, as demonstrated by studying a radically different type of art. The data suggests that the ``edge'' method can distinguish between artists in the same movement, and is proposed to represent a toy model of visual discrimination. A ``fractal reconstruction'' analysis technique is also applied to the images, in order to determine whether or not a specific signature can be extracted which might serve as a type of fingerprint for the movement. However, these results are vague and no direct conclusions may be drawn.Comment: 53 pp LaTeX, 10 figures (ps/eps

Digital Image-Based Frameworks for Monitoring and Controlling of Particulate Systems

Particulate processes have been widely involved in various industries and most products in the chemical industry today are manufactured as particulates. Previous research and practise illustrate that the final product quality can be influenced by particle properties such as size and shape which are related to operating conditions. Online characterization of these particles is an important step for maintaining desired product quality in particulate processes. Image-based characterization method for the purpose of monitoring and control particulate processes is very promising and attractive. The development of a digital image-based framework, in the context of this research, can be envisioned in two parts. One is performing image analysis and designing advanced algorithms for segmentation and texture analysis. The other is formulating and implementing modern predictive tools to establish the correlations between the texture features and the particle characteristics. According to the extent of touching and overlapping between particles in images, two image analysis methods were developed and tested. For slight touching problems, image segmentation algorithms were developed by introducing Wavelet Transform de-noising and Fuzzy C-means Clustering detecting the touching regions, and by adopting the intensity and geometry characteristics of touching areas. Since individual particles can be identified through image segmentation, particle number, particle equivalent diameter, and size distribution were used as the features. For severe touching and overlapping problems, texture analysis was carried out through the estimation of wavelet energy signature and fractal dimension based on wavelet decomposition on the objects. Predictive models for monitoring and control for particulate processes were formulated and implemented. Building on the feature extraction properties of the wavelet decomposition, a projection technique such as principal component analysis (PCA) was used to detect off-specification conditions which generate particle mean size deviates the target value. Furthermore, linear and nonlinear predictive models based on partial least squares (PLS) and artificial neural networks (ANN) were formulated, implemented and tested on an experimental facility to predict particle characteristics (mean size and standard deviation) from the image texture analysis

A Review of Wavelet Based Fingerprint Image Retrieval

A digital image is composed of pixels and information about brightness of image and RGB triples are used to encode color information. Image retrieval problem encountered when searching and retrieving images that is relevant to a user’s request from a database. In Content based image retrieval, input goes in the form of an image. In these images, different features are extracted and then the other images from database are retrieved accordingly. Biometric distinguishes the people by their physical or behavioral qualities. Fingerprints are viewed as a standout amongst the most solid for human distinguishment because of their uniqueness and ingenuity. To retrieve fingerprint images on the basis of their textural features,by using different wavelets. From the input fingerprint image, first of all center point area is selected and then its textural features are extracted and stored in database. When a query image comes then again its center point is selected and then its texture feature are extracted. Then these features are matched for similarity and then resultant image is displayed. DOI: 10.17762/ijritcc2321-8169.15026

A Quantitative Study of Scaling Properties of Fracture Networks

Fracture networks and their scaling properties are important from both an academic and practical perspective since they play a significant role in many areas ranging from crustal fluid flow to studies of earthquakes. Over the years, researchers have employed a wide variety of techniques to quantify the complexities of fractured media. These range from deterministic, process-based approaches employing the laws of physics, to ones involving the applications of geostatistics and more recently, fractal geometry. Fractals are irregular entities that show self-similarity over a wide range of scales and can be quantified by the fractal dimension, D. It is important that the D-values of such networks are properly evaluated. The box-counting algorithm is a widely used technique for characterizing fracture networks as fractals and estimating their D-values. If this analysis yields a power law distribution given by N ∝ r−D , where N is the number of boxes containing one or more fractures and r is the box size, the network is considered to be fractal. However, researchers are divided in their opinion about issues such as the best box-counting algorithm for estimating the ‘correct’ D-value or whether a fracture network is indeed fractal. For instance, a closer look at the N vs. r plots for a set of previously published fracture trace maps shows that such distributions do not follow power law scaling. As part of the present work, a synthetic fractal-fracture network with a known theoretical fractal dimension, D, was used to develop an improved algorithm for the box-counting method that returns “unbiased” D-values. A suite of 17 fracture trace maps that had previously been evaluated for their fractal nature was reanalyzed using the improved technique. “Unbiased” estimates of D for these maps ranged from 1.56±0.02 to 1.79±0.02, and were much higher than the original estimates. The fractal dimension of a pattern however, does not capture all of the heterogeneity present. For instance, two patterns that have the same fractal dimension may have very different appearances. We investigated the applicability of a new parameter, namely lacunarity, L, for distinguishing between different fracture networks having the same fractal dimension. The lacunarity is the degree of clustering in a pattern and is a geostatistical parameter that can be used to study patterns that are both fractals non-fractal. The gliding-box algorithm is a popular technique for computing lacunarities as a function of the box-size, r. In the present work it has been successfully used for the first time to analyze fracture networks. Apart from computing lacunarity curves for a set of synthetic patterns generated in MATLAB, we also analyzed a set of 7 nested natural fracture maps with similar D values ranging from 1.80±0.05 to 1.84±0.04. Our results show that differences between maps are most pronounced when L values are determined using intermediate box sizes. Estimates of L based on such box sizes indicate that fractures are more clustered at smaller scales. Future work in this area should explore the use of the gliding box algorithm to see whether fracture networks are self-similar over a given range of scales and if lacunarity analysis alone can furnish information on the “unbiased” fractal dimensions of such networks

Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis

This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment. This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.PhDCommittee Chair: Dr. May D. Wang; Committee Member: Dr. Andrew N. Young; Committee Member: Dr. Anthony J. Yezzi; Committee Member: Dr. Edward J. Coyle; Committee Member: Dr. Paul Benkese

The merger history, AGN and dwarf galaxies of Hickson Compact Group 59

Compact group galaxies often appear unaffected by their unusually dense environment. Closer examination can, however, reveal the subtle, cumulative effects of multiple galaxy interactions. Hickson Compact Group (HCG) 59 is an excellent example of this situation. We present a photometric study of this group in the optical (HST), infrared (Spitzer) and X-ray (Chandra) regimes aimed at characterizing the star formation and nuclear activity in its constituent galaxies and intra-group medium. We associate five dwarf galaxies with the group and update the velocity dispersion, leading to an increase in the dynamical mass of the group of up to a factor of 10 (to 2.8e13 Msun), and a subsequent revision of its evolutionary stage. Star formation is proceeding at a level consistent with the morphological types of the four main galaxies, of which two are star-forming and the other two quiescent. Unlike in some other compact groups, star-forming complexes across HCG 59 closely follow mass-radius scaling relations typical of nearby galaxies. In contrast, the ancient globular cluster populations in galaxies HCG 59A and B show intriguing irregularities, and two extragalactic HII regions are found just west of B. We age-date a faint stellar stream in the intra-group medium at ~1 Gyr to examine recent interactions. We detect a likely low-luminosity AGN in HCG 59A by its ~10e40 erg/s X-ray emission; the active nucleus rather than star formation can account for the UV+IR SED. We discuss the implications of our findings in the context of galaxy evolution in dense environments.Comment: 38 pages, 17 figures. Please visit "http://tinyurl.com/isk-hcg59" for a full-resolution PDF. Accepted for publication in the Astrophysical Journa

Dynamic reconfiguration of human brain networks during learning

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes -- flexibility and selection -- must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4 figures, 3 table

Statistical shape analysis for bio-structures : local shape modelling, techniques and applications

A Statistical Shape Model (SSM) is a statistical representation of a shape obtained from data to study variation in shapes. Work on shape modelling is constrained by many unsolved problems, for instance, difficulties in modelling local versus global variation. SSM have been successfully applied in medical image applications such as the analysis of brain anatomy. Since brain structure is so complex and varies across subjects, methods to identify morphological variability can be useful for diagnosis and treatment. The main objective of this research is to generate and develop a statistical shape model to analyse local variation in shapes. Within this particular context, this work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point Distribution Model and uses a combination of other well known techniques: Fractal analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space representation for the problem of contour localisation. Similarly, Diffusion Maps are employed as a spectral shape clustering tool to identify sets of local partitions useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis method based on the Gaussian and Laplacian pyramids is explained and used to compare the featured Local Shape Model. Experimental results on a number of real contours such as animal, leaf and brain white matter outlines have been shown to demonstrate the effectiveness of the proposed model. These results show that local shape models are efficient in modelling the statistical variation of shape of biological structures. Particularly, the development of this model provides an approach to the analysis of brain images and brain morphometrics. Likewise, the model can be adapted to the problem of content based image retrieval, where global and local shape similarity needs to be measured

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications