Geodesics on the manifold of multivariate generalized Gaussian distributions with an application to multicomponent texture discrimination

We consider the Rao geodesic distance (GD) based on the Fisher information as a similarity measure on the manifold of zero-mean multivariate generalized Gaussian distributions (MGGD). The MGGD is shown to be an adequate model for the heavy-tailed wavelet statistics in multicomponent images, such as color or multispectral images. We discuss the estimation of MGGD parameters using various methods. We apply the GD between MGGDs to color texture discrimination in several classification experiments, taking into account the correlation structure between the spectral bands in the wavelet domain. We compare the performance, both in terms of texture discrimination capability and computational load, of the GD and the Kullback-Leibler divergence (KLD). Likewise, both uni- and multivariate generalized Gaussian models are evaluated, characterized by a fixed or a variable shape parameter. The modeling of the interband correlation significantly improves classification efficiency, while the GD is shown to consistently outperform the KLD as a similarity measure

On Nonrigid Shape Similarity and Correspondence

An important operation in geometry processing is finding the correspondences between pairs of shapes. The Gromov-Hausdorff distance, a measure of dissimilarity between metric spaces, has been found to be highly useful for nonrigid shape comparison. Here, we explore the applicability of related shape similarity measures to the problem of shape correspondence, adopting spectral type distances. We propose to evaluate the spectral kernel distance, the spectral embedding distance and the novel spectral quasi-conformal distance, comparing the manifolds from different viewpoints. By matching the shapes in the spectral domain, important attributes of surface structure are being aligned. For the purpose of testing our ideas, we introduce a fully automatic framework for finding intrinsic correspondence between two shapes. The proposed method achieves state-of-the-art results on the Princeton isometric shape matching protocol applied, as usual, to the TOSCA and SCAPE benchmarks

Analytical modeling of large-angle CMBR anisotropies from textures

We propose an analytic method for predicting the large angle CMBR temperature fluctuations induced by model textures. The model makes use of only a small number of phenomenological parameters which ought to be measured from simple simulations. We derive semi-analytically the $C^l$-spectrum for $2\leq l\leq 30$ together with its associated non-Gaussian cosmic variance error bars. A slightly tilted spectrum with an extra suppression at low $l$ is found, and we investigate the dependence of the tilt on the parameters of the model. We also produce a prediction for the two point correlation function. We find a high level of cosmic confusion between texture scenarios and standard inflationary theories in any of these quantities. However, we discover that a distinctive non-Gaussian signal ought to be expected at low $l$, reflecting the prominent effect of the last texture in these multipoles

2-D iteratively reweighted least squares lattice algorithm and its application to defect detection in textured images

In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective

Characterisation of the relationship between surface texture and surface integrity of superalloy components machined by grinding

The surface texture of a machined component is influenced largely by the processing parameters used during machining and hence, there is a relationship between both the formation of the surface texture and surface integrity of the machined component. In the study to be reported in this paper, GH4169, a hard-to-cut superalloy, widely used in aero-engines, was selected for a detailed investigation into the relationship between the surface texture and the component-performance (surface integrity) of the machined components for which a series of grinding experiments with different grinding-wheels and grinding parameter-values was carried out in order to quantitatively analyze variations of the surface roughness with processing parameters. Further, considering that the features of the ground-surfaces measured are of a random nature, statistic properties of the produced surfaces were revealed and characterised with power spectral density function (PSD) and auto-covariance function(ACV) method respectively

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe