The effectiveness of methods of shape analysis / Cliff A. Lemen.

n.s. no.15(1983)

Morphometric shape analysis using learning vector quantization neural networks - an example distinguishing two microtine vole species

Closely related species may be very difficult to distinguish morphologically, yet sometimes morphology is the only reasonable possibility for taxonomic classification. Here we present learning-vector-quantization artificial neural networks as a powerful tool to classify specimens on the basis of geometric morphometric shape measurements. As an example, we trained a neural network to distinguish between field and root voles from Procrustes transformed landmark coordinates on the dorsal side of the skull, which is so similar in these two species that the human eye cannot make this distinction. Properly trained neural networks misclassified only 3% of specimens. Therefore, we conclude that the capacity of learning vector quantization neural networks to analyse spatial coordinates is a powerful tool among the range of pattern recognition procedures that is available to employ the information content of geometric morphometrics.

Otolith shape analysis as a tool for population discrimination of the white grunt (Haemulon plumieri) stock in the northern coast of the Yucatan Peninsula, Mexico

In this study, the population structure of the white grunt (Haemulon plumieri) from the northern coast of the Yucatan Peninsula was determined through an otolith shape analysis based on the samples collected in three locations: Celestún (N 20°49",W 90°25"), Dzilam (N 21°23", W 88°54") and Cancún (N 21°21",W 86°52"). The otolith outline was based on the elliptic Fourier descriptors, which indicated that the H. plumieri population in the northern coast of the Yucatan Peninsula is composed of three geographically delimited units (Celestún, Dzilam, and Cancún). Significant differences were observed in mean otolith shapes among all samples (PERMANOVA; F2, 99 = 11.20, P = 0.0002), and the subsequent pairwise comparisons showed that all samples were significantly differently from each other. Samples do not belong to a unique white grunt population, and results suggest that they might represent a structured population along the northern coast of the Yucatan Peninsula

A complex network-based approach for boundary shape analysis

This paper introduces a novel methodology to shape boundary characterization, where a shape is modeled into a small-world complex network. It uses degree and joint degree measurements in a dynamic evolution network to compose a set of shape descriptors. The proposed shape characterization method has all efficient power of shape characterization, it is robust, noise tolerant, scale invariant and rotation invariant. A leaf plant classification experiment is presented on three image databases in order to evaluate the method and compare it with other descriptors in the literature (Fourier descriptors, Curvature, Zernike moments and multiscale fractal dimension). (C) 2008 Elsevier Ltd. All rights reserved.

Biological shape analysis by digital curvature

This paper reports the novel application of digital curvature as a feature for morphological characterization and classification of landmark shapes. By inheriting several unique features of the continuous curvature, the digital curvature provides invariance to translations, rotations, local shape deformations, and is easily made tolerant to scaling. In addition, the bending energy, a global shape feature, can be directly estimated from the curvature values. The application of these features to analyse patterns of cranial morphological geographic differentiation in the rodent species Thrichomys apereoides has led to encouraging results, indicating a close correspondence between the geographical and morphological distributions. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

New likelihoods for shape analysis

We introduce a new kind of likelihood function based on the sequence of moments of the data distribution. Both binned and unbinned data samples are discussed, and the multivariate case is also derived. Building on this approach we lay out the formalism of shape analysis for signal searches. In addition to moment-based likelihoods, standard likelihoods and approximate statistical tests are provided. Enough material is included to make the paper self-contained from the perspective of shape analysis. We argue that the moment-based likelihoods can advantageously replace unbinned standard likelihoods for the search of nonlocal signals, by avoiding the step of fitting Monte Carlo generated distributions. This benefit increases with the number of variables simultaneously analyzed. The moment-based signal search is exemplified and tested in various 1D toy models mimicking typical high-energy signal-background configurations. Moment-based techniques should be particularly appropriate for the searches for effective operators at the LHC.

Shape analysis using fractal dimension: A curvature based approach

The present work shows a novel fractal dimension method for shape analysis. The proposed technique extracts descriptors from a shape by applying a multi-scale approach to the calculus of the fractal dimension. The fractal dimension is estimated by applying the curvature scale-space technique to the original shape. By applying a multi-scale transform to the calculus, we obtain a set of descriptors which is capable of describing the shape under investigation with high precision. We validate the computed descriptors in a classification process. The results demonstrate that the novel technique provides highly reliable descriptors, confirming the efficiency of the proposed method. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4757226]

Estimating persistent Betti numbers for discrete shape analysis

Persistent Topology is an innovative way of matching topology and geometry, and it proves to be an effective mathematical tool in shape analysis. In order to express its full potential for applications, it has to interface with the typical environment of Computer Science: It must be possible to deal with a finite sampling of the object of interest, and with combinatorial representations of it. Following that idea, the main result claims that it is possible to construct a relation between the persistent Betti numbers (PBNs; also called rank invariant) of a compact, Riemannian submanifold X of R^m and the ones of an approximation U of X itself, where U is generated by a ball covering centered in the points of the sampling. Moreover we can state a further result in which, this time, we relate X with a finite simplicial complex S generated, thanks to a particular construction, by the sampling points. To be more precise, strict inequalities hold only in "blind strips'', i.e narrow areas around the discontinuity sets of the PBNs of U (or S). Out of the blind strips, the values of the PBNs of the original object, of the ball covering of it, and of the simplicial complex coincide, respectively.

Statistical shape analysis via principal factor analysis

Statistical shape analysis techniques commonly employed in the medical imaging community, such as active shape models or active appearance models, rely on principal component analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose principal factor analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficient technique that performs dimensionality reduction (as opposed to independent component analysis, ICA). The key difference between PFA and PCA is that PFA models covariance between variables, rather than the total variance in the data. The added value of PFA is illustrated on 2D landmark data of corpora callosa outlines. Then, a study of the 3D shape variability of the human left femur is performed. Finally, we report results on vector-valued 3D deformation fields resulting from non-rigid registration of ventricles in MRI of the brain.

Pulse-Shape Analysis of PDM-QPSK Modulation Formats for 100 and 200 Gb/s DWDM transmissions

Advanced optical modulation format polarization-division multiplexed quadrature phase shift keying (PDM-QPSK) has become a key ingredient in the design of 100 and 200-Gb/s dense wavelength-division multiplexed (DWDM) networks. The performance of this format varies according to the shape of the pulses employed by the optical carrier: non-return to zero (NRZ), return to zero (RZ) or carrier-suppressed return to zero (CSRZ). In this paper we analyze the tolerance of PDM-QPSK to linear and nonlinear optical impairments: amplified spontaneous emission (ASE) noise, crosstalk, distortion by optical filtering, chromatic dispersion (CD), polarization mode dispersion (PMD) and fiber Kerr nonlinearities. RZ formats with a low duty cycle value reduce pulse-to-pulse interaction obtaining a higher tolerance to CD, PMD and intrachannel nonlinearities.

Trasmission and Reflection (ATR)Far-Infrared Spectroscopy Applied in the Analysis of Cultural Heritage Materials

FIR spectroscopy is an alternative way of collecting spectra of many inorganic pigments and corrosion products found on art objects, which is not normally observed in the MIR region. Most FIR spectra are traditionally collected in transmission mode but as a real novelty it is now also possible to record FIR spectra in ATR (Attenuated Total Reflectance) mode. In FIR transmission we employ polyethylene (PE) for preparation of pellets by embedding the sample in PE. Unfortunately, the preparation requires heating of the PE in order to produces at transparent pellet. This will affect compounds with low melting points, especially those with structurally incorporated water. Another option in FIR transmission is the use of thin films. We test the use of polyethylene thin film (PETF), both commercial and laboratory-made PETF. ATR collection of samples is possible in both the MIR and FIR region on solid, powdery or liquid samples. Changing from the MIR to the FIR region is easy as it simply requires the change of detector and beamsplitter (which can be performed within a few minutes). No preparation of the sample is necessary, which is a huge advantage over the PE transmission method. The most obvious difference, when comparing transmission with ATR, is the distortion of band shape (which appears asymmetrical in the lower wavenumber region) and intensity differences. However, the biggest difference can be the shift of strong absorbing bands moving to lower wavenumbers in ATR mode. The sometimes huge band shift necessitates the collection of standard library spectra in both FIR transmission and ATR modes, provided these two methods of collecting are to be employed for analyses of unknown samples. Standard samples of 150 pigment and corrosion compounds are thus collected in both FIR transmission and ATR mode in order to build up a digital library of spectra for comparison with unknown samples. XRD, XRF and Raman spectroscopy assists us in confirming the purity or impurity of our standard samples. 24 didactic test tables, with known pigment and binder painted on the surface of a limestone tablet, are used for testing the established library and different ways of collecting in ATR and transmission mode. In ATR, micro samples are scratched from the surface and examined in both the MIR and FIR region. Additionally, direct surface contact of the didactic tablets with the ATR crystal are tested together with water enhanced surface contact. In FIR transmission we compare the powder from our test tablet on the laboratory PETF and embedded in PE. We also compare the PE pellets collected using a 4x beam condenser, focusing the IR beam area from 8 mm to 2 mm. A few samples collected from a mural painting in a Nepalese temple, corrosion products collected from archaeological Chinese bronze objects and samples from a mural paintings in an Italian abbey, are examined by ATR or transmission spectroscopy.