47,520 research outputs found
Aspects of genetic and morphological variation in selected new world land birds
Thesis (M.S.) University of Alaska Fairbanks, 2000The objective of this thesis is to examine variation in certain New World land birds, focusing on morphological difference at the intraspecific level and genetic differences at the intra- and interspecific levels. First, I investigate sexual dimorphism in the Wilson's Warbler (Wilsonia pusilla), a Nearctic-Neotropic migrant parulid. Using museum specimens, I quantify the degree of dimorphism and devise a method to distinguish the sexes using morphological measurements. Second, I outline a new method of approximating Weir and Cockerham's 0 (1984, 1993), an unbiased estimator of genetic population structure. The method uses commonly published parameters and obviates the need to recode existing allozyme data sets to calculate 0. The estimation algorithm is shown to be useful for both model populations and real-world avian populations
Tracing the Scenarios in Scenario-Based Product Design: a study to support scenario generation
Scenario-based design originates from the human-computer interaction and\ud
software engineering disciplines, and continues to be adapted for product development. Product development differs from software development in the former’s more varied context of use, broader characteristics of users and more tangible solutions. The possible use of scenarios in product design is therefore broader and more challenging. Existing design methods that involve scenarios can be employed in many different stages of the product design process. However, there is no proficient overview that discusses a\ud
scenario-based product design process in its full extent. The purposes of creating scenarios and the evolution of scenarios from their original design data are often not obvious, although the results from using scenarios are clearly visible. Therefore, this paper proposes to classify possible scenario uses with their purpose, characteristics and supporting design methods. The classification makes explicit different types of scenarios and their relation to one another. Furthermore, novel scenario uses can be referred or added to the classification to develop it in parallel with the scenario-based design\ud
practice. Eventually, a scenario-based product design process could take inspiration for creating scenarios from the classification because it provides detailed characteristics of the scenario
Morphological Number Counts and Redshift Distributions to I = 25 from the Hubble Deep Fields: Constraints on Cosmological Models from Early Type Galaxies
We combine magnitude and photometric redshift data on galaxies in the Hubble
Deep Fields with morphological classifications in order to separate out the
distributions for early type galaxies. The updated morphological galaxy number
counts down to I = 25 and the corresponding redshift distributions are used as
joint constraints on cosmological models, in particular on the values of the
density parameter Omega_{0} and normalised cosmological constant Lambda_{0}.
We find that an Einstein - de Sitter universe with simple passive evolution
gives an excellent fit to the counts and redshift data at all magnitudes. An
open, low Omega_{0}, model with no net evolution (and conservation of the
number of ellipticals), which fits the counts equally well, is somewhat less
successful, predicting slightly lower mean redshifts and, more significantly,
the lack of a high--z tail. A number conserving model with a dominant
contribution from Lambda_{0}, on the other hand, is far less successful,
predicting a much narrower distribution than seen. More complex models are
obviously possible, but we conclude that if large scale transmutation between
types does {\it not} occur, then the lambda-dominated models provide a very
poor fit to the current data.Comment: Accepted for publication in MNRA
Modeling of evolving textures using granulometries
This chapter describes a statistical approach to classification of dynamic texture images, called parallel evolution functions (PEFs). Traditional classification methods predict texture class membership using comparisons with a finite set of predefined texture classes and identify the closest class. However, where texture images arise from a dynamic texture evolving over time, estimation of a time state in a continuous evolutionary process is required instead. The PEF approach does this using regression modeling techniques to predict time state. It is a flexible approach which may be based on any suitable image features. Many textures are well suited to a morphological analysis and the PEF approach uses image texture features derived from a granulometric analysis of the image. The method is illustrated using both simulated images of Boolean processes and real images of corrosion. The PEF approach has particular advantages for training sets containing limited numbers of observations, which is the case in many real world industrial inspection scenarios and for which other methods can fail or perform badly. [41] G.W. Horgan, Mathematical morphology for analysing soil structure from images, European Journal of Soil Science, vol. 49, pp. 161–173, 1998. [42] G.W. Horgan, C.A. Reid and C.A. Glasbey, Biological image processing and enhancement, Image Processing and Analysis, A Practical Approach, R. Baldock and J. Graham, eds., Oxford University Press, Oxford, UK, pp. 37–67, 2000. [43] B.B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the Making, A.K. Peters Ltd., Wellesley, MA, 1995. [44] H. Iversen and T. Lonnestad. An evaluation of stochastic models for analysis and synthesis of gray-scale texture, Pattern Recognition Letters, vol. 15, pp. 575–585, 1994. [45] A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, vol. 24(12), pp. 1167–1186, 1991. [46] T. Jossang and F. Feder, The fractal characterization of rough surfaces, Physica Scripta, vol. T44, pp. 9–14, 1992. [47] A.K. Katsaggelos and T. Chun-Jen, Iterative image restoration, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 208–209, 2000. [48] M. K¨oppen, C.H. Nowack and G. R¨osel, Pareto-morphology for color image processing, Proceedings of SCIA99, 11th Scandinavian Conference on Image Analysis 1, Kangerlussuaq, Greenland, pp. 195–202, 1999. [49] S. Krishnamachari and R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation, IEEE Transactions on Image Processing, vol. 6(2), pp. 251–267, 1997. [50] T. Kurita and N. Otsu, Texture classification by higher order local autocorrelation features, Proceedings of ACCV93, Asian Conference on Computer Vision, Osaka, pp. 175–178, 1993. [51] S.T. Kyvelidis, L. Lykouropoulos and N. Kouloumbi, Digital system for detecting, classifying, and fast retrieving corrosion generated defects, Journal of Coatings Technology, vol. 73(915), pp. 67–73, 2001. [52] Y. Liu, T. Zhao and J. Zhang, Learning multispectral texture features for cervical cancer detection, Proceedings of 2002 IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 169–172, 2002. [53] G. McGunnigle and M.J. Chantler, Modeling deposition of surface texture, Electronics Letters, vol. 37(12), pp. 749–750, 2001. [54] J. McKenzie, S. Marshall, A.J. Gray and E.R. Dougherty, Morphological texture analysis using the texture evolution function, International Journal of Pattern Recognition and Artificial Intelligence, vol. 17(2), pp. 167–185, 2003. [55] J. McKenzie, Classification of dynamically evolving textures using evolution functions, Ph.D. Thesis, University of Strathclyde, UK, 2004. [56] S.G. Mallat, Multiresolution approximations and wavelet orthonormal bases of L2(R), Transactions of the American Mathematical Society, vol. 315, pp. 69–87, 1989. [57] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989. [58] B.S. Manjunath and W.Y. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837–842, 1996. [59] B.S. Manjunath, G.M. Haley and W.Y. Ma, Multiband techniques for texture classification and segmentation, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 367–381, 2000. [60] G. Matheron, Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York, 1975
Utilizing Astroinformatics to Maximize the Science Return of the Next Generation Virgo Cluster Survey
The Next Generation Virgo Cluster Survey is a 104 square degree survey of the
Virgo Cluster, carried out using the MegaPrime camera of the
Canada-France-Hawaii telescope, from semesters 2009A-2012A. The survey will
provide coverage of this nearby dense environment in the universe to
unprecedented depth, providing profound insights into galaxy formation and
evolution, including definitive measurements of the properties of galaxies in a
dense environment in the local universe, such as the luminosity function. The
limiting magnitude of the survey is g_AB = 25.7 (10 sigma point source), and
the 2 sigma surface brightness limit is g_AB ~ 29 mag arcsec^-2. The data
volume of the survey (approximately 50 terabytes of images), while large by
contemporary astronomical standards, is not intractable. This renders the
survey amenable to the methods of astroinformatics. The enormous dynamic range
of objects, from the giant elliptical galaxy M87 at M(B) = -21.6, to the
faintest dwarf ellipticals at M(B) ~ -6, combined with photometry in 5 broad
bands (u* g' r' i' z'), and unprecedented depth revealing many previously
unseen structures, creates new challenges in object detection and
classification. We present results from ongoing work on the survey, including
photometric redshifts, Virgo cluster membership, and the implementation of fast
data mining algorithms on the infrastructure of the Canadian Astronomy Data
Centre, as part of the Canadian Advanced Network for Astronomical Research
(CANFAR).Comment: 8 pages, 2 figures. Accepted for the Joint Workshop and Summer
School: Astrostatistics and Data Mining in Large Astronomical Databases, La
Palma, May 30th - June 3rd 2011. A higher resolution version is available at
http://sites.google.com/site/nickballastronomer/publication
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