Classification of ordered texture images using regression modelling and granulometric features

Structural information available from the granulometry of an image has been used widely in image texture analysis and classification. In this paper we present a method for classifying texture images which follow an intrinsic ordering of textures, using polynomial regression to express granulometric moments as a function of class label. Separate models are built for each individual moment and combined for back-prediction of the class label of a new image. The methodology was developed on synthetic images of evolving textures and tested using real images of 8 different grades of cut-tear-curl black tea leaves. For comparison, grey level co-occurrence (GLCM) based features were also computed, and both feature types were used in a range of classifiers including the regression approach. Experimental results demonstrate the superiority of the granulometric moments over GLCM-based features for classifying these tea images

Morphological granulometry for classification of evolving and ordered texture images.

In this work we investigate the use of morphological granulometric moments as texture descriptors to predict time or class of texture images which evolve over time or follow an intrinsic ordering of textures. A cubic polynomial regression was used to model each of several granulometric moments as a function of time or class. These models are then combined and used to predict time or class. The methodology was developed on synthetic images of evolving textures and then successfully applied to classify a sequence of corrosion images to a point on an evolution time scale. Classification performance of the new regression approach is compared to that of linear discriminant analysis, neural networks and support vector machines. We also apply our method to images of black tea leaves, which are ordered according to granule size, and very high classification accuracy was attained compared to existing published results for these images. It was also found that granulometric moments provide much improved classification compared to grey level co-occurrence features for shape-based texture images

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

Search for the Standard Model Higgs Boson in the Diphoton Decay Channel with 4.9 fb^(-1) of pp Collision Data at √s = 7 TeV with ATLAS

A search for the standard model Higgs boson is performed in the diphoton decay channel. The data used correspond to an integrated luminosity of 4.9  fb^(-1) collected with the ATLAS detector at the Large Hadron Collider in proton-proton collisions at a center-of-mass energy of √s = 7  TeV. In the diphoton mass range 110–150 GeV, the largest excess with respect to the background-only hypothesis is observed at 126.5 GeV, with a local significance of 2.8 standard deviations. Taking the look-elsewhere effect into account in the range 110–150 GeV, this significance becomes 1.5 standard deviations. The standard model Higgs boson is excluded at 95% confidence level in the mass ranges of 113–115 GeV and 134.5–136 GeV

Measurement of the W →τν_τ cross section in pp collisions at √s = 7 TeV with the ATLAS experiment

The cross section for the production of W bosons with subsequent decay W→τν_τ is measured with the ATLAS detector at the LHC. The analysis is based on a data sample that was recorded in 2010 at a proton–proton center-of-mass energy of √s = 7TeV and corresponds to an integrated luminosity of 34 pb^(−1). The cross section is measured in a region of high detector acceptance and then extrapolated to the full phase space. The product of the total W production cross section and the W→τν_τ branching ratio is measured to be σ^(tot) _(W→τντ) = 11.1±0.3 (stat)±1.7 (syst)±0.4 (lumi) nb

Measurement of the W^±Z production cross section and limits on anomalous triple gauge couplings in proton–proton collisions at √s = 7 TeV with the ATLAS detector

This Letter presents a measurement of W^± Z production in 1.02 fb^(−1) of pp collision data at √s = 7 TeV collected by the ATLAS experiment in 2011. Doubly leptonic decay events are selected with electrons, muons and missing transverse momentum in the final state. In total 71 candidates are observed, with a background expectation of 12.1 ± 1.4(stat.)^(+4.1)_(−2.0)(syst.) events. The total cross section for W^± Z production for Z/γ^∗ masses within the range 66 GeV to 116 GeV is determined to be σ^(tot)_(WZ) = 20.5^(+3.1)_(−2.8)(stat.)^(+1.4)_(−1.3)(syst.)^(+0.9)_(−0.8)(lumi.) pb, which is consistent with the Standard Model expectation of 17.3^(+1.3) _(0.8) pb. Limits on anomalous triple gauge boson couplings are extracted

Search for a Light Higgs Boson Decaying to Long-Lived Weakly Interacting Particles in Proton-Proton Collisions at √s = 7 TeV with the ATLAS Detector

A search for the decay of a light Higgs boson (120–140 GeV) to a pair of weakly interacting, long-lived particles in 1.94  fb^(-1) of proton-proton collisions at √s=7  TeV recorded in 2011 by the ATLAS detector is presented. The search strategy requires that both long-lived particles decay inside the muon spectrometer. No excess of events is observed above the expected background and limits on the Higgs boson production times branching ratio to weakly interacting, long-lived particles are derived as a function of the particle proper decay length

Search for contact interactions in dilepton events from pp collisions at √s = 7 TeV with the ATLAS detector

This Letter presents a search for contact interactions in the dielectron and dimuon channels using data from proton–proton collisions produced by the LHC at √s=7 TeV and recorded by the ATLAS detector. The data sample, collected in 2011, corresponds to an integrated luminosity of 1.08 and 1.21 fb^(−1) in the e^+e^− and μ^+μ^− channels, respectively. No significant deviations from the standard model are observed. Using a Bayesian approach with a prior flat in 1/Λ^2, the following 95% CL lower limits are placed on the energy scale of ℓℓqq contact interactions: Λ−>10.1 TeV (Λ^+>9.4 TeV) in the electron channel and Λ^−>8.0 TeV (Λ^+>7.0 TeV) in the muon channel for constructive (destructive) interference in the left–left isoscalar contact interaction model. Limits are also provided for a prior flat in 1/Λ^4

Search for an excess of events with an identical flavour lepton pair and significant missing transverse momentum in √s =7 TeV proton–proton collisions with the ATLAS detector

Results are presented of a search for particles decaying into final states with significant missing transverse momentum and exactly two identical flavour leptons (e, μ) of opposite charge in √s = 7 TeV collisions at the Large Hadron Collider. This channel is particularly sensitive to supersymmetric particle cascade decays producing flavour correlated lepton pairs. Flavour uncorrelated backgrounds are subtracted using a sample of opposite flavour lepton pair events. Observation of an excess beyond Standard Model expectations following this subtraction procedure would offer one of the best routes to measuring the masses of supersymmetric particles. In a data sample corresponding to an integrated luminosity of 35 pb^(−1) no such excess is observed. Model-independent limits are set on the contribution to these final states from supersymmetry and are used to exclude regions of a phenomenological supersymmetric parameter space