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

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

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

The texture of thin NiSi films and its effect on agglomeration

Nickel silicide films are used as contacting materials in the micro electronics industry. It was recently [1] discovered that these films exhibit a peculiar type of texture, which was called 'axiotaxy', whereby certain lattice planes in the NiSi grains are preferentially aligned to (110)-type lattice planes in the single crystal Si substrate. In this contribution, we present a quantitative study of this phenomenon, using both XRD pole figure measurements and EBSD. Furthermore, we report a correlation between the texture of these NiSi films and their morphological stability during annealing at high temperature. In spite of the small grain size in these films, EBSD could be used to determine the volume fractions of the various texture components. This provided quantitative support for the claim that axiotaxy is the main texture component in these films, as about 40% of the grains belong to one of the axiotaxial texture components, and the remaining fraction exhibits a random orientation. A discussion of the techniques used during the measurement and analysis of the EBSD data is presented, as this must be given special consideration in view of the peculiar type of texture encountered in these films. Secondly, both XRD and EBSD were performed after annealing the NiSi films at various temperatures and durations. It is known that thin NiSi films have a strong tendency to agglomerate [2]. Our data indicates a correlation between the texture evolution and the agglomeration of the NiSi layer. Grains with axiotaxial orientation were observed to grow and thicken during the annealing process, by consuming neighboring randomly oriented grains. This suggests that the texture of the NiSi layer is a determining factor for the morphological stability of the film. The fact that grains with axiotaxial orientation grow during heat treatment can be related to the one dimensional periodicity at the interface, which lowers the interface energy and thus provides a driving force for the preferred growth of these grains. The agglomeration of NiSi films results in a significant increase of the sheet resistance. Therefore, these results illustrate the importance of texture control for the application of these films as contacts in micro-electronic devices

Running-in wear modeling of honed surface for combustion engine cylinder liners

The texture change during running-in alters the performance and efficiency of a tribo-mechanical system. During mass production of cylinder liners, a final finishing stage known as ‘‘plateau honing’’ is commonly added to reduce the running-in wear process of the liner surface. The majority of researchers think that this operation improves the engine efficiency and decreases oil consumption. It was believed that there are close links between the surface topography of honed cylinders change and their wear resistance during running-in. However, these interactions have not yet been established. Some running-in wear models were developed in the open literature to predict topographical surface changes without considering the running-in conditions. The present paper thus investigates the various aspects of the wear modeling that caused running- in problems in honed surfaces and its implications on ring-pack friction performance. To illustrate this, plateau honing experiments under different conditions were first carried out on an instrumented vertical honing machine. The plateau honing experiments characterize the surface modifications during running-in wear of cast-iron engine bores using advanced characterization method. Based on the experimental evidence, a running-in wear model was developed. Finally, a numerical extension of the developed model was applied to solve the Reynolds equation by taking into account the real surface topographies of the engine bore. This enables us to predict realistic friction performance within the cylinder ring-pack tribosystem

Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning

We present morphological classifications obtained using machine learning for objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes, namely early types, spirals and point sources/artifacts. An artificial neural network is trained on a subset of objects classified by the human eye and we test whether the machine learning algorithm can reproduce the human classifications for the rest of the sample. We find that the success of the neural network in matching the human classifications depends crucially on the set of input parameters chosen for the machine-learning algorithm. The colours and parameters associated with profile-fitting are reasonable in separating the objects into three classes. However, these results are considerably improved when adding adaptive shape parameters as well as concentration and texture. The adaptive moments, concentration and texture parameters alone cannot distinguish between early type galaxies and the point sources/artifacts. Using a set of twelve parameters, the neural network is able to reproduce the human classifications to better than 90% for all three morphological classes. We find that using a training set that is incomplete in magnitude does not degrade our results given our particular choice of the input parameters to the network. We conclude that it is promising to use machine- learning algorithms to perform morphological classification for the next generation of wide-field imaging surveys and that the Galaxy Zoo catalogue provides an invaluable training set for such purposes.Comment: 13 Pages, 5 figures, 10 tables. Accepted for publication in MNRAS. Revised to match accepted version

Texture in thin film silicides and germanides : a review

Silicides and germanides are compounds consisting of a metal and silicon or germanium. In the microelectronics industry, silicides are the material of choice for contacting silicon based devices (over the years, CoSi2, C54-TiSi2, and NiSi have been adopted), while germanides are considered as a top candidate for contacting future germanium based electronics. Since also strain engineering through the use of Si1-xGex in the source/drain/gate regions of MOSFET devices is an important technique for improving device characteristics in modern Si-based microelectronics industry, a profound understanding of the formation of silicide/germanide contacts to silicon and germanium is of utmost importance. The crystallographic texture of these films, which is defined as the statistical distribution of the orientation of the grains in the film, has been the subject of scientific studies since the 1970s. Different types of texture like epitaxy, axiotaxy, fiber, or combinations thereof have been observed in such films. In recent years, it has become increasingly clear that film texture can have a profound influence on the formation and stability of silicide/germanide contacts, as it controls the type and orientation of grain boundaries (affecting diffusion and agglomeration) and the interface energy (affecting nucleation during the solid-state reaction). Furthermore, the texture also has an impact on the electrical characteristics of the contact, as the orientation and size of individual grains influences functional properties such as contact resistance and sheet resistance and will induce local variations in strain and Schottky barrier height. This review aims to give a comprehensive overview of the scientific work that has been published in the field of texture studies on thin film silicide/germanide contacts. Published by AIP Publishing

Effects of Instrumentation on Dental Microwear Textures: Reanalysis and Augmentation of an Early Hominin Sample

Dental microwear texture analysis has been refined to a methodology relying upon scanning confocal microscopy for its advantages of repeatability and standardized quantification. A new instrument, the Plu Neox (Sensofar Corp.) confocal profiler recently entered the market, sparking questions among dental anthropologists related to the advantages and efficacy of this new technology, which has better resolution and lighting properties than previously available white-lighted based confocal profilers. This thesis reports on three complementary studies that set out to evaluate the comparability of the Plu Neox to the Plu Standard system and assess its ability to distinguish primates on the basis of their microwear patterning. The first study examines a sample of hominin molars (Australopithecus africanus and Paranthropus robustus) for comparison with data previously scanned and analyzed on the University of Arkansas\u27 Plu Standard confocal microscope (Scott et al., 2005). The second study expands the sample of early hominins to determine whether an enlarged sample of A. africanus continues to show significant texture separation from P. robustus. And the third study examines extant primate microwear textures of pitheciids with known dietary differences to determine whether documented food-choice trends are reflected in microwear patterning obtained using the Plu Neox. Examining pitheciine molar facets in the past was not possible because of their small size. The new instrument provides higher resolution (0.11 um with a 150x objective compared to 0.18 um at 100x on the Plu Standard confocal), with a smaller work envelop for a comparable number of sampled points for texture analysis. Results of the first study generally correspond to the original texture analysis of 2005, and the expanded dataset in the second study shows increased variance but the same pattern of differences for A. africanus compared with P. robustus. The third study finds that the Plu Neox is capable of parsing broad diet-related differences in microwear textures among the pitheciids, indicating that the new instrument may become an effective instrument for the quantitative characterization and comparison of dental microwear textures to be utilized in laboratories around the world