Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification

This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.Esta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes

Learning sparse representations of depth

This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, typically obtained by laser range scanners or structured light depth cameras. Sparse representations are learned from the Middlebury database disparity maps and then exploited in a two-layer graphical model for inferring depth from stereo, by including a sparsity prior on the learned features. Since they capture higher-order dependencies in the depth structure, these priors can complement smoothness priors commonly used in depth inference based on Markov Random Field (MRF) models. Inference on the proposed graph is achieved using an alternating iterative optimization technique, where the first layer is solved using an existing MRF-based stereo matching algorithm, then held fixed as the second layer is solved using the proposed non-stationary sparse coding algorithm. This leads to a general method for improving solutions of state of the art MRF-based depth estimation algorithms. Our experimental results first show that depth inference using learned representations leads to state of the art denoising of depth maps obtained from laser range scanners and a time of flight camera. Furthermore, we show that adding sparse priors improves the results of two depth estimation methods: the classical graph cut algorithm by Boykov et al. and the more recent algorithm of Woodford et al.Comment: 12 page

Piecewise parameterised Markov random fields for semi-local Hurst estimation

Semi-local Hurst estimation is considered by incorporating a Markov random field model to constrain a wavelet-based pointwise Hurst estimator. This results in an estimator which is able to exploit the spatial regularities of a piecewise parametric varying Hurst parameter. The pointwise estimates are jointly inferred along with the parametric form of the underlying Hurst function which characterises how the Hurst parameter varies deterministically over the spatial support of the data. Unlike recent Hurst regularistion methods, the proposed approach is flexible in that arbitrary parametric forms can be considered and is extensible in as much as the associated gradient descent algorithm can accommodate a broad class of distributional assumptions without any significant modifications. The potential benefits of the approach are illustrated with simulations of various first-order polynomial forms

Content-based image retrieval of museum images

Content-based image retrieval (CBIR) is becoming more and more important with the advance of multimedia and imaging technology. Among many retrieval features associated with CBIR, texture retrieval is one of the most difficult. This is mainly because no satisfactory quantitative definition of texture exists at this time, and also because of the complex nature of the texture itself. Another difficult problem in CBIR is query by low-quality images, which means attempts to retrieve images using a poor quality image as a query. Not many content-based retrieval systems have addressed the problem of query by low-quality images. Wavelet analysis is a relatively new and promising tool for signal and image analysis. Its time-scale representation provides both spatial and frequency information, thus giving extra information compared to other image representation schemes. This research aims to address some of the problems of query by texture and query by low quality images by exploiting all the advantages that wavelet analysis has to offer, particularly in the context of museum image collections. A novel query by low-quality images algorithm is presented as a solution to the problem of poor retrieval performance using conventional methods. In the query by texture problem, this thesis provides a comprehensive evaluation on wavelet-based texture method as well as comparison with other techniques. A novel automatic texture segmentation algorithm and an improved block oriented decomposition is proposed for use in query by texture. Finally all the proposed techniques are integrated in a content-based image retrieval application for museum image collections

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

A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques

In this paper, we systematically review recent advances in surface inspection using computer vision andimage processing techniques, particularly those based on texture analysis methods. The aim is to reviewthe state-of-the-art techniques for the purposes of visual inspection and decision making schemes that areable to discriminate the features extracted from normal and defective regions. This field is so vast that itis impossible to cover all the aspects of visual inspection. This paper focuses on a particular but importantsubset which generally treats visual surface inspection as texture analysis problems. Other topics related tovisual inspection such as imaging system and data acquisition are out of the scope of this survey.The surface defects are loosely separated into two types. One is local textural irregularities which is themain concern for most visual surface inspection applications. The other is global deviation of colour and/ortexture, where local pattern or texture does not exhibit abnormalities. We refer this type of defects as shadeor tonality problem. The second type of defects have been largely neglected until recently, particularly whencolour imaging system has been widely used in visual inspection and where chromatic consistency plays animportant role in quality control. The emphasis of this survey though is still on detecting local abnormalities,given the fact that majority of the reported works are dealing with the first type of defects.The techniques used to inspect textural abnormalities are discussed in four categories, statistical approaches,structural approaches, filter based methods, and model based approaches, with a comprehensivelist of references to some recent works. Due to rising demand and practice of colour texture analysis inapplication to visual inspection, those works that are dealing with colour texture analysis are discussedseparately. It is also worth noting that processing vector-valued data has its unique challenges, which conventionalsurface inspection methods have often ignored or do not encounter.We also compare classification approaches with novelty detection approaches at the decision makingstage. Classification approaches often require supervised training and usually provide better performancethan novelty detection based approaches where training is only carried out on defect-free samples. However,novelty detection is relatively easier to adapt and is particularly desirable when training samples areincomplet