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texture-based segmentation with Gabor filters, wavelet and pyramid decompositions for extracting individual surface features from areal surface topography maps

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MEASUREMENT SCIENCE AND TECHNOLOGY 2015年第9期26卷

作者： Senin, Nicola Leach, Richard K. Pini, Stefano Blunt, Liam A. Univ Perugia Dept Engn I-06100 Perugia Italy Univ Nottingham Dept Mech Mat & Mfg Engn Nottingham NG7 2RD England Univ Parma SITEIA PARMA Interdept Ctr I-43100 Parma Italy Univ Huddersfield EPRSC Ctr Innovat Mfg Adv Metrol Huddersfield HD1 3DH W Yorkshire England

Areal topography segmentation plays a fundamental role in those surface metrology applications concerned with the characterisation of individual topography features. Typical scenarios include the dimensional inspection and verification of micro-structured surface features, and the identification and characterisation of localised defects and other random singularities. While morphological segmentation into hills or dales is the only partitioning operation currently endorsed by the ISO specification standards on surface texture metrology, many other approaches are possible, in particular adapted from the literature on digital image segmentation. In this work an original segmentation approach is introduced and discussed, where topography partitioning is driven by information collected through the application of texture characterisation transforms popular in digital image processing. Gabor filters, wavelets and pyramid decompositions are investigated and applied to a selected set of test cases. The behaviour, performance and limitations of the proposed approach are discussed from the viewpoint of the identification and extraction of individual surface topography features.

关键词： Surface topography texture-based segmentation Gabor filters wavelet decomposition pyramid decompositions

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Three dimensional multi-scale visual words for texture-based cerebellum segmentation

Three dimensional multi-scale visual words for texture-based...

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Conference on Medical Imaging - Image Processing

作者： Foncubierta-Rodriguez, Antonio Depeursinge, Adrien Gui, Laura Mueller, Henning Univ Appl Sci Western Switzerland HES SO Sierre Switzerland Univ Geneva Univ Hosp Geneva Dept Radiol & Med Informat Geneva Switzerland Univ Geneva Fac Med Geneva Switzerland

ISBN: (纸本)9780819489630

segmentation of the various parts of the brain is a challenging area in medical imaging and it is a prerequisite for many image analysis tasks useful for clinical research. Advances have been made in generating brain image templates that can be registered to automatically segment regions of interest in the human brain. However, these methods may fail with some subjects if there is a significant shape distortion or difference from the proposed models. This is also the case of newborns, where the developing brain strongly differs from adult magnetic resonance imaging (MRI) templates. In this article, a texture-based cerebellum segmentation method is described. The algorithm presented does not use any prior spatial knowledge to segment the MRI images. Instead, the system learns the texture features by means of a multi-scale filtering and visual words feature aggregation. Visual words are a commonly used technique in image retrieval. Instead of using visual features directly, the features of specific regions are modeled (clustered) into groups of discriminative features. This means that the final feature space can be reduced in size and also that the visual words in local regions are really discriminative for the given data set. The system is currently trained and tested with a dataset of 18 adult brain MRIs. An extension to the use with newborn brain images is being foreseen as this could highlight the advantages of the proposed technique. Results show that the use of texture features can be valuable for the task described and can lead to good results. The use of visual words can potentially improve robustness of existing shape-based techniques for cases with significant shape distortion or other differences from the models. As the visual words based techniques are not assuming any prior knowledge such techniques could be used for other types of segmentations as well using a large variety of basic visual features.

关键词： texture-based segmentation texture features visual words multi-scale image processing

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A MARKOV RANDOM-FIELD MODEL-based APPROACH TO UNSUPERVISED texture segmentation USING LOCAL AND GLOBAL SPATIAL STATISTICS

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1995年第6期4卷 856-862页

作者： KERVRANN, C HEITZ, F IRISA/INRIA Rennes.

Many studies have proven that statistical model-based texture segmentation algorithms yield good results provided that the model parameters and the number of regions be known a priori. In this correspondence, we present an unsupervised texture segmentation method that does not require knowledge about the different texture regions, their parameters, or the number of available texture classes. The proposed algorithm relies on the analysis of local and global second and higher order spatial statistics of the original images. The segmentation map is modeled using an augmented-state Markov random field, including an outlier class that enables dynamic creation of new regions during the optimization process. A Bayesian estimate of this map is computed using a deterministic relaxation algorithm. Results on real-world textured images are presented.

关键词： model parameters maps Optimization Procedure field simulation texture-based segmentation Markov random fields

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Fast segmentation of texture image regions based on hill-climbing

Fast segmentation of texture image regions based on hill-cli...

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IEEE Pacific Rim Conference on Communications Computers and Signal Processing (PACRIM 2003)

作者： Ohashi, T Aghbari, Z Makinouchi, A Kyushu Univ Grad Sch Informat Sci & Elect Engn Dept Intelligent Syst Fukuoka 8128581 Japan

ISBN: (纸本)0780379780

In this paper, we present a fast method for segmenting texture images. Since the texture features of images are generally present at various scales, a multiscale decomposition method is necessary to analyze the image effectively. Thus, this paper utilizes Gabor filters to extract texture features of images. Then, we use a simple and fast hill-climbing algorithm to detect coherent regions of an image. Our hill-climbing algorithm detects the peaks (local maxima) that represent clusters in the global texture histogram of an image. We utilize the histogram bins rather than the pixels themselves to find these peaks;thus, our algorithm can find the peaks efficiently.

关键词： texture-based segmentation hill-climbing method Gabor filters

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Automatic texture segmentation based on k-means Clustering and Efficient Calculation of Co-occurrence Features

Automatic Texture Segmentation Based on k-means Clustering a...

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International Conference on Systems, Signals and Image Processing

作者： L. de O. Bastos P. Liatsis A. Conci Instituto de Computacao Universidade Federal Fluminense Passo da Patria 156 Niteroi RJ Brazil

ISBN: (纸本)9788022728560

This work presents a method for automatic texture segmentation based on the k-means clustering technique and co- occurrence texture features. A set of eight features were extracted from the grey-level co-occurrence information. Two of them are proposed here to improve segmentation results for magnetic resonance (MR) images. All the features are used to segment image regions based on textural homogeneity of its areas. As the process of calculating the grey-level co-occurrence matrix (GLCM) demands increased computational resources, we propose a new methodology based on an indexed list (IL) for fast element access. This novel approach highly optimizes the algorithm called grey-level co-occurrence indexed list (GLCIL). Moreover, we compare the efficiency of the proposed method against two others, namely the traditional GLCM and the grey level co-occurrence linked list (GLCLL), which was suggested as a faster alternative to GLCM. The technique proposed here is the most efficient in terms of computational time, when compared to the other two. Additionally, examples on real time segmentation are presented to illustrate the appropriateness and robustness of this new method.

关键词： Co-occurrence matrix Optimization Textural segmentation Haralick's features MR images co-occurrence matrix texture-based segmentation region of an image segmentation computational time AUTOMATIC

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User-centric image segmentation using an interactive parameter adaptation tool

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PATTERN RECOGNITION 2010年第2期43卷 519-529页

作者： Pauplin, Olivier Caleb-Solly, Praminda Smith, Jim Univ W England Bristol Inst Technol Bristol BS16 1QY Avon England

Creating successful machine vision systems often begins a process of developing customised reliable image segmentation algorithms for the detection, and possibly categorisation of regions of interest within images. This can require significant investment of time from both the image processing and the domain experts to set up. Frequently this process is mediated via interviews, or language-based systems which may not fully capture the visual decision-making process of the domain experts. The resulting algorithms can also often be "brittle" in the sense of being highly specialised to the task for which they are tuned, and are consequently sensitive to changes in operating conditions or image specifications. One approach is to use interactive evolution for developing rapidly reconfigurable systems in which the users' tacit knowledge and requirements can be elicited and used for finding the appropriate parameters to achieve the required segmentation without any need for specialised knowledge of the underlying machine vision systems. This paper presents an interactive tool that can be used to quickly and easily evolve optimal image segmentation parameters from scratch. Building on previous work, the new algorithm reported here incorporates user-guided local search and makes the fitness function more flexible to facilitate the underlying multi-objective decision-making process. One of the key requirements for any interactive system is a high level of usability, both in terms of effectiveness-being able to build accurate models that meet end-user requirements-and efficiency-being able to achieve the required results within a minimal amount of time and undue effort. The system described in this paper has been designed with these considerations in mind to ensure a high level of user-experience of the interaction process. We present results from a series of experiments with a range of users to analyse the effect of the improvements that have been made over the previous system

关键词： Interactive evolution texture-based segmentation Evolutionary algorithm

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Resolution of problem with use of closing for texture segmentation

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ELECTRONICS LETTERS 2000年第20期36卷 1694-1696页

作者： Davies, ER Univ London Royal Holloway & Bedford New Coll Dept Phys Machine Vis Grp Egham TW20 0EX Surrey England

The morphological closing operation is widely known to be useful for separating regions containing high and low densities of particles. It is shown that the area of the region of high density becomes enlarged as the density of particles in the low density region increases - an effect that also occurs if impulse noise appears. It is found that the size increase is calculable and can approximately be cancelled by application of a suitable additional erosion operation, thereby improving the accuracy of the final representation.

关键词： Size enlargement High density PARTICLE DENSITY texture-based segmentation Impulse noise Surgery Resolving power image closing operation

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texture segmentation Using Sequential Kernel Density Approximation

Texture Segmentation Using Sequential Kernel Density Approxi...

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WRI International Conference on Communications and Mobile Computing

作者： Yang Hongbo Hou Xia Beijing Informat Sci & Technol Univ Sch Automat Beijing Peoples R China Beijing Informat Sci & Technol Univ Sch Comp Beijing Peoples R China

ISBN: (纸本)9780769535012

In this paper, we present a scheme for texture segmentation using sequential kernel density approximation. For sequential kernel density approximation, every texture regions intensity distribution can be described to a mixture Gaussian model, and the number of mixture model need not be set in advance. Exploiting intensity distributions directly leads to a region based measure for well-suited texture discrimination. Together with the mean of image intensity, a novel texture discrimination method is obtained. A demonstration of the performance of the scheme in this paper is given in the scope of texture segmentation.

关键词： texture-based segmentation texture discrimination Seeds Local density approximation approximation Sequential Kernel Scheme IMAGE INTENSITY

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GMRF models and wavelet decomposition for texture segmentation

GMRF models and wavelet decomposition for texture segmentati...

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International Conference on Image Processing

作者： Krishnamachari, S Chellappa, R UNIV MARYLAND DEPT ELECT ENGNCOLLEGE PKMD 20742

ISBN: (纸本)0780331222

A multichannel scheme for texture segmentation using Gauss Markov random fields (GMRF) is presented. We present a family of filters called Markov filters with a property that, when a homogeneous GMRF is filtered with these filters, the resulting output is also a GMRF. We use these filters to decompose images in a fashion similar to the wavelet decomposition, such that the individual subbands are also GMRFs. However, in wavelet decomposition, after filtering, the individual subbands are subsampled and Markov fields lose the Markov property when subsampled. We have shown in [Krishnamachari and Chellappa, 1995] that subsampled GMRFs can be efficiently approximated by Markov fields using the local conditional distribution invariance approximation. Hence individual subbands can be modeled by GMRFs. We have used this multichannel model to classify remote sensed imagery and to perform texture segmentation.

关键词： texture-based segmentation Filters Markov Subbands wavelets

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Multiscale complex moments of the local power spectrum

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION 2007年第3期24卷 618-625页

作者： Karlsson, Stefan Bigun, Josef Halmstad Univ Sch Informat Sci SE-30118 Halmstad Sweden

Complex moments of the local power spectrum (CMP) are investigated in a multiscale context. The multiscale CMPs are shown to approximate well the 1D angular Fourier transform of the band in question. This observation is used to derive further properties of the power spectrum in terms of texture orientations or n-folded symmetry patterns. A method is presented to approximate the power spectrum using only separable filtering in the spatial domain. Interesting implications to the Gabor decomposition are shown. The number of orientations in the filter bank is related to the order of n-folded symmetry detectable. Furthermore, the multiscale CMP's can be estimated incrementally in the spatial domain, which is both fast and reliable. Experiments on power spectrum estimation, orientation estimation, and texture segmentation are presented. (c) 2007 Optical Society of America.

关键词： Filter banks spatial domain power spectra texture-based segmentation Moment of force

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