Fuzzy sets on 2D spaces for fineness representation

The analysis of the perceptual properties of texture plays a fundamental role in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. In this paper, we propose a methodology to model texture properties by means of fuzzy sets defined on bidimensional spaces. In particular, we have focused our study on the fineness property that is considered as the most important feature for human visual interpretation. In our approach, pairwise combinations of fineness measures are used as a reference set, which allows to improve the ability to capture the presence of this property. To obtain the membership functions, we propose to learn the relationship between the computational values given by the measures and the human perception of fineness. The performance of each fuzzy set is analyzed and tested with the human assessments, allowing us to evaluate the goodness of each model and to identify the most suitable combination of measures for representing the fineness presence

Perception-based fuzzy partitions for visual texture modelling

Visual textures in images are usually described by humans using linguistic terms related to their perceptual properties, like “very coarse”, “low directional”, or “high contrasted”. Computational models with the ability of providing a perceptual texture characterization on the basis of these terms can be very useful in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. In this paper, we address the problem of simulating the human perception of texture, obtaining linguistic labels to describe it in natural language. For this modeling, fuzzy partitions defined on the domain of some of the most representative measures of each property are employed. In order to define the fuzzy partitions, the number of linguistic labels and the parameters of the membership functions are calculated taking into account the relationship between the computational values given by the measures and the human perception of the corresponding property. The performance of each fuzzy partition is analyzed and tested using the human assessments, and a ranking of measures is obtained according to their ability to represent the perception of the property, allowing to identify the most suitable measure

An adaptive fuzzy approach for modelling visual texture properties

The analysis of the perceptual properties of texture plays a fundamental role in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. The presence of these properties in images is very difficult to characterize due to their imprecision, and, moreover, because their perception may change depending on the user or the image context. In this paper, texture properties are modeled by means of an adaptive fuzzy approach that takes into account the subjectivity of the human perception. For this purpose, a methodology in two phases has been proposed. First, non-adaptive fuzzy models, that represent the average human perception about the presence of the texture properties, are obtained. For this modeling, we propose to learn a relationship between representative measures of the properties and the assessments given by human subjects. In a second phase, the obtained fuzzy sets are adapted in order to model the particular perception of the properties that a user may have, as well as the changes in perception influenced by the image context. For this purpose, the membership functions are automatically transformed on the basic of the information given by the user or extracted from the image context, respectively

A New Approach to Automatic Saliency Identification in Images Based on Irregularity of Regions

This research introduces an image retrieval system which is, in different ways, inspired by the human vision system. The main problems with existing machine vision systems and image understanding are studied and identified, in order to design a system that relies on human image understanding. The main improvement of the developed system is that it uses the human attention principles in the process of image contents identification. Human attention shall be represented by saliency extraction algorithms, which extract the salient regions or in other words, the regions of interest. This work presents a new approach for the saliency identification which relies on the irregularity of the region. Irregularity is clearly defined and measuring tools developed. These measures are derived from the formality and variation of the region with respect to the surrounding regions. Both local and global saliency have been studied and appropriate algorithms were developed based on the local and global irregularity defined in this work. The need for suitable automatic clustering techniques motivate us to study the available clustering techniques and to development of a technique that is suitable for salient points clustering. Based on the fact that humans usually look at the surrounding region of the gaze point, an agglomerative clustering technique is developed utilising the principles of blobs extraction and intersection. Automatic thresholding was needed in different stages of the system development. Therefore, a Fuzzy thresholding technique was developed. Evaluation methods of saliency region extraction have been studied and analysed; subsequently we have developed evaluation techniques based on the extracted regions (or points) and compared them with the ground truth data. The proposed algorithms were tested against standard datasets and compared with the existing state-of-the-art algorithms. Both quantitative and qualitative benchmarking are presented in this thesis and a detailed discussion for the results has been included. The benchmarking showed promising results in different algorithms. The developed algorithms have been utilised in designing an integrated saliency-based image retrieval system which uses the salient regions to give a description for the scene. The system auto-labels the objects in the image by identifying the salient objects and gives labels based on the knowledge database contents. In addition, the system identifies the unimportant part of the image (background) to give a full description for the scene

Visualization Techniques for Tongue Analysis in Traditional Chinese Medicine

Visual inspection of the tongue has been an important diagnostic method of Traditional Chinese Medicine (TCM). Clinic data have shown significant connections between various viscera cancers and abnormalities in the tongue and the tongue coating. Visual inspection of the tongue is simple and inexpensive, but the current practice in TCM is mainly experience-based and the quality of the visual inspection varies between individuals. The computerized inspection method provides quantitative models to evaluate color, texture and surface features on the tongue. In this paper, we investigate visualization techniques and processes to allow interactive data analysis with the aim to merge computerized measurements with human expert's diagnostic variables based on five-scale diagnostic conditions: Healthy (H), History Cancers (HC), History of Polyps (HP), Polyps (P) and Colon Cancer (C)

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

Adaptive Multidimensional Fuzzy Sets for Texture Modeling

The modeling of the perceptual properties of texture plays a fundamental role in tasks where some interaction with subjects is needed. In order to face the imprecision related to these properties, fuzzy sets defined on the domain of computational measures of the corresponding property are usually employed. In this sense, the most interesting approaches show that the combination of different measures as reference sets improve the texture characterization. However, the main drawback of these proposals is that they do not take into account the subjectivity associated with human perception. For example, the perception of a texture property may change depending on the user, and in addition, the image context may influence the global perception of a given property. In this paper, we propose to solve these problems by combining the use of several computational measures in a reference set with adaptation to the subjectivity of human perception. To do this, we propose a generic methodology that automatically transforms any multidimensional fuzzy set modeling a texture property to the particular perception of a new user or to the image context. For this purpose, the information given by the user, or extracted from the textures present in the image, are employed

Textural Difference Enhancement based on Image Component Analysis

In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method