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

Modelling the flow behaviour, recrystallisation and crystallographic texture in hot deformed Fe-30wt%Ni Austenite

Abstract: The present work describes a hybrid modelling approach developed for predicting the flow behaviour, recrystallisation characteristics and crystallographic texture evolution in a Fe-30wt%Ni austenitic model alloy subjected to hot plane strain compression. A series of compression tests were performed at temperatures between 850 and 1050ºC and strain rates between 0.1 and 10 s-1. The evolution of grain structure, crystallographic texture and dislocation substructure was characterised in detail for a deformation temperature of 950ºC and strain rates of 0.1 and 10 s-1, using electron backscatter diffraction and transmission electron microscopy. The hybrid modelling method utilises a combination of empirical, physically-based and neuro-fuzzy models. The flow stress is described as a function of the applied variables of strain rate and temperature using an empirical model. The recrystallisation behaviour is predicted from the measured microstructural state variables of internal dislocation density, subgrain size and misorientation between subgrains using a physically-based model. The texture evolution is modelled using artificial neural networks

Entering the digital world (Pedometrics 2009)

Development in pedometrics has not only shaped the research agenda in soil science but also attracted the attention of practitioners from other communities such as environmental modelling and land management who require digital information on soils. At the same time, demands from these communities and developments in information technology help to fuel and drive the research agenda of pedometrics. These factors have combined to draw scientists with diverse backgrounds and interests into the field of pedometrics over its short history as a distinctive subdiscipline of soil science

A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain

Detecting camouflaged moving foreground objects has been known to be difficult due to the similarity between the foreground objects and the background. Conventional methods cannot distinguish the foreground from background due to the small differences between them and thus suffer from under-detection of the camouflaged foreground objects. In this paper, we present a fusion framework to address this problem in the wavelet domain. We first show that the small differences in the image domain can be highlighted in certain wavelet bands. Then the likelihood of each wavelet coefficient being foreground is estimated by formulating foreground and background models for each wavelet band. The proposed framework effectively aggregates the likelihoods from different wavelet bands based on the characteristics of the wavelet transform. Experimental results demonstrated that the proposed method significantly outperformed existing methods in detecting camouflaged foreground objects. Specifically, the average F-measure for the proposed algorithm was 0.87, compared to 0.71 to 0.8 for the other state-of-the-art methods.Comment: 13 pages, accepted by IEEE TI

Visual Importance-Biased Image Synthesis Animation

Present ray tracing algorithms are computationally intensive, requiring hours of computing time for complex scenes. Our previous work has dealt with the development of an overall approach to the application of visual attention to progressive and adaptive ray-tracing techniques. The approach facilitates large computational savings by modulating the supersampling rates in an image by the visual importance of the region being rendered. This paper extends the approach by incorporating temporal changes into the models and techniques developed, as it is expected that further efficiency savings can be reaped for animated scenes. Applications for this approach include entertainment, visualisation and simulation

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