This paper proposes a fuzzy logic approach, UFM (unified feature matching), for region-based image retrieval. In our retrieval system, an image is represented by a set of segmented regions each of which is characterized by a fuzzy feature (fuzzy set) reflecting color, texture, and shape properties. As a result, an image is associated with a family of fuzzy features corresponding to regions. Fuzzy features naturally characterize the gradual transition between regions (blurry boundaries) within an image, and incorporate the segmentation-related uncertainties into the retrieval algorithm. The resemblance of two images is then defined as the overall similarity between two families of fuzzy features, and quantified by a similarity measure, UFM measure, which integrates properties of all the regions in the images. Compared with similarity measures based on individual regions and on all regions with crisp-valued feature representations, the UFM measure greatly reduces the inuence of inaccurate segmentation, and provides a very intuitive quantification. The UFM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The performance of the system is illustrated using examples from an image database of about 60,000 general-purpose images.

Data mining is the discovery of previously unknown, potentially useful and hidden knowledge in databases. In this paper, we concentrate on the discovery of association rules. Many algorithms have been proposed to #nd association rules in databases with binary attributes. Weintroduce the fuzzy association rules of the form, 'If X is A then Y is B', to deal with quantitative attributes. X, Y are set of attributes and A, B are fuzzy sets which describe X and Y respectively. Using the fuzzy set concept, the discovered rules are more understandable to human. Moreover, fuzzy sets handle numerical values better than existing methods because fuzzy sets soften the e#ect of sharp boundaries. 1 Introduction During the past years, boolean association rule mining has received considerable attention. Boolean association rule mining tries to #nd consumer behavior in retail data. The discovered rule can tell, for example, people buy butter and milk will also buy bread. Such rules can be used in cust...

An algorithmic solution method is presented for the problem of autonomous robot motion in completely unknown environments. Our approach is based on the alternate execution of two fundamental processes: map building and navigation. In the former, range measures are collected through the robot exteroceptive sensors and processed in order to build a local representation of the surrounding area. This representation is then integrated in the global map so far reconstructed by filtering out insufficient or conflicting information. In the navigation phase, an A*-based planner generates a local path from the current robot position to the goal. Such path is safe inside the explored area and provides a direction for further exploration. The robot follows the path up to the boundary of the explored area, terminating its motion if unexpected obstacles are encountered. The most peculiar aspects of our method are the use of fuzzy logic for efficiently building and modifying the environment map, and ...

An essential component of an autonomous mobile robot is the heteroceptive sensory system. Sensing capabilities should be integrated with a method for extracting a representation of the environment from uncertain sensor data and with an appropriate planning algorithm. In this paper, fuzzy logic concepts are used to introduce a tool useful for robot perception as well as for planning collision-free motions. In particular, a map of the environment is defined as the fuzzy set of unsafe points, whose membership function quantifies the possibility for each point to belong to an obstacle. The computation of this set is based on a specific sensor model and makes use of intermediate sets generated from range measures and aggregated by means of fuzzy set operators. This general approach is applied to a robot with ultrasonic rangefinders. The resulting map building algorithm performs well, as confirmed by a comparison with stochastic methods. The planning problem on fuzzy maps can be ...

In meeting the challenges that resulted from the explosion of collected, stored, and transferred data, Knowledge Discovery in Databases or Data Mining has emerged as a new research area. However, the approaches studied in this area have mainly been oriented at highly structured and precise data. In addition, the goal to obtain understandable results is often neglected. Therefore we suggest to concentrate on Information Mining, i.e., the analysis of heterogeneous information sources with the prominent aim of producing comprehensible results. Since the aim of fuzzy technology has always been to model linguistic information and to achieve understandable solutions, we expect it to play an important role in information mining.

This report addresses the characterization of measurements that include epistemic uncertainties in the form of intervals. It reviews the application of basic descriptive statistics to data sets which contain intervals rather than exclusively point estimates. It describes algorithms to compute various means, the median and other percentiles, variance, interquartile range, moments, confidence limits, and other important statistics and summarizes the computability of these statistics as a function of sample size and characteristics of the intervals in the data (degree of overlap, size and regularity of widths, etc.). It also reviews the prospects for analyzing such data sets with the methods of inferential statistics such as outlier detection and regressions. The report explores the tradeoff between measurement precision and sample size in statistical results that are sensitive to both. It also argues that an approach based on interval statistics could be a reasonable alternative to current standard methods for evaluating, expressing and propagating measurement uncertainties.

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Intelligent agents embedded in physical environments need the ability to connect, or anchor, the symbols used to perform abstract reasoning to the physical entities which these symbols refer to. Anchoring must rely on perceptual data which is inherently affected by uncertainty. We propose an anchoring technique based on the use of fuzzy sets to represent uncertainty, and of degree of subset-hood to compute the partial match between signatures of objects. We show examples where we use this technique to allow a deliberative system to reason about the objects (cars) observed by a vision system embarked in an unmanned helicopter, in the framework of the Witas project.

Abstract. The monitoring of buildings, slide slopes and crustal movements is a central task of geodetic engineering. The aim is the generation of meaningful motion and deformation models in order to quickly and specifically initiate constructional or geotechnical safety measures. The adequateness of the actions depends essentially on the quality of the observation and analysis techniques. Therefore it is important to correctly derive the model parameters and their uncertainty budget considering that the model parameters are typically estimated from a large number of heterogeneous and redundant observations by means of a least-squares adjustment. Here, the uncertainty budget is assumed to comprise both random variability and remaining systematics (imprecision). In practice, there are outliers in the data which have to be detected and eliminated. In conventional techniques only random effects are taken into account. When imprecision is considered additionally, the test strategies have to be extended accordingly. In this study imprecise extensions are obtained for the estimated outliers which are tested statistically using one- and multidimensional hypotheses. The applied procedure is outlined in detail showing both theory and numerical examples.

Consider the problem of building the map of an unknown environment by using range readings obtained through ultrasonic sensors, and assume that a bitmap representation is adopted for compactness. In an ideal map, each cell of the bitmap is either empty or occupied by an obstacle. Because of the uncertainty introduced by sonar sensing, it is necessary to process appropriately the measures in order to classify each cell with a reasonable degree of accuracy. We compare three di#erent algorithms for ultrasonic map building that are based respectively on Probability Theory, Fuzzy Logic and Fuzzy Measures. Simulation results in a one-dimensional case and experimental results for an o#ce-like environment are presented to perform a comparison among the presented approaches. Keywords: Map building, ultrasonic sensors, fuzzy logic, fuzzy measures, probability theory 1 INTRODUCTION To perform navigation tasks in an unknown environment, a mobile robot needs to gather some information about the s...