bluuebunny/crossref_metadata_2025 huggingface dataset:
KNOWLEDGE-BASED SELF-ADAPTATION IN EVOLUTIONARY SEARCH
Authors: CHUNG CHAN-JIN , REYNOLDS ROBERT G.
Published: April 2003

Self-adaptation has been frequently employed in evolutionary computation. Angeline1 defined three distinct adaptive levels which are: population, individual and component levels. Cultural Algorithms have been shown to provide a framework in which to model self-adaptation at each of these levels. Here, we examine the role that different forms of knowledge can play in the self-adaptation process at the population level for evolution-based function optimizers. In particular, we compare the relative performance of normative and situational knowledge in guiding the search process. An acceptance function using a fuzzy inference engine is employed to select acceptable individuals for forming the generalized knowledge in the belief space. Evolutionary programming is used to implement the population space. The results suggest that the use of a cultural framework can produce substantial performance improvements in execution time and accuracy for a given set of function minimization problems over population-only evolutionary systems.
KNOWLEDGE BASE REFORMATION: PREPARING FIRST-ORDER THEORIES FOR EFFICIENT PROPOSITIONAL REASONING
Authors: PRENDINGER HELMUT , ISHIZUKA MITSURU , SCHURZ GERHARD
Published: April 2003

We present an approach to knowledge compilation that transforms a function-free first-order Horn knowledge base to propositional logic. This form of compilation is important since the most efficient reasoning methods are defined for propositional logic, while knowledge is most conveniently expressed within a first-order language. To obtain compact propositional representations, we employ techniques from (ir)relevance reasoning as well as theory transformation via unfold/fold transformations. Application areas include diagnosis, planning, and vision. Preliminary experiments with a hypothetical reasoner indicate that our method may yield significant speed-ups.
PRACTICAL ISSUES IN MODELING LARGE DIAGNOSTIC SYSTEMS WITH MULTIPLY SECTIONED BAYESIAN NETWORKS
Authors: XIANG Y. , OLESEN K. G. , JENSEN F. V.
Published: April 2003

As Bayesian networks become widely accepted as a normative formalism for diagnosis based on probabilistic knowledge, they are applied to increasingly larger problem domains. These large projects demand a systematic approach to handle the complexity in knowledge engineering. The needs include modularity in representation, distribution in computation, as well as coherence in inference. Multiply Sectioned Bayesian Networks (MSBNs) provide a distributed multiagent framework to address these needs.

According to the framework, a large system is partitioned into subsystems and represented as a set of related Bayesian subnets. To ensure exact inference, the partition of a large system into subsystems and the representation of subsystems must follow a set of technical constraints. How to satisfy these goals for a given system may not be obvious to a practitioner. In this paper, we address three practical modeling issues.
A KNOWLEDGE-BASED APPROACH TO COOPERATIVE RELATIONAL DATABASE QUERYING
Authors: BRAGA JOSÉ LUÍS , LAENDER ALBERTO H. F. , VANDER RAMOS CLAUDINEY
Published: April 2003

We present in this paper an approach to providing cooperativeness in database querying using artificial intelligence techniques. The main focus is a cooperative interface that assists nonexperienced and casual users in extracting useful answers from a relational database. Our approach relies on an architecture that comprises two knowledge bases which store rules that describe the application domain and guide the process of query formulation and answering. A subset of SQL is used for expressing queries, and the cooperative interface relieves the user from knowing its full syntax and the structure of the database.
OFF-LINE UNCONSTRAINED HANDWRITTEN WORD RECOGNITION
Authors: CAI JINHAI , LIU ZHI-QIANG
Published: April 2003

In this paper, we describe our system for writer independent, off-line unconstrained handwritten word recognition. We have developed a new method to automatically determine the parameters of Gabor filters to extract features from slant and tilt corrected images. An algorithm is also developed to translate 2D images to 1D domain. Finally, we propose a modified dynamic programming method with fuzzy theory to recognize words. Our initial experiments have shown promising results.
PARALLEL REDUCTION OPERATORS FOR GRAY-TONE PICTURES
Authors: ARCELLI CARLO , SERINO LUCA
Published: April 2003

Reduction operators iteratively applied to gray-tone pictures cause dilation of the regions, the gray-value of which is locally minimal, so that a gray-tone picture with no distinction between foreground and background is transformed into a set of bottom regions separated by ridge lines. The set of ridge lines can be understood as the skeleton of what is perceived as foreground. We describe a two-subiteration parallel skeletonization algorithm employing new reduction operators which are based on the nonridge point condition and are topology preserving. The performance of the algorithm is discussed, in particular with respect to certain critical configurations, and experimental work is reported to evaluate the thinning capability of the proposed reduction operators.
GENETIC ALGORITHMS FOR ERROR-BOUNDED POLYGONAL APPROXIMATION
Authors: SUN YUNG-NIEN , HUANG SHU-CHIEN
Published: April 2003

A new polygonal approximation algorithm, employing the concept of genetic evolution, is presented. In the proposed method, a chromosome is used to represent a polygon by a binary string. Each bit, called a gene, represents a point on the given curve. Three genetic operators, including selection, crossover, and mutation, are designed to obtain the approximated polygon whose error is bounded by a given norm. Many experiments show that the convergence is guaranteed and the optimal or near-optimal solutions can be obtained. Compared with the Zhu–Seneviratne algorithm,24 the proposed algorithm successfully reduced the number of segments under the same error condition in the polygonal approximation.
RECOGNIZING 3D OBJECTS BY USING MODELS LEARNED AUTOMATICALLY FROM 2D TRAINING IMAGES
Authors: JIA JIN , ABE KEIICHI
Published: April 2003

A scheme for learning and recognizing 3D objects from their 2D views is presented. The scheme proceeds in two stages. In the first stage, we try to learn a prototype automatically from 2D training images of different objects which belong to the same object class and consequently have similar shapes of parts and similar adjacency relations between the parts. In the second stage, the generated prototype is used to recognize learned objects or the objects similar to the learned ones from images of complex real scenes. We tested the approach on recognizing some simple objects from images of indoor scenes and got satisfactory results.
AN INTEGER RECURRENT ARTIFICIAL NEURAL NETWORK FOR CLASSIFYING FEATURE VECTORS
Authors: BROUWER ROELOF K.
Published: April 2003

The main contribution of this paper is the development of an Integer Recurrent Artificial Neural Network (IRANN) for classification of feature vectors. The network consists both of threshold units or perceptrons and of counters, which are non-threshold units with binary input and integer output. Input and output of the network consists of vectors of natural numbers that may be used to represent feature vectors. For classification purposes, representatives of sets are stored by calculating a connection matrix such that all the elements in a training set are attracted to members of the same training set. The class of its attractor then classifies an arbitrary element if the attractor is a member of one of the original training sets. The network is successfully applied to the classification of sugar diabetes data, credit application data, and the iris data set.
FUZZY INFORMATION GRANULATION ON BLOOD VESSEL EXTRACTION FROM 3D TOF MRA IMAGE
Authors: KOBASHI SYOJI , KAMIURA NAOTAKE , HATA YUTAKA , MIYAWAKI FUJIO
Published: April 2003

This paper shows an application of fuzzy information granulation (fuzzy IG) to medical image segmentation. Fuzzy IG is to derive fuzzy granules from information. In the case of medical image segmentation, information and fuzzy granules correspond to an image taken from a medical scanner, and anatomical parts, namely region of interests (ROIs), respectively. The proposed method to granulate information is composed of volume quantization and fuzzy merging. Volume quantization is to gather similar neighboring voxels. The generated quanta are selectively merged according to degrees for pre-defined fuzzy models that represent anatomical knowledge of medical images. The proposed method was applied to blood vessel extraction from three-dimensional time-of-flight (TOF) magnetic resonance angiography (MRA) images of the brain. The volume data studied in this work is composed of about 100 contiguous and volumetric MRA images. According to the fuzzy IG concept, information correspond to the volume data, fuzzy granules corresponds to the blood vessels and fat. The qualitative evaluation by a physician was done for two- and three-dimensional images generated from the obtained blood vessels. The evaluation shows that the method can segment MRA volume data, and that fuzzy IG is applicable to, and suitable for medical image segmentation.
HIPPOCAMPUS EXTRACTION BASED ON PARALLEL MULTISCALE STRUCTURE MATCHING
Authors: HIRANO SHOJI , KAMIURA NAOTAKE , MATSUI NOBUYUKI , HATA YUTAKA
Published: April 2003

This paper proposes a method for extracting the human hippocampus based on multiscale structure matching scheme. Focusing on the feature that an overextraction occurs on anatomically specific place, the method detects the redundancy by comparing with given desired models. Since each of the desired models has information about locations of their redundant segments, the place of corresponding redundancy can be specified on the overextracted object. Then, subtle intensity difference around their connecting place is investigated to separate the hippocampus and redundancy. The matching process can proceed in parallel for various types of redundancy and individual variances. Qualitative evaluation of a physician shows that our method can detect the redundancies and extract hippocampus correctly.
ON THE STUDY OF VLSI DERIVATION FOR OPTICAL FLOW ESTIMATION
Authors: MÉMIN ÉTIENNE , RISSET TANGUY
Published: April 2003

In this paper we propose studying several ways to implement a realistic and efficient VLSI design for a gradient-based dense motion estimator. The kind of estimator we focus on belongs to the class of differential methods. It is classically based on the optical flow constraint equation in association with a smoothness regularization term and also incorporates robust cost functions to alleviate the influence of large residuals. This estimator is expressed as the minimization of a global energy function defined within the framework of an incremental formulation associated with a multiresolution setup. In order to make possible the conception of efficient hardware, we consider a modified minimization strategy. This new minimization strategy is not only well suited to VLSI derivation, it is also very efficient in terms of quality of the result. The complete VLSI derivation is realized using high-level specifications.
QUALITY AND COMPLEXITY BOUNDS OF LOAD BALANCING ALGORITHMS FOR PARALLEL IMAGE PROCESSING
Authors: MIGUET SERGE , PIERSON JEAN-MARC
Published: April 2003

The parallel implementation of image processing algorithms implies an important choice of data distribution strategy. In order to handle the specific constraints associated with images, data distribution must take into account not only the locality of the data and its geometrical regularity but also the possible irregular computation costs associated with different image elements. A widely studied field to tackle this problem is the family of methods related to rectilinear partitioning. We introduce two fully parallel heuristics that compute suboptimal partitions, with a better complexity than the best known algorithms that compute optimal partitions. In this paper, we compare our heuristics to an optimal partitioning, both in terms of execution time and accuracy of the partition. We give some theoretical bounds on the quality of these heuristics that are corroborated by results of random numerical experiments and real applications.
A NOTE ON THREE-WAY TWO-DIMENSIONAL PROBABILISTIC TURING MACHINES
Authors: OKAZAKI TOKIO , INOUE KATSUSHI , ITO AKIRA , WANG YUE
Published: April 2003

This paper introduces a three-way two-dimensional probabilistic Turing machine (tr2-ptm), and investigates several properties of the machine. The tr2-ptm is a two-dimensional probabilistic Turing machine (2-ptm) whose input head can only move left, right, or down, but not up.

Let 2-ptms (resp. tr2-ptms) denote a 2-ptm (resp. tr2-ptm) whose input tape is restricted to square ones, and let 2-PTMs(S(n)) (resp. TR2-PTMs(S(n))) denote the class of sets recognized by S(n) space-bounded 2-ptms's (resp. tr2-ptms's) with error probability less than ½, where S(n): N→N is a function of one variable n (= the side-length of input tapes). Let TR2-PTM(L(m,n)) denote the class of sets recognized by L(m,n) space-bounded tr2-ptm's with error probability less than ½, where L(m,n): N2→N is a function of two variables m (= the number of rows of input tapes) and n (= the number of columns of input tapes).

The main results of this paper are:

(1) 2-NFAs - TR2-PTMs(S(n))≠ϕ for any S(n)=o(log n), where 2-NFAs denotes the class of sets of square tapes accepted by two-dimensional nondeterministic finite automata,

(2) TR2-PTMsS(n)[Formula: see text]2-PTMs(S(n)) for any S(n)=o(log n), and

(3) for any function g(n)=o(log n) (resp. g(n)=o(log n/log log n)) and any monotonic nondecreasing function f(m) which can be constructed by some one-dimensional deterministic Turing machine, TR2-PTM(f(m)+g(n)) (resp. TR2-PTM(f(m)×g(n))) is not closed under column catenation, column closure, and projection.

Additionally, we show that two-dimensional nondeterministic finite automata are equivalent to two-dimensional probabilistic finite automata with one-sided error in accepting power.
FINITE AUTOMATA AND DIGITAL IMAGES
Authors: RAMASUBRAMANIAN S. V. , KRITHIVASAN KAMALA
Published: April 2003

In this paper, we initially consider representation of 2D black–white images and 3D objects using finite state automata. We describe transformation of scaling on the 2D image by an operation on the FSA. We also give constructions for getting the projections of a 3D object on to coordinate planes and for reconstructing the 3D object from its projections. We define minimization of nondeterministic FSAs and give an O(e2) (e is the number of edges in the FSA) algorithm for minimization of NFAs. Later, we define a WFA and describe various properties of WFA. We define four normal forms of WFA and show how a WFA can be normalized into any of these forms. We show the equivalence of WFAs with ∊ edges and ∊-free WFAs. Then, we define minimization of WFAs and present an algorithm to minimize a WFA.
INFINITE ARRAYS AND RECOGNIZABILITY
Authors: DARE V. R. , SUBRAMANIAN K. G. , THOMAS D. G. , SIROMONEY R. , LE SAEC B.
Published: April 2003

In this paper, the concept of local languages is extended to infinite arrays and ωω-local languages are defined. ωω-recognizable languages of infinite arrays accepted by online tesselation automata are considered. Properties of these languages are studied. The notion of Muller recognizability is extended to infinite arrays. This is related to acceptance of infinite arrays by online tesselation automata.
SOME RESULTS ON ARRAY CONTEXTUAL GRAMMARS
Authors: RAMA RAGHAVAN , SMITHA T. A.
Published: April 2003

Array contextual grammars are new array models obtained by inserting arrays columnwise. Even though the growth is restricted to one direction, this array model is capable of generating pictures. Some interesting properties were given using column boundedness and bounded column length increase. An array model of cooperative type called column contextual array hybrid grammar is introduced; closure properties of these two models are investigated.
ON THE POWER OF PICTORIAL LANGUAGES
Authors: BOTTONI PAOLO , MAURI GIANCARLO , MUSSIO PIERO , PĂUN GHEORGHE
Published: April 2003

We continue the study of pictorial languages as formalized in Ref. 1 (based on the operations of shifting and superposing elementary pictures). If the pixels are superposed without composing their colors, then we produce only recursive languages. When the colors of the superposed pixels can be composed, then any array grammar can be simulated (hence, all recursively enumerable languages can be obtained). Bidimensional pictorial frameworks with a nonrecursive membership problem are obtained in the restricted case when (1) we do not allow the superposition of nontransparent pixels, excepting the fact that (2) for each color there is a complementary color, which, superposed on the original one, leads to a transparent pixel.
SIMULTANEOUS DETECTION OF LINES AND EDGES USING COMPOUND GABOR FILTERS
Authors: VAN DEEMTER J. H. , DU BUF J. M. H.
Published: April 2003

Lines and edges play an important role in pattern recognition because they mark object surfaces and boundaries. In spite of many attempts to construct optimal detectors, e.g. an edge detector, it appears that all known algorithms have problems at locations where lines and edges are very close and/or intersect. Furthermore, there are still very few schemes which can detect and classify lines and edges (events) simultaneously.

In view of the fact that the human visual system does not seem to suffer from any problems, our aim is to develop an event detection scheme that makes use of biologically-motivated operators and, therefore, to overcome the problems known from the literature. In this scheme we apply 2D complex Gabor filters and exploit the local phase information. In order to cope with events having a different curvature, we develop an adaptive scheme that is based on compound Gabor filter kernels with different orientation bandwidths.
BUILDING DETECTION USING BAYESIAN NETWORKS
Authors: STASSOPOULOU A. , CAELLI T.
Published: April 2003

This paper further explores the uses of Bayesian Networks for detecting buildings from digital orthophotos. This work differs from current research in building detection in so far as it utilizes the ability of Bayesian Networks to provide probabilistic methods for evidence combination and, via training, to determine how such evidence should be weighted to maximize classification. In this vein, then, we have also utilized expert performance to not only configure the network values but also to adapt the feature extraction pre-processing units to fit human behavior as closely as possible. Results from digital orthophotos of the Washington DC area prove that such an approach is feasible, robust and worth further analysis.
VISION GUIDED CIRCUMNAVIGATING AUTONOMOUS ROBOTS
Authors: BARNES NICK , LIU ZHI-QIANG
Published: April 2003

We present a system for vision guided autonomous circumnavigation, allowing a mobile robot to navigate safely around objects of arbitrary pose, and avoid obstacles. The system performs model-based object recognition from an intensity image. By enabling robots to recognize and navigate with respect to particular objects, this system empowers robots to perform deterministic actions on specific objects, rather than general exploration and navigation as emphasized in much of the current literature. This paper describes a fully integrated system, and, in particular, introduces canonical-views. Further, we derive a direct algebraic method for finding object pose and position for the four-dimensional case of a ground-based robot with uncalibrated vertical movement of its camera. Vision for mobile robots can be treated as a very different problem to traditional computer vision, as mobile robots have a characteristic perspective, and there is a causal relation between robot actions and view changes. Canonical-views are a novel, active object representation designed specifically to take advantage of the constraints of the robot navigation problem to allow efficient recognition and navigation.
AN IMPROVED EMBEDDED ZEROTREE WAVELET IMAGE CODING METHOD BASED ON COEFFICIENT PARTITIONING USING MORPHOLOGICAL OPERATION
Authors: ZHONG JUNMEI , LEUNG C. H. , TANG Y. Y.
Published: April 2003

In recent years, wavelets have attracted great attention in both still image compression and video coding, and several novel wavelet-based image compression algorithms have been developed so far, one of which is Shapiro's embedded zerotree wavelet (EZW) image compression algorithm. However, there are still some deficiencies in this algorithm. In this paper, after the analysis of the deficiency in EZW, a new algorithm based on quantized coefficient partitioning using morphological operation is proposed. Instead of encoding the coefficients in each subband line-by-line, regions in which most of the quantized coefficients are significant are extracted by morphological dilation and encoded first. This is followed by using zerotrees to encode the remaining space which has mostly zeros. Experimental results show that the proposed algorithm is not only superior to the EZW, but also compares favorably with the most efficient wavelet-based image compression algorithms reported so far.
A DEFECT DETECTION SCHEME FOR WEB SURFACE INSPECTION
Authors: IIVARINEN JUKKA , HEIKKINEN KATRIINA , RAUHAMAA JUHANI , VUORIMAA PETRI , VISA ARI
Published: April 2003

The goal of this work was to develop an improved defect detection scheme for high-speed real-time web surface inspection. This goal was realized by splitting the task into two independent parts: feature extraction and segmentation. Both parts were implemented using efficient algorithms which were implemented in hardware that is suitable and fast enough to be included in a working web inspection system. The proposed scheme is based on some derived texture features and a new self-organizing map variant, the statistical self-organizing map. These techniques offer several improvements over the gray-level thresholding techniques that have been traditionally used in commercial web inspection systems.
EDGE EXTRACTION OF IMAGES BY RECONSTRUCTION USING WAVELET DECOMPOSITION DETAILS AT DIFFERENT RESOLUTION LEVELS
Authors: FENG L. , SUEN C. Y. , TANG Y. Y. , YANG L. H.
Published: April 2003

This paper describes a novel method for edge feature detection of document images based on wavelet decomposition and reconstruction. By applying the wavelet decomposition technique, a document image becomes a wavelet representation, i.e. the image is decomposed into a set of wavelet approximation coefficients and wavelet detail coefficients. Discarding wavelet approximation, the edge extraction is implemented by means of the wavelet reconstruction technique. In consideration of the mutual frequency, overlapping will occur between wavelet approximation and wavelet details, a multiresolution-edge extraction with respect to an iterative reconstruction procedure is developed to ameliorate the quality of the reconstructed edges in this case. A novel combination of this multiresolution-edge results in clear final edges of the document images. This multi-resolution reconstruction procedure follows a coarser-to-finer searching strategy. The edge feature extraction is accompanied by an energy distribution estimation from which the levels of wavelet decomposition are adaptively controlled. Compared with the scheme of wavelet transform, our method does not incur any redundant operation. Therefore, the computational time and the memory requirement are less than those in wavelet transform.
PATTERNS OF PREPOSITIONAL ATTACHMENTS — WHERE DICTIONARY SEMANTICS MEETS CORPUS STATISTICS
Authors: HARABAGIU SANDA M.
Published: April 2003

This paper presents a novel methodology of disambiguating prepositional phrase attachments. We create patterns of attachments by classifying a collection of prepositional relations derived from Treebank parses. As a by-product, the arguments of every prepositional relation are semantically disambiguated. Attachment decisions are generated as the result of a learning process, that builds upon some of the most popular current statistical and machine learning techniques.

We have tested this methodology on (1) Wall Street Journal articles, (2) textual definitions of concepts from a dictionary and (3) an ad hoc corpus of Web documents, used for conceptual indexing and information extraction.
3D ARTICULATED OBJECT UNDERSTANDING, LEARNING, AND RECOGNITION FROM 2D IMAGES
Authors: WANG PATRICK S. P.
Published: April 2003

This paper is aimed at 3D object understanding from 2D images, including articulated objects in active vision environment, using interactive, and internet virtual reality techniques. Generally speaking, an articulated object can be divided into two portions: main rigid portion and articulated portion. It is more complicated that "rigid" object in that the relative positions, shapes or angles between the main portion and the articulated portion have essentially infinite variations, in addition to the infinite variations of each individual rigid portions due to orientations, rotations and topological transformations. A new method generalized from linear combination is employed to investigate such problems. It uses very few learning samples, and can describe, understand, and recognize 3D articulated objects while the objects status is being changed in an active vision environment.
MINIMUM DESCRIPTION LENGTH METHOD FOR FACET MATCHING
Authors: MAYBANK STEPHEN , FRAILE ROBERTO
Published: April 2003

The Minimum Description Length (MDL) criterion is used to fit a facet model of a car to an image. The best fit is achieved when the difference image between the car and the background has the greatest compression. MDL overcomes the overfitting and parameter precision problems which hamper the more usual maximum likelihood method of model fitting. Some preliminary results are shown.
DETECTION OF SEA SURFACE SMALL TARGETS IN INFRARED IMAGES BASED ON MULTILEVEL FILTER AND MINIMUM RISK BAYES TEST
Authors: MOON Y.-S. , ZHANG TIANXU , ZUO ZHENGRONG , ZUO ZHEN
Published: April 2003

This paper discusses the research in small target detection in infrared images with heavy clutter background. For most infrared images, ship objects are rather dim in the relative dark sea surface background. The existence of scan line disturbance and noise also increases the difficulty in proper detection. Dim objects must be distinguished from a dark background. On the other hand, the small targets must also be distinguished from clutters. Through analysis of the targets and background, we build characteristic models of small ship objects, noise and sea backgrounds respectively, and indicate their differences in spatial and frequency domains among them. Based on the principles of signal processing, pattern recognition and artificial intelligence, we propose a combined algorithm for detecting sea surface small targets. In this algorithm, components of background and noise are first suppressed by a multilevel filter designed accordingly, meanwhile enhancing the target ones of interest. The pixels of the candidate targets are then discriminated by minimum risk Bayes test. Finally, according to a priori knowledge about the targets such as the ranges of their sizes, the targets of interest can be detected. In particular, the related probability distributions used by statistic decision are obtained by offline learning of typical training samples. Experiments show that the algorithm is excellent for such kinds of target detection and is robust to noise.
MULTISPECTRAL IMAGE PROCESSING: THE NATURE FACTOR
Authors: WATKINS WENDELL R.
Published: April 2003

The images processed by our brain represent our window into the world. For some animals this window is derived from a single eye, for others, including humans, two eyes provide stereo imagery, for others like the black widow spider several eyes are used (8 eyes), and some insects like the common housefly utilize thousands of eyes (ommatidia). Still other animals like the bat and dolphin have eyes for regular vision, but employ acoustic sonar vision for seeing when their regular eyes do not work such as in pitch black caves or turbid water. Of course, other animals have adapted to dark environments by bringing along their own lighting such as the firefly and several creatures from the depths of the ocean floor. Animal vision is truly varied and has developed over millennia in many remarkable ways. We have learned a lot about vision processes by studying these animal systems and can still learn even more.
ON GEOMETRIC AND ORTHOGONAL MOMENTS
Authors: SHEN JUN , SHEN WEI , SHEN DANFEI
Published: April 2003

Moments are widely used in pattern recognition, image processing, computer vision and multiresolution analysis. To clarify and to guide the use of different types of moments, we present in this paper a study on the different moments and compare their behavior. After an introduction to geometric, Legendre, Hermite and Gaussian–Hermite moments and their calculation, we analyze at first their behavior in spatial domain. Our analysis shows orthogonal moment base functions of different orders having different number of zero-crossings and very different shapes, therefore they can better separate image features based on different modes, which is very interesting for pattern analysis and shape classification. Moreover, Gaussian–Hermite moment base functions are much more smoothed, they are thus less sensitive to noise and avoid the artifacts introduced by window function discontinuity. We then analyze the spectral behavior of moments in frequency domain. Theoretical and numerical analyses show that orthogonal Legendre and Gaussian–Hermite moments of different orders separate different frequency bands more effectively. It is also shown that Gaussian–Hermite moments present an approach to construct orthogonal features from the results of wavelet analysis. The orthogonality equivalence theorem is also presented. Our analysis is confirmed by numerical results, which are then reported.
AN INTEGRATED VISION SYSTEM FOR ALV NAVIGATION
Authors: YE XIUQING , LIU JILIN , GU WEIKANG
Published: April 2003

In this paper an integrated vision system for autonomous land vehicle is described. The vision system includes 2D and 3D vision modules and information fusion module. The task of 2D vision is to provide the physical and geometry information of road, and the task of 3D vision system is to detect the obstacles in the surrounding. Fusion module combines 2D and 3D information to generate a feasible region provided for vehicle navigation.
FUZZY BAYESIAN NETWORKS — A GENERAL FORMALISM FOR REPRESENTATION, INFERENCE AND LEARNING WITH HYBRID BAYESIAN NETWORKS
Authors: PAN HEPING , LIU LIN
Published: April 2003

This paper proposes a general formalism for representation, inference and learning with general hybrid Bayesian networks in which continuous and discrete variables may appear anywhere in a directed acyclic graph. The formalism fuzzifies a hybrid Bayesian network into two alternative forms: the first form replaces each continuous variable in the given directed acyclic graph (DAG) by a partner discrete variable and adds a directed link from the partner discrete variable to the continuous one. The mapping between two variables is not crisp quantization but is approximated (fuzzified) by a conditional Gaussian (CG) distribution. The CG model is equivalent to a fuzzy set but no fuzzy logic formalism is employed. The conditional distribution of a discrete variable given its discrete parents is still assumed to be multinomial as in discrete Bayesian networks. The second form only replaces each continuous variable whose descendants include discrete variables by a partner discrete variable and adds a directed link from that partner discrete variable to the continuous one. The dependence between the partner discrete variable and the original continuous variable is approximated by a CG distribution, but the dependence between a continuous variable and its continuous and discrete parents is approximated by a conditional Gaussian regression (CGR) distribution. Obviously, the second form is a finer approximation, but restricted to CGR models, and requires more complicated inference and learning algorithms. This results in two general approximate representations of a general hybrid Bayesian networks, which are called here the fuzzy Bayesian network (FBN) form-I and form-II. For the two forms of FBN, general exact inference algorithms exists, which are extensions of the junction tree inference algorithm for discrete Bayesian networks. Learning fuzzy Bayesian networks from data is different from learning purely discrete Bayesian networks because not only all the newly converted discrete variables are latent in the data, but also the number of discrete states for each of these variables and the CG or CGR distribution of each continuous variable given its partner discrete parents or both continuous and discrete parents have to be determined.
EXTRACTION OF BIBLIOGRAPHY INFORMATION BASED ON IMAGE OF BOOK COVER
Authors: YANG HUA , KASHIMURA MASAAKI , ONDA NORIKADU , OZAWA SHINJI
Published: April 2003

This paper describes a new system for extracting and classifying bibliography regions from the color image of a book cover. The system consists of three major components: preprocessing, color space segmentation and text region extraction and classification.

Preprocessing extracts the edge lines of the book and geometrically corrects and segments the input image, into the parts of front cover, spine and back cover.

The same as all color image processing researches, the segmentation of color space is an essential and important step here. Instead of RGB color space, HSI color space is used in this system. The color space is segmented into achromatic and chromatic regions first; and both the achromatic and chromatic regions are segmented further to complete the color space segmentation.

Then text region extraction and classification follow. After detecting fundamental features (stroke width and local label width) text regions are determined. By comparing the text regions on front cover with those on spine, all extracted text regions are classified into suitable bibliography categories: author, title, publisher and other information, without applying OCR.
RADAR TARGET RECOGNITION BASED ON PARAMETERIZED HIGH RESOLUTION RANGE PROFILES
Authors: LIAO XUEJUN , BAO ZHENG
Published: April 2003

A new scheme of radar target recognition based on parameterized high resolution range profiles (PHRRP) is presented in this paper. A novel criterion called generalized-weighted-normalized correlation (GWNC) is proposed for measuring the similarity between PHRRP's. By properly choosing the parameter of the mainlobe width in GWNC, aspect sensitivity of PHRRP's can be reduced without sacrificing their discriminative power. Performance of the scheme is evaluated using a dataset of three scaled aircraft models. The experimental results show that by using GWNC, only a small number of most dominant scatterers can achieve the same recognition rates as HRRP's, thus leading to a significant data reduction for the recognition system.
DETECTION OF ROADS FROM SATELLITE IMAGES USING OPTIMAL SEARCH
Authors: RIANTO YAN , KONDO SHOZO , KIM TALGUK
Published: April 2003

Since networks of main roads are basic information for the classification of use of the earth surface, the automatic detection of roads from satellite images is a very important issue. In this paper, a new detection theory is proposed which can overcome drawbacks of current theories and detect plural roads in an image with high speed and high precision. Firstly, binary images representing edges are used to evaluate the possibility for a road to pass on a given pixel. An 8-directions-filter, a clearing filter and a parallel-edge-detection filter are proposed which can bring insufficient local information to each other to obtain global information enough to detect a road and by which the possibility of a road-passing on the pixel can be effectively evaluated. Secondly, by using the Hough transform and the optimal search method it is possible to detect a complete road. This detection theory does not depend on the size of image and can detect all the roads in an image including intersecting and T-type roads.
A COMPENSATED FUZZY HOPFIELD NEURAL NETWORK FOR CODEBOOK DESIGN IN VECTOR QUANTIZATION
Authors: LIU SHAO-HAN , LIN JZAU-SHENG
Published: April 2003

In this paper, a new Hopfield-model net called Compensated Fuzzy Hopfield Neural Network (CFHNN) is proposed for vector quantization in image compression. In CFHNN, the compensated fuzzy c-means algorithm, modified from penalized fuzzy c-means, is embedded into Hopfield neural network so that the parallel implementation for codebook design is feasible. The vector quantization can be cast as an optimal problem that may also be regarded as a minimization of a criterion defined as a function of the average distortion between training vector and codevector. The CFHNN is trained to classify the divided vectors on a real image into feasible class to generate an available codebook when the defined energy function converges to near global minimum. The training vectors on a divided image are mapped to a two-dimensional Hopfield neural network. Also the compensated fuzzy c-means technique is used to update the quantization performance and to eliminate searching for the weighting factors. In the context of vector quantization, each training vector on the divided image is represented by a neuron which is fully connected by the other neurons. After a number of iterations, neuron states are refined to reach near optimal result when the defined energy function is converged.
IMAGE ENHANCEMENT BY KRIGING AND FUZZY SETS
Authors: PHAM TUAN D. , WAGNER MICHAEL
Published: April 2003

A kriging method is presented as a spatial filter for smoothing gray-scale images degraded by Gaussian white noise. The concepts are based on the analysis of semivariances, the linear combination scheme of kriging, and fuzzy sets. Application of fuzzy sets allows a gradual transition between two boundaries of semivariance levels as a criterion for smoothing the pixel values. This fuzzy thresholding also allows some degree of flexibility to suit various desired results for particular problems. Experimental results obtained by the fuzzy kriging filter are smoother and still preserve edges compared with those by the adaptive Wiener filter.
KNOWLEDGE-GUIDED CLASSIFICATION OF COASTAL ZONE COLOR IMAGES OFF THE WEST FLORIDA SHELF
Authors: ZHANG MINGRUI , HALL LAWRENCE O. , GOLDGOF DMITRY B. , MÜLLER-KARGER FRANK E.
Published: April 2003

A knowledge-guided approach to automatic classification of Coastal Zone Color images off the West Florida Shelf is described. The approach is used to identify red tides on the West Florida Shelf, as well as areas with high concentration of dissolved organic matter such as a river plume found seasonally along the West Florida coast over the middle of the shelf. The Coastal Zone Color images are initially segmented by the unsupervised Multistage Random Sampling Fuzzy c-Means algorithm. Then, a knowledge-guided system is applied to the centroid values of resultant clusters to label case I, case II waters, a dilute river plume ("green river"), and red tide. The domain knowledge base contains information on cluster distribution in feature space, as well as spatial information such as bathymetry data. Our knowledge base consists of a rule-guided system and an embedded neural network. From 60 images, after training the system, this procedure recognizes all 15 images which contained a river plume and 45 images without. The system can correctly classify 74% of the pixels that belong to the river plume, which provides a substantial advantage to users looking for offshore extensions of riverine influence. Red tides are also successfully identified in a time series of images for which ground truth confirmed the presence of a harmful bloom.
USING LABELED AND UNLABELED DATA FOR PROBABILISTIC MODELING OF FACE ORIENTATION
Authors: BALUJA SHUMEET
Published: April 2003

This paper describes probabilistic modeling methods to solve the problem of discriminating between five facial orientations with very little labeled data. Three models are explored. The first model maintains no inter-pixel dependencies, the second model is capable of modeling a set of arbitrary pair-wise dependencies, and the last model allows dependencies only between neighboring pixels. We show that for all three of these models, the accuracy of the learned models can be greatly improved by augmenting a small number of labeled training images with a large set of unlabeled images using Expectation–Maximization. This is important because it is often difficult to obtain image labels, while many unlabeled images are readily available. Through a large set of empirical tests, we examine the benefits of unlabeled data for each of the models. By using only two randomly selected labeled examples per class, we can discriminate between the five facial orientations with an accuracy of 94%; with six labeled examples, we achieve an accuracy of 98%.
IMAGE RECOGNITION USING SIMPLIFIED FUZZY ARTMAP AUGMENTED WITH A MOMENT BASED FEATURE EXTRACTOR
Authors: RAJASEKARAN S. , PAI G. A. VIJAYALAKSHMI
Published: April 2003

The capability of Kasuba's Simplified Fuzzy ARTMAP (SFAM) to behave as a Pattern Recognizer/Classifier of images both noisy and noise free has been investigated in this paper. This calls for augmenting the original Neuro–Fuzzy model with a modified moment-based RST invariant feature extractor.

The potential of the SFAM based Pattern Recognizer to recognize patterns — monochrome and color, noisy and noise free — has been studied on two experimental problems. The first experiment which concerns monochrome images, pertains to recognition of satellite images, a problem discussed by Wang et al. The second experiment, which concerns color images, deals with the recognition of some sample test colored patterns. The results of the computer simulation have also been presented.
SYMMETRIC STEREO WITH MULTIPLE WINDOWING
Authors: FUSIELLO ANDREA , ROBERTO VITO , TRUCCO EMANUELE
Published: April 2003

We present a new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions. The algorithm is an adaptive, multiwindow scheme using left–right consistency to compute disparity and its associated uncertainty. We demonstrate and discuss performances with both synthetic and real stereo pairs, and show how our results improve on those of closely related techniques for both accuracy and efficiency.
GENERALIZED APPROACH FOR FINDING BOUNDARY CODES FROM REGION REPRESENTATION OF SET-OF-CODES TYPE
Authors: SARKAR DEBRANJAN , DAS PRADIP K.
Published: April 2003

Linear Quadtree (LQ), Interpolation-Based Binary tree (IBB) and Minimized Boolean Function (MBF) are some of the set-of-codes type of encoding scheme for region representation of binary images. Chain Code (CC) is a well-known technique for boundary representation of such images. It is desirable to develop efficient methods of conversion between the two types of representation schemes. In this paper, we present a generalized algorithm for conversion from set-of-codes type of representation to chain codes. The computational complexity of the algorithm is also discussed.
KNOWLEDGE GRANULARITY SPECTRUM, ACTION PYRAMID, AND THE SCALING PROBLEM
Authors: YE YIMING , TSOTSOS JOHN K.
Published: April 2003

In this paper we introduce the concept of knowledge granularity and study the relationship between different knowledge representation schemes and the scaling problem. By scale to a task, we mean that an agent's planning system and knowledge representation scheme are able to generate the range of behaviors required by the task in a timely fashion. Action selection is critical to an agent performing a task in a dynamic, unpredictable environment. Knowledge representation is central to the agent's action selection process. It is important to study how an agent should adapt its methods of representation such that its performance can scale to different task requirements. Here we study the following issues. One is the knowledge granularity problem: to what detail should an agent represent a certain kind of knowledge if a single granularity of representation is to be used. Another is the representation scheme problem: to scale to a given task, should an agent represent its knowledge using a single granularity or a set of hierarchical granularities.
A BELIEF-GOAL-ROLE THEORY FOR MULTIAGENT SYSTEMS
Authors: CHAINBI WALID , BEN-HAMADOU ABDELMAJID , JMAIEL MOHAMED
Published: April 2003

Agent-oriented techniques represent a promising approach for engineering complex systems where interaction is probably the most important single characteristic. Accordingly, the recent years have witnessed the emergence of different approaches for the study of intelligent agent-based systems. One such architecture views the system as a rational agent having certain mental attitudes of Belief, Desire and Intention (BDI). This paper explores a particular type of rational agent, a Belief-Goal-Role agent. Unlike most previous work, our approach has been to characterize the mental state of the agents that leads them to take part in cooperative action. Hence, beliefs, goals and roles are relevant to our study of cooperation which have lead to the identification of communication concepts (beliefs and goals) and organization concepts (roles). The model is formalized by expressing it as a theory in a first-order, multi-modal, and linear-time logic. We use labeled transition systems to deal with the truth conditions of the formulae of our theory as well as with the behavioral semantics of our agents. We illustrate our work with the well-known prey/predator game.
DYNAMICALLY ORGANIZING KDD PROCESSES
Authors: ZHONG NING , LIU CHUNNIAN , OHSUGA SETSUO
Published: April 2003

How to increase both autonomy and versatility of a knowledge discovery system is a core problem and a crucial aspect of KDD (Knowledge Discovery and Data Mining). Within the framework of the KDD process and the GLS (Global Learning Scheme) system recently proposed by us, this paper describes a way of increasing both autonomy and versatility of a KDD system by dynamically organizing KDD processes. In our approach, the KDD process is modeled as an organized society of KDD agents with multiple levels. We propose an ontology to describe KDD agents, in the style of OOER (Object Oriented Entity Relationship) data model. Based on this ontology of KDD agents, we apply several AI planning techniques, which are implemented as a meta-agent, so that we might (1) solve the most difficult problem in a multiagent KDD system: how to automatically choose appropriate KDD techniques (KDD agents) to achieve a particular discovery goal in a particular application domain; (2) tackle the complexity of KDD process; and (3) support evolution of KDD data, knowledge and process. The GLS system, as a multistrategy and multiagent KDD system based on the methodology, increases both autonomy and versatility.
ALIFE: A MULTIAGENT COMPUTING PARADIGM FOR CONSTRAINT SATISFACTION PROBLEMS
Authors: LIU JIMING , JING HAN
Published: April 2003

This paper presents a new approach to solving N-queen problems, which involves a model of distributed autonomous agents with artificial life (ALIFE) and a method of representing N-queen constraints in an agent environment. The distributed agents locally interact with their living environment, i.e. a chessboard, and execute their reactive behaviors by applying their behavioral rules for randomized motion, least-conflict position searching, and cooperating with other agents, etc. The agent-based N-queen problem solving system evolves through selection and contest, in which some agents will die or be eaten if their moving strategies are less effective than others. The experimental results have shown that this system is capable of solving large-scale N-queen problems. This paper also provides a model of ALIFE agents for solving general CSPs.