bluuebunny/crossref_metadata_2025 huggingface dataset:
MACHINE VISION TOOLS FOR CAGD
Authors: KOIVUNEN V. , VEZIEN J.-M.
Published: September 2004

In this paper, the problem of constructing geometric models from data provided by 3-D imaging sensors is addressed. Such techniques allow for rapid modeling of sculptured free-form shapes and generation of geometric models for existing parts. In order for a complete data set to be obtained, multiple images, each from a different viewpoint, have to be merged. A technique stemming from the Iterative Closest Point (ICP) method for estimating the relative transformations among the viewpoints is developed. Computational solutions are provided for estimating shape from noisy sensory measurements using representations that conform with commonly used representations from Computer Aided Geometric Design (CAGD). In particular, NURBS and triangular surface representations are applied in shape estimation. The surface approximations are refined by the algorithms to meet a user-defined tolerance value.
GROWTH TRANSFORMATIONS FOR PROBABILISTIC FUNCTIONS OF STOCHASTIC GRAMMARS
Authors: CASACUBERTA FRANCISCO
Published: September 2004

Stochastic Grammars are the most usual models in Syntactic Pattern Recognition. Both components of a Stochastic Grammar, the characteristic grammar and the probabilities attached to the rules, can be learnt automatically from training samples. In this paper, first a review of some algorithms are presented to infer the probabilistic component of Stochastic Regular and Context-Free Grammars under the framework of the Growth Transformations.

On the other hand, with Stochastic Grammars, the patterns must be represented as strings over a finite set of symbols. However, the most natural representation in many Syntactic Pattern Recognition applications (i.e. speech) is as sequences of vectors from a feature vector space, that is, a continuous representation. Therefore, to obtain a discrete representation of the patterns, some quantization errors are introduced in the representation process. To avoid this drawback, a formal presentation of a semi-continuous extension of the Stochastic Regular and Context-Free Grammars is studied and probabilistic estimation algorithms are developed in this paper. In this extension, sequences of vectors, instead of strings of symbols, can be processed with Stochastic Grammars.
A NEW APPROACH FOR RECOGNIZING MULTIFONT CHINESE CHARACTERS USED IN A SPECIAL APPLICATION
Authors: CHEN LING-HWEI , LEE YEUAN-KUEN
Published: September 2004

Many approaches for recognizing all of the 5401 commonly used Chinese characters have been proposed, but they are complex. For some applications such as cheques, receipts, addresses, in Chinese, etc., only a limited set of printed characters is needed, but they are usually require a real time response and a high recognition rate. Based on this reason, here we provide a simpler approach to meet these two requirements. The approach takes horizontal strokes, vertical ones and crossings among these strokes as the character features. A fast modified Hough transform is provided to extract these features, it satisfies an important stroke property that a dotted line must not be a real stroke. In the learning stage, an error measure is defined and an optimal stroke matching algorithm is proposed to establish an accurate random model for each type of characters. In the recognition stage, another error measure is defined and a nearly-optimal matching algorithm is presented to speed up the recognition process. It is worth mentioning that the approach does not use any thinning process. Some experimental results are also provided to show the effectiveness of the proposed recognizer.
HOW CAN PERMUTATIONS BE USED IN THE EVALUATION OF ZONING ALGORITHMS?
Authors: LATIFI SHAHRAM
Published: September 2004

In processing a page image by a given zoning algorithm (automatic or manual), a certain text string is generated which may not be the same as the correct string. The difference may be due to the incorrect reading order selected by the employed zoning algorithm or poor recognition of characters. A difference algorithm is commonly used to find the best match between the generated string and the correct string. The output of such an algorithm will then be a sequence of matched substrings which are not in the correct order. To determine the performance of a given zoning algorithm, it is of interest to find the minimum number of moves needed to obtain the correct string from the string generated by that algorithm. The problem can be modeled as a sorting problem where a string of n integers ordered in a random manner, must be sorted in ascending (or descending) order. In this paper, we derive bounds on the time complexity of sorting a given string and present a near-optimal algorithm for that.
A COVERING ALGORITHM IN MACHINE LEARNING FROM THE POINT OF VIEW OF THE SET THEORY
Authors: KOČKOVÁ SYLVA , BRUHA IVAN
Published: September 2004

The empirical inductive algorithms that utilize the covering paradigm (such as the AQ x and CN x families of inductive systems) comprise various heuristics and statistical tools so that the core of the covering paradigm remains often quite hidden. The goal of this paper is thus to disclose theoretical underlying principles of covering learning algorithms. By exploiting the set theory, the paper exhibits how the correctness and generality required for decision rules induced by a covering algorithm may be satisfied. The principle differences between a genuine theoretical approach and actual empirical machine learning algorithms are also discussed.
LEARNING FROM EXAMPLES, AGENT TEAMS AND THE CONCEPT OF REFLECTION
Authors: BEYER UWE , ŚMIEJA FRANK J.
Published: September 2004

Learning from examples has a number of distinct algebraic forms, depending on what is to be learned from the available information. One of these forms is [Formula: see text], where the input-output tuple (x, y) is the available information, and G represents the process determining the mapping from x to y. Various models, y = f(x), of G can be constructed using the information from the (x, y) tuples. In general, and for real-world problems, it is not reasonable to expect the exact representation of G to be found (i.e. a formula that is correct for all possible (x, y)). The modeling procedure involves finding a satisfactory set of basis functions, their combination, a coding for (x, y) and then to adjust all free parameters in an approximation process, to construct a final model. The approximation process can bring the accuracy of the model to a certain level, after which it becomes increasingly expensive to improve further. Further improvement may be gained through constructing a number of agents {α}, each of which develops its own model fα. These may then be combined in a second modeling phase to synthesize a team model. If each agent has the ability for internal reflection the combination in a team framework becomes more profitable. We describe reflection and the generation of a confidence function: the agent's estimate of the correctness of each of its predictions. The presence of reflective information is shown to increase significantly the performance of a team.
ON VIRTUAL PARTITIONING OF LARGE DICTIONARIES FOR CONTEXTUAL POST-PROCESSING TO IMPROVE CHARACTER RECOGNITION
Authors: HOCH RAINER , KIENINGER THOMAS
Published: September 2004

This article presents a new approach to the partitioning of large dictionaries by virtual views. The basic idea is that additional knowledge sources of text recognition and text analysis are employed for fast dictionary look-up in order to prune search space through static or dynamic views. The heart of the system is a redundant hashing technique which involves a set of hash functions dealing with noisy input efficiently. Currently, the system is composed of two main system components: the dictionary generator and the dictionary controller. While the dictionary generator initially builds the system by using profiles and source dictionaries, the controller allows the flexible integration of different search heuristics. Results prove that our system achieves a significant speed-up of dictionary access time.
SYMMETRY ANALYSIS THROUGH WAVE PROPAGATION
Authors: MUKHERJEE DIPTI PRASAD , BRADY MICHAEL
Published: September 2004

A significant drawback of the symmetry set evaluation for a 2D shape using the wave diffusion process1 is its slow execution time caused, in large part, by the diffusion step. We first recall the need for a diffusion process. A parallel implementation of the wave diffusion algorithm on a transputer network is presented. A faster alternative approach to detect the symmetry set, which we call the normal transform, and which is similar to the wave propagation mechanism, is described.
MULTI-CLASS CLUSTERING BY ANALYTICAL TWO-CLASS FORMULAS
Authors: LIN JA-CHEN
Published: September 2004

This paper proposes a new clustering method based on the hierarchical use of the analytical two-class clustering tool introduced by Lin and Tsai.1 The method comprises two phases. In the first phase, called the splitting phase, the data set is hierarchically decomposed into some subsets. In the second phase, called the merging phase, the set-to-set distances between these subsets are checked so that some subsets can be merged back together to obtain better clustering results. We use the idea of the so-called dense cut to determine when to stop the splitting phase. We also use a trace-following technique for the so-called boundary data to reduce significantly the computational load involved in the merging phase. Two algorithms are provided, and many experiments are included to show that the data being processed are not required to be linearly separable, noiseless, or formed of spherical clusters.
FEATURE EXTRACTION IN CHARACTER RECOGNITION WITH ASSOCIATIVE MEMORY CLASSIFIER
Authors: ZHANG MING , SUEN CHING Y. , BUI TIEN D.
Published: September 2004

A pattern recognition system mainly contains two functional parts, i.e. feature extraction and pattern classification. The success of such a system depends on not only the effectiveness of each of them, but also their operation in concert. The feature extraction process in a traditional recognition system has two major tasks, namely, to extract deformation invariant signals and to reduce data. When a neural network is used as a pattern classifier, however, an alteration in these basic objectives is needed. In particular, the consideration of data reduction will be replaced by that of the suitability of feature vectors to the neural network. In this paper, feature extraction algorithms in character recognition have been designed based on these principles. The improvements made by these algorithms have been demonstrated in a series of experiments which justify such a change in the fundamental objectives of the feature extraction process when an associative memory classifier is used.
OPTIMAL MATRIX TRANSFORM FOR THE EXTRACTION OF ALGEBRAIC FEATURES FROM IMAGES
Authors: LIU KE , HUANG YEA S. , SUEN CHING Y.
Published: September 2004

A new algebraic feature extraction method for image recognition is presented. The optimal transform of image matrices is proposed to extract the features from images. The Frobenius norm of matrices is first introduced as a measure of the distance between two matrices. Based on this, the within-class and between-class distances of image samples are defined. The ratio of the between-class and within-class distances of the transformed image sample set is taken as the criterion function J(T). The optimal transform matrix T is calculated by maximizing J(T) under some constraints. Experiments have been conducted to recognize both human face and handwritten character images. These results indicate that the algebraic features extracted by the present method possess a very strong discriminant power. An important conclusion about the present method is that the traditional linear discriminant method can be considered as a special case of the present feature method when image samples have only one column of vectors.
REPRESENTATION OF 2D AND 3D BINARY IMAGES USING MEDIAL CIRCLES AND SPHERES
Authors: ASWATHA KUMAR M. , CHATTERJI B. N. , MUKHERJEE JAYANTA , DAS P. P.
Published: September 2004

Representation schemes play an important role in the fields of Computer Vision, Graphics, Image Processing, CAD/CAM etc. Various representation schemes have been discussed in the literature for both 2D and 3D. In this paper, we are presenting a scheme of representation using the concept of octagonal distances. They are called Medial Circle Representation (MCR) and Medial Sphere Representation (MSR) in 2D and 3D, respectively. Storage requirement, computational complexity, merits and demerits of the representation schemes are discussed.
PATTERN CATEGORY ASSIGNMENT BY NEURAL NETWORKS AND NEAREST NEIGHBORS RULE: A SYNOPSIS AND A CHARACTERIZATION
Authors: MITICHE A. , AGGARWAL J. K.
Published: September 2004

The purpose of this paper is two-fold: to give a synoptic description of favored neural networks and to characterize the potency of these neural networks as pattern classifiers, against the background of the familiar nearest neighbors classification. We limit the study to those neural network structures most commonly used for pattern classification: the multilayer perceptron, the Kohonen associative memory, and the Carpenter–Grossberg clustering network, for which we give a tutorial description with the aim of making the driving concepts apparent. The nearest neighbors rule is presented with improved nearest neighbor search and reference data sample pruning. To gain some familiarity with the classifiers, we expound the sequence of computations implicated in pattern category assignment by each classifier. A characterization of the classifiers is drawn from observed and expected properties and from experiments in automatic target recognition and optical character recognition as summarized in comparative tables of performance. This characterization supports the suggestion that nearest neighbors classification always be considered before endorsing alternative pattern classifiers such as neural networks.
LEARNING IN NAVIGATION: GOAL FINDING IN GRAPHS
Authors: CUCKA PETER , NETANYAHU NATHAN S. , ROSENFELD AZRIEL
Published: September 2004

A robotic agent operating in an unknown and complex environment may employ a search strategy of some kind to perform a navigational task such as reaching a given goal. In the process of performing the task, the agent can attempt to discover characteristics of its environment that enable it to choose a more efficient search strategy for that environment. If the agent is able to do this, we can say that it has "learned to navigate" — i.e., to improve its navigational performance. This paper describes how an agent can learn to improve its goal-finding performance in a class of discrete spaces, represented by graphs embedded in the plane. We compare several basic search strategies on two different classes of "random" graphs and show how information collected during the traversal of a graph can be used to classify the graph, thus allowing the agent to choose the search strategy best suited for that graph.
SUBSET LEAST SQUARES METHOD FOR ROBUST SPEECH AND IMAGE PROCESSING
Authors: KASHYAP R. L. , LIAW JIN-NAN
Published: September 2004

In speech and image processing, many good algorithms do not perform well in noisy conditions. Recently, robust algorithms have been proposed to improve the performance and robustness of those algorithms. However, many of them are either computationally very expensive or break down quickly. A generalized maximum-likelihood-based robust algorithm called Subset Least Squares (SLS) has been proposed by Kashyap to deal with contaminated data with less complexity and higher breakdown. In this paper, we first illustrate the basic framework of SLS and the use of SLS for robust regression. We then present the use of SLS method in several image and speech applications. Finally, we discuss the possible use of SLS in future applications.
SHAPE RECOGNITION BY HUMAN-LIKE TRIAL AND ERROR RANDOM PROCESSES
Authors: NAGAO MAKOTO
Published: September 2004

Pattern recognition and object detection systems so far developed required the algorithmic description of every detail of the objects to be recognized by bottom-up process from pixel-to-pixel relation to line, corner, and structural description. Because this low-level process does not see global information, feature detection is highly sensitive to noise. To overcome this problem and to give human-like flexibility to machine recognition process, we developed a new system which had non-algorithmic feature detection functions by seeing a comparatively large area at once. It uses a variable size window which is applied to the most plausible parts in an image by a top-down command from an object model, and obtains characteristic features of object parts. This window application is realized mostly in hardware, and has some autonomic ability to detect the best features by a sort of random trial and error search. The system has some other hardware functions such as mutual correlation of one- and two-dimensional patterns, which are also flexible according to the variable size window. The system interprets user's declarative description of objects, and activates the window application functions to obtain characteristic features of the description. This new flexible approach of object detection can be used as a robot eye to recognize many simple two-dimensional shapes.
3-D FACE MODELING AND ITS APPLICATIONS
Authors: HUANG THOMAS S. , TANG LI-AN
Published: September 2004

This paper describes some issues in building a 3-D human face modeling system which mainly consists of three parts:

• Modeling human faces;

• Analyzing facial motions;

• Synthesizing facial expressions.

A variety of techniques developed for this system are described in detail in this paper. Some preliminary results of applying this system to computer animation, video sequence compression and human face recognition are also shown.
DIMENSION REDUCTION, FEATURE EXTRACTION AND INTERPRETATION OF DATA WITH NETWORK COMPUTING
Authors: PAO YOH-HAN
Published: September 2004

The subject matter of this paper is one of long-standing interest to the Pattern Recognition and Artificial Intelligence research communities, namely that of "feature extraction" for facilitating the task of classification or various other tasks. We show that internal representations of neural networks do not yield unique feature values but can provide the basis for facilitating a number of useful information management tasks, such as memorization, categorization, discovery, associative recall and others. These matters are illustrated with three sets of data, one of a benchmark nature, another of the nature of real-world sensor data, and a third set consisting of semiconductor crystal structure parameters.
CHARACTERISTIC-VIEW MODELING OF CURVED-SURFACE SOLIDS
Authors: CHEN SHUANG , FREEMAN HERBERT
Published: September 2004

Given a 3-D object (solid), it is possible to divide the exterior viewing space of the solid into maximally connected subspaces of vantage points such that the perspective views of the solid from all vantage points within a subspace have identical labeled line-junction graphs. We refer to such subspaces as the characteristic-view domains (CVDs) of the solid, and to a representative perspective view from within a domain, as a characteristic view (CV). The concept suggests a scheme for modeling 3-D objects in which the infinite number of possible views of a given solid are represented by a finite set of characteristic views (together with their respective projection-defining parameters).

The concept was introduced more than 15 years ago as a modeling scheme to facilitate real-time recognition of 3-D objects. However, the difficulty of determining the CVs hindered its application. This article describes a technique for computing the characteristic views of general solids bounded by quadric surfaces (including polyhedra as a special case), and suggests how the technique can be used for 3-D object recognition.
PROPAGATING COVARIANCE IN COMPUTER VISION
Authors: HARALICK ROBERT M.
Published: September 2004

This paper describes how to propagate approximately additive random perturbations through any kind of vision algorithm step in which the appropriate random perturbation model for the estimated quantity produced by the vision step is also an additive random perturbation. We assume that the vision algorithm step can be modeled as a calculation (linear or non-linear) that produces an estimate that minimizes an implicit scaler function of the input quantity and the calculated estimate. The only assumption is that the scaler function has finite second partial derivatives and that the random perturbations are small enough so that the relationship between the scaler function evaluated at the ideal but unknown input and output quantities and the observed input quantity and perturbed output quantity can be approximated sufficiently well by a first order Taylor series expansion.

The paper finally discusses the issues of verifying that the derived statistical behavior agrees with the experimentally observed statistical behavior.
SHAPE DESCRIPTION BY A SYNTACTIC PYRAMIDAL APPROACH
Authors: LEVIALDI STEFANO , CINQUE LUIGI
Published: September 2004

In the syntactic approach to pattern recognition, patterns are represented as strings, where each pattern is expressed as a composition of its component patterns, called subpatterns and pattern primitives. This approach draws an analogy between the structure of patterns and the syntax of a language. The patterns are considered at a single resolution level, and the recognition of each pattern is usually made by parsing the pattern structure according to a given set of syntax rules, obtained in the first stage of the analysis. This paper describes a pyramidal approach to 2-D object representation which relies on a linguistic description of the object contour at different resolution levels. Our approach, which is parallel and context-sensitive, implies the use of higher dimensional grammars for defining production rules which describe the evolution of the contour between levels. This method is computationally efficient (due to the fast coarse-fine search), rotationally invariant, as well as rather insensitive to noise.
GENETIC SELECTION AND NEURAL MODELING OF PIECEWISE-LINEAR CLASSIFIERS
Authors: SKLANSKY JACK , VRIESENGA MARK
Published: September 2004

Piecewise-linear mathematical structures form a convenient and important framework for implementing trainable and adaptive pattern classifiers. Neural networks and genetic algorithms offer additional approaches with important benefits for the design of such classifiers. In this paper we show how neural modeling and genetic selection can be applied to piecewise-linear structures to optimize both the topology and the parameter values of the network forming the classifier. Such a classifier will tend to have a low error rate and high robustness. We describe applications of these techniques to an adaptive detector of abnormal tissue in mammograms and a detector of straight lines and edges in noisy aerial images.
AN ABDUCTIVE REASONING BASED IMAGE INTERPRETATION SYSTEM
Authors: CHAUDHURY SANTANU , GUPTA ARBIND , PARTHASARATHY GUTURU , SUBRAMANIAN S.
Published: September 2004

This paper describes an abductive reasoning based inferencing engine for image interpretation. The inferencing strategy finds an acceptable and consistent explanation of the features detected in the image in terms of the objects known a priori. The inferencing scheme assumes representation of the domain knowledge about the objects in terms of local and/or relational features. The inferencing system can be applied for different types of image interpretation problems like 2-D and 3-D object recognition, aerial image interpretation, etc. In this paper, we illustrate functioning of the system with the help of a 2-D object recognition problem.
FINDING THE SHORTEST PATHS ON SURFACES BY FAST GLOBAL APPROXIMATION AND PRECISE LOCAL REFINEMENT
Authors: KIMMEL RON , KIRYATI NAHUM
Published: September 2004

Finding the shortest path between points on a surface is a challenging global optimization problem. It is difficult to devise an algorithm that is computationally efficient, locally accurate and guarantees to converge to the globally shortest path. In this paper a two stage coarse-to-fine approach for finding the shortest paths is suggested. In the first stage the algorithm of Ref. 10 that combines a 3D length estimator with graph search is used to rapidly obtain an approximation to the globally shortest path. In the second stage the approximation is refined to become a shorter geodesic curve, i.e., a locally optimal path. This is achieved by using an algorithm that deforms an arbitrary initial curve ending at two given surface points via geodesic curvature shortening flow. The 3D curve shortening flow is transformed into an equivalent 2D one that is implemented using an efficient numerical algorithm for curve evolution with fixed end points, introduced in Ref. 9.
RECOGNITION OF RADICALS IN HANDWRITTEN CHINESE CHARACTERS BY MEANS OF PROBLEM REDUCTION AND KNOWLEDGE GUIDANCE
Authors: CHENG REI-HENG , LEE CHI-WEI , CHEN ZEN
Published: September 2004

Recognizing handwritten Chinese characters is a complex problem. We break the problem down into a series of sub-problems and concentrate on the radical recognition problem. The sub-problems are linked in a hierarchy of three layers: radical, radical sub-unit, and salient stroke. This problem reduction technique enables us to solve complex recognition problems effectively. We describe how to analyze and choose radical sub-units and salient strokes in order to recognize a set of radicals. We also construct two knowledge bases in the form of decision trees to guide the hypothesis and test procedures used to solve the recognition sub-problems. The problem reduction is done in a top-down fashion, while the problem solving process proceeds in a bottom-up fashion. The advantages of our method are described. The representation and organization of the two knowledge bases are explicitly described. The method is tested on two sets of handprinted characters using an IBM PC (486-33). The recognition rate is over 95.7% and the computer time needed to recognize a radical averages about 0.07 s. The experimental results indicate that our method effectively copes with a wide range of ordinary variations encountered in handwritten Chinese characters.
PARACONSISTENT CIRCUMSCRIPTION: PRELIMINARY REPORT
Authors: LIN ZUOQUAN
Published: September 2004

In this paper we describe the paraconsistent circumscription by the application of predicate circumscription in a paraconsistent logic, the logic of paradox LP. In addition to circumscribing the predicates, we also circumscribe the inconsistency. The paraconsistent circumscription can be well characterized by the minimal semantics which is both nonmonotonic and paraconsistent. It brings us advantages in two respects: nonmonotonic logic would be nontrivial while there was a contradiction, and paraconsistent logic would be equivalent to classical logic while there was no effect of a contradiction.
A HOUGH-BASED MATCHING OF 2D LINE SEGMENTS IN A MONOCULAR IMAGE SEQUENCE
Authors: FORESTI G. L. , REGAZZONI C. S.
Published: September 2004

The paper describes a method for detecting 2D straight segments and their correspondences in successive frames of an image sequence by means of a Hough-based matching approach. The main advantage of this method is the possibility of extracting and matching 2D straight segments directly in the feature space, without the need for complex matching operations and time-consuming inverse transformations. An additional advantage is that only four attributes of 2D straight segments are required to perform an efficient matching process: position, orientation, length, and midpoint. Tests were performed on both synthetic and real images containing complex man-made objects moving in a scene. A comparison with a well-known 2D line matching algorithm is also made.
A SPATIAL FILTER FOR SIMILARITY RETRIEVAL
Authors: CHANG CHIN-CHEN , JIANG JI-HAN
Published: September 2004

Recently, Yang and Lee13 proposed a powerful data structure, the 2D B-string, to express the spatial knowledge embedded in iconic images. The data structure can be used to describe images with partly- or completely- overlapping objects without the need for partitioning the objects. We propose a signature file method for similarity retrieval of symbolic images described in 2D B-strings. The signatures of images are extracted by the disjoint coding and the superimposed coding techniques. A linear hashing function is adopted to organize the signature file. The experimental results show that the method has better performance than the one proposed by Lee, Yang and Chen.11
GENETIC ALGORITHM WITH ELITIST MODEL AND ITS CONVERGENCE
Authors: BHANDARI DINABANDHU , MURTHY C. A. , PAL SANKAR K.
Published: September 2004

In this article, the genetic algorithm with elitist model (EGA) is modeled as a finite state Markov chain. A state in the Markov chain denotes a population together with a potential string. Proof for the convergence of an EGA to the best chromosome (string), among all possible chromosomes, is provided here. Mutation operation has been found to be essential for convergence. It has been shown that an EGA converges to the global optimal solution with any choice of initial population.
DATA PARALLEL SUPERVISED CLASSIFICATION ALGORITHMS ON MULTISPECTRAL IMAGES
Authors: JOHANSSON THOMAS , BENGTSSON EWERT
Published: September 2004

In remote sensing the intensities from a multispectral image are used in a classification scheme to distinguish different ground cover from each other. An example is given where different soil types are classified. A digitized complete scene from a satellite sensor consists of a large amount of data and in future image sensors the resolution and the number of spectral bands will increase even further. Data parallel computers are therefore well-suited for these types of classification algorithms. This article will focus on three supervised classified algorithms: the Maximum Likelihood, the K-Nearest Neighbor and the Backpropagation algorithm, together with their parallel implementations. They are implemented on the Connection Machine/200 in the high-level language C*. The algorithms are finally tested and compared on an image registered over western Estonia.
AN EVALUATION OF THE NOVEL "NORMALIZED-REDUNDANCY" REPRESENTATION FOR PLANAR CURVES
Authors: FDEZ-VALDIVIA J. , GARCIA J. A. , GARCIA-SILVENTE M.
Published: September 2004

In this paper, a novel approach for improving model-based recognition is proposed. Our approach provides a suitable shape representation by extracting only the most significant scales that best describe a planar noisy curve. The proposed representation satisfies several necessary criteria for general-purpose shape representation methods. The representation is capable of dealing with different levels of noise, it does not require user-set parameters or prior knowledge about the curve's nature, it also has a very low-order polynomial computational complexity in time and space. Hence such a shape representation is very useful for shape recognition. The method depends on the connection between the redundancy of two signals' smoothed versions and the essential structure being simultaneously isolated in both versions. Two different ways of formulating this approach are described in this paper: the global "normalized-redundancy" representation and the local "normalized-redundancy" representation. Results of applying the proposed formulation to synthetic and real 2-D shapes are presented.
ON-LINE RECOGNITION OF CURSIVE KOREAN CHARACTERS USING ART-BASED STROKE CLASSIFICATION (RECOGNITION OF CURSIVE KOREAN CHARACTERS)
Authors: KIM HANG-JOON , KIM SANG-KYOON
Published: September 2004

This paper proposes an efficient method for on-line recognition of cursive Korean characters. Strokes, primitive components of Korean characters, are usually warped into a cursive form and classifying them is very difficult. To deal with such cursive strokes, we consider them as a recognition unit and automatically classify them by using an ART-2 neural network. The neural network has the advantage of assembling similar patterns together to form classes in a self-organized manner. This ART-2 stroke classifier contributes to high stroke recognition rate and less recognition time. A database for character recognition is also dynamically constructed with a tree structure, and a new character can be included simply by adding a new sequence to it. Character recognition is achieved by traversing the database with a sequence of recognized strokes and positional relations between the strokes. To verify the performance of the system, we tested it on 17,500 handwritten characters, and obtained a good recognition rate of 96.8% and a speed of 0.52 second per character. This results suggest that the proposed method is pertinent to be put into practical use.
ON THE DETECTION OF PARALLEL CURVES: MODELS AND REPRESENTATIONS
Authors: WONG W. H. , IP HORACE H. S.
Published: September 2004

In this paper, we study three parallel models for curves. Based on their common properties we develop an algorithm for the detection of parallelism among curves. Furthermore, to speed up processing, we developed theorems for fast verification at critical points. False alarm is prevented by an enhancement in tangent representation, thus no postprocessing is required. The new tangent representation is called direction-dependent tangent (DDT). It incorporates concavity information into the tangent representation and prohibits false matching. Based on the theorems and the tangent representation, we show that parallelism detection can be formulated as a correspondence problem. The algorithm handles also partial parallelism, i.e., parallelism in certain ranges along the curves but not lasting throughout the whole curves spans. By "curve" we mean any planar curve with C3 continuity. The technique treats both curves and lines alike and is valid for both open and closed curves.
LEARNING TO RECOGNIZE HAND-PRINTED CHINESE CHARACTERS USING INDUCTIVE LOGIC PROGRAMMING
Authors: AMIN ADNAN , SAMMUT CLAUDE , SUM K. C.
Published: September 2004

Recognition of Chinese characters has been a major interest of researchers for many years, and a large number of research papers and reports have already been published in this area. There are several major problems: Chinese characters are distinct and ideographic, the character size is very large and a lot of structurally similar characters exist in the character set. Thus, classification criteria are difficult to find.

This paper presents a new technique for the recognition of hand-printed Chinese characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variations in writing styles. The advantages of machine learning are twofold: it can generalize over the large degree of variations between writing styles and recognition rules can be constructed by example.

The paper also describes three methods of feature extraction for Chinese character recognition: regular expression, dominant point and modified Hough transform. These methods are then compared in terms of accuracy and efficiency.
GENETIC TIME WARPING FOR ISOLATED WORD RECOGNITION
Authors: KWONG S. , HE Q. H. , MAN K. F.
Published: September 2004

In this paper, a Genetic Time Warping (GTW) algorithm for isolated word recognition was proposed. Relative representation techniques, fitness techniques and reproduction techniques were described and genetic operators were also discussed in detail. Different from the conventional genetic algorithms with fixed genes, every chromosome has its own number of genes. A modified order-based crossover operator was introduced in order to deal with the chromosomes with a different number of genes. Besides the mutation and crossover operators, a new heuristic local optimum operator was also built and it could alter part of a chromosome based on a function of local distance and average distortion of the paths. Finally, experimental investigations were carried out to test the performance of GTW. Based on Rabiner's normal assumptions23 on the distributions of the distances, the overall probability of making a word error could be calculated experimentally. Results demonstrated that GTW performed better or much better than the DTW method for most of the tested words.
AN ITERATIVE CLUSTERING PROCEDURE FOR INTERPRETING AN IMPERFECT LINE DRAWING
Authors: CHUNG RONALD , LEUNG KIN-LAP
Published: September 2004

Recovering three-dimensional shape of an object from a single line drawing is a classical problem in computer vision. Methods proposed range from Huffman–Clowes junction labeling, to Kanade's gradient space and skew symmetry analysis, to Sugihara's necessary and sufficient condition for a realizable polyhedral object, to Marill's MSDA shape recovery procedure, to Leclerc–Fischler's shape recovery procedure which assures planar faces, and to the recent Baird–Wang's gradient-descent algorithm which has a favorable time complexity. Yet all these assume perfect line drawings as the input. We propose a method that through the use of iterative clustering interprets an imperfect line drawing of a polyhedral scene. It distinguishes the true surface boundaries from the extraneous ones like the surface markings, fill-in the missing surface boundaries, and recovers 3-D shapes satisfying constraints of planarity of faces and parallel symmetry of lines, all at the same time. Experiments also show that the 3-D interpretation agrees with human perception.
MULTISENSOR IMAGE RECOGNITION BY NEURAL NETWORKS WITH UNDERSTANDABLE BEHAVIOR
Authors: ROLI F.
Published: September 2004

Recently, a kind of structured neural networks (SNNs) explicitly devoted to multisensor image recognition and aimed at allowing the interpretation of the "network behavior" was presented in Ref. 1. Experiments reported in Ref. 1 pointed out that SNNs provide a trade-off between recognition accuracy and interpretation of the network behavior. In this paper, the combination of multiple SNNs, each of which has been trained on the same data set, is proposed as a means to improve recognition results, while keeping the possibility of interpreting the network behavior. A simple method for interpreting the "collective behaviors" of such SNN ensembles is described. Such an interpretation method can be used to understand the different kinds of "solutions" learned by the SNNs belonging to an ensemble. In addition, as compared with the interpretation method presented in Ref. 1, it is shown that the knowledge embodied in an SNN can be translated into a set of understandable "recognition rules". Experimental results on the recognition of multisensor remote-sensing images (optical and radar images) are reported in terms of both recognition accuracy and network-behavior interpretation. An additional experiment on a multisource remote-sensing data set is described to show that SNNs can also be effectively used for multisource recognition tasks.
HIGH CAPACITY ASSOCIATIVE MEMORY BASED ON THE RANDOM NEURAL NETWORK MODEL
Authors: LIKAS ARISTIDIS , STAFYLOPATIS ANDREAS
Published: September 2004

In this paper the Bipolar Random Network is described, which constitutes an extension of the Random Neural Network model and exhibits autoassociative memory capabilities. This model is characterized by the existence of positive and negative nodes and symmetrical behavior of positive and negative signals circulating in the network. The network's ability of acting as autoassociative memory is examined and several techniques are developed concerning storage and reconstruction of patterns. These approaches are either based on properties of the network or constitute adaptations of existing neural network techniques. The performance of the network under the proposed schemes has been investigated through experiments showing very good storage and reconstruction capabilities. Moreover, the scheme exhibiting the best behavior seems to outperform other well-known associative neural network models, achieving capacities that exceed 0.5n where n is the size of the network.
COMPARISON OF VARIOUS ROUTINES FOR UNKNOWN ATTRIBUTE VALUE PROCESSING: THE COVERING PARADIGM
Authors: BRUHA IVAN , FRANEK FRANTISEK
Published: September 2004

Simple inductive learning algorithms assume that all attribute values are available. The well-known Quinlan's paper1 discusses quite a few routines for the processing of unknown attribute values in the TDIDT family and analyzes seven of them.

This paper introduces five routines for the processing of unknown attribute values that have been designed for the CN4 learning algorithm, a large extension of the well-known CN2. Both algorithms CN2 and CN4 induce lists of decision rules from examples applying the covering paradigm.

CN2 offers two ways for the processing of unknown attribute values. The CN4's five routines differ in style of matching complexes with examples (objects) that involve unknown attribute values. The definition of matching is discussed in detail in the paper. The strategy of unknown value processing is described both for learning and classification phases in individual routines.

The results of experiments with various percentages of unknown attribute values on real-world (mostly medical) data are presented and performances of all five routines are compared.
HIERARCHICAL DECOMPOSITION OF DISTANCE LABELED SKELETONS
Authors: ABLAMEYKO SERGEY V. , ARCELLI CARLO , SANNITI DI BAJA GABRIELLA
Published: September 2004

The distance labeled skeleton of a two-dimensional digital object is hierarchically decomposed into loops, branches and concatenations of branches. Decomposition is obtained by using an iterated tracing-and-deleting process alternately applied to more and more internal skeleton subsets. Every skeleton subset is associated a feature, called the relevance, which is related to the spatial extension of the object region represented by that subset. The relevance is used to decide on how to perform concatenations, as well as to rank skeleton decomposition components ascribed to the same hierarchy level. The decomposition is stable under object rotation and can be used in a recognition process accomplished via graph matching.
RANK-ORDER FUNCTIONS FOR THE FAST DETECTION OF TEXTURE FAULTS
Authors: DE NATALE FRANCESCO G. B.
Published: September 2004

Simple approaches to texture discrimination based on histogram analysis are useful in real-time applications but often yield inadequate results. On the other hand, methods based on higher-order statistics (e.g., co-occurrence matrices) provide a more complete statistical characterisation but are extremely time-consuming. In this paper, methods based on first order statistical analysis are reviewed and the significance of the relevant representative features analyzed. Then, rank functions are considered and appropriate distance functions are introduced that prove to have substantial advantages over classical histogram-based approaches.
ANIMATING LOTOS SPECIFICATIONS USING AMLOG
Authors: TOGASHI ATSUSHI , MANSFIELD GLENN , SHIRATORI NORIO
Published: September 2004

In order to achieve a more convenient and intelligent handling of LOTOS specifications, an approach is proposed wherein specifications are translated into an appropriate equational logic language and interpreted. LOTOS has an excellent formal model for describing communication processes and equational logic languages have the capability of inferencing and data abstraction. Naturally, the marriage of LOTOS and equational logic provides a powerful framework with features that could not be realized in existing technologies. To demonstrate this potential, AMLOG is chosen as the appropriate equational logic language. Apart from rapid prototyping of specifications, some of the exciting possibilities of the new framework that are demonstrated include stepwise refinement and testing for correctness of specifications, facilities for property checking and verification of bisimulation equivalences. Also, the derivation of behavior expressions from trace histories is shown. The approach, extensible to other FDTs as well, is a step towards realizing an intelligent communication protocol specification environment.
SPECIFYING INDUSTRIAL REAL-TIME SYSTEMS WITH A TEMPORAL LOGIC FRAMEWORK
Authors: CIAPESSONI EMANUELE , CORSETTI EDOARDO , MIGLIORATI MANLIO , RATTO ELENA , CRIVELLI ERNANI
Published: September 2004

This paper presents the TRIO logical framework to specify industrial real-time systems. Typical specification requirements for this kind of system are discussed and related to corresponding features of the framework. In particular, the methodological expressiveness, the validation and the temporal constraint proof capabilities are highlighted. The former is mainly dealt with by the Object Oriented paradigm, while the latter is dealt with by a formal method specification derived from Metric Temporal Logic languages. Further, this Metric Temporal Logic is extended with the ability to treat different granularities. In order to show the capabilities of the TRIO logical framework, the specification of a complex real-time system is proposed.
ON THE ANIMATION OF “NOT EXECUTABLE” SPECIFICATIONS BY PROLOG
Authors: STERLING LEON , CIANCARINI PAOLO , TURNIDGE TODD
Published: September 2004

An impediment to the widespread use of formal methods for software development is the difficulty in dealing with specifications, namely using them consistently in the software process. One approach to easing the management of specifications and improving their impact in the software process is animation, allowing developers to “execute” formal specifications as prototypes. This paper illustrates how Prolog can serve a multifaceted role for animating and prototyping specifications—as a target language, as the compilation/translation language, and to facilitate the advantages of formal methods through help in building formal proofs of properties such as correctness. Further, there is an implicit claim that, because of the correctness and directness of the translation, a subset of Z, not definitively established here, can be viewed as equivalent to a subset of Prolog.
TOWARDS A THEORY OF ELABORATION TOLERANCE: LOGIC PROGRAMMING APPROACH
Authors: GELFOND MICHAEL , PRZYMUSINSKA HALINA
Published: September 2004

This paper is an attempt at mathematical investigation of software development process in the context of declarative logic programming. We introduce notions of specification and specification constructor which are developed from natural language description of a problem. Generalizations of logic programs, called lp-functions are introduced to represent these specifications. We argue that the process of constructing lp-function representing a specification S should be supported by certain types of mathematical results which we call representation theorems. We present two such theorems to illustrate the idea.
REPRESENTING COBOL IN PROLOG—TOWARDS PROGRAM COMPREHENSION AND REENGINEERING
Authors: GESKE ULRICH , NITSCHE MICHAEL
Published: September 2004

This paper sets out to investigate how COBOL programs can be interpreted using Prolog. For this purpose, we examine how an executable logic specification can be obtained, which is semantically equivalent to the original source. We define a specification language COPRA as a restricted subset of Prolog and show how various imperative constructs, such as GOTOs and subroutine calls, can be handled in this language. The specification is generated automatically and may serve as a starting point for reengineering in form of a higher-level modification of the source program.