bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Synthesizing Method for Handwriting Korean Scripts
Authors: Lee Do-Hoon , Cho Hwan-Gue
Published: July 2003

So far there have been models for generating handwriting scripts of western languages. Since the basic structure of oriental languages such as Chinese, Japanese and Korean, is quite different from that of alphabet-based western languages, western handwriting models cannot be applied directly to oriental languages except for a few models. In this paper, we propose a new generation model for generating Korean handwriting scripts. For this we choose 11 characteristic parameters which determine the shape of handwriting scripts. These parameters were selected experimentally by investigating ample Korean scripts. Furthermore we propose the Beta-velocity model to simulate the cursive stroke movement by manipulating curvilinear and cursive connection for consecutive strokes within a letter or a word. In order to generate these oriental cursive strokes, we adopted Beta distribution curve basis which enables one to make easy asymmetric velocity profile. By combining all these features, we synthesize various handwriting Korean scripts by controlling 11 characteristic parameters. In the final section of this paper, we give experimental results. These easy-to-synthesize Korean scripts can be used in evaluating off-line character recognition system and a word processor by supporting handwriting-style fonts.
Differentiating Between Oriental and European Scripts by Statistical Features
Authors: Lam Louisa , Ding Jie , Suen Ching Y.
Published: July 2003

Two types of techniques are usually adopted in language differentiation: token matching and statistical analysis. In this paper we present a method which uses a combined analysis of several discriminating statistical features, for the differentiation between European and oriental language scripts. When applied to more than 23 languages, it has proved to be effective in differentiating between documents printed in these different scripts.
Gray-Scale Nonlinear Shape Normalization Method for Handwritten Oriental Character Recognition
Authors: Kim Sang-Yup , Lee Seong-Whan
Published: July 2003

In general, nonlinear shape normalization methods for binary images have been used in order to compensate for the shape distortions of handwritten characters. However, in most document image analysis and recognition systems, a gray-scale image is first captured and digitized using a scanner or a video camera, then a binary image is extracted from the original gray-scale image using a certain extraction technique. This binarization process may remove some useful information of character images such as topological features, and introduce noises to character background. These errors are accumulated in nonlinear shape normalization step and transferred to the following feature extraction or recognition step. They may eventually cause incorrect recognition results.

In this paper, we propose nonlinear shape normalization methods for gray-scale handwritten Oriental characters in order to minimize the loss of information caused by binarization and compensate for the shape distortions of characters. Two-dimensional linear interpolation technique has been extended to nonlinear space and the extended interpolation technique has been adopted in the proposed methods to enhance the quality of normalized images.

In order to verify the efficiency of the proposed methods, the recognition rate, the processing time and the computational complexity of the proposed algorithms have been considered. The experimental results demonstrate that the proposed methods are efficient not only to compensate for the shape distortions of handwritten Oriental characters but also to maintain the information in gray-scale Oriental characters.
Distributed Autonomous Agents for Chinese Document Image Segmentation
Authors: Liu Jiming , Tang Y. Y.
Published: July 2003

In Chinese document image processing, text and/or graphical block detection serves as an essential step in document layout analysis that in turn permits the effective reasoning about the logical relationships among various text paragraphs and graphical entities for the purpose of document understanding. This paper presents a novel computational paradigm for extracting text/graphic blocks from Chinese document images, which is based on a notion of distributed autonomous agents. The primary features of the agents lie in that they are (1) adaptive to the locality of given images and hence efficient in locating the homogeneous image blocks, (2) reliable in performing image processing as the computation proceeds simultaneously from different image locations, (3) less sensitive to the noise in the given images as the computation disperses gracefully when it is moving away from the homogeneous blocks, and (4) easy to represent in their behaviors and evolvable in their performance. The paper, first of all, describes the formalisms as well as the behavioral characteristics of the agents, which is followed by a demonstration of the agents in detecting document blocks from some real-life images.
On-Line Handwritten Chinese Character Recognition Directed by Components with Dynamic Templates
Authors: Xiao Xuhong , Dai Ruwei
Published: July 2003

Structural matching has been recognized as a promising approach for on-line Chinese character recognition. In order to reduce its great computational complexity and improve its performance, people have been seeking for ways to direct the matching of a whole character by the result of partial matching. In this paper, the authors proposed 45 basic components for 3,755 categories of daily-used Chinese characters to direct the stroke segment matching of whole characters. Since they are always located at either the beginning or the end of the stroke segment string of characters, these components are easy to be extracted and separated from other parts of a character. Besides, in our approach, the reference templates of these components are extracted dynamically from the corresponding segment string of characters when a specific matching is carried out. This strategy avoids building multiple templates for the components of the same kind but at different places of characters. The experiments show that the segment matching computation has been reduced greatly without reducing the correctness of matching.
A Reliability Design Methodology for Chinese Character Recognition
Authors: Huang Y. S. , Liu K. , Suen C. Y. , Tang Y. Y.
Published: July 2003

This paper proposes a novel method which enables a Chinese character recognition system to obtain reliable recognition. In this method, two thresholds, i.e. class region thresholdRk and disambiguity thresholdAk, are used by each Chinese character k when the classifier is designed based on the nearest neighbor rule, where Rk defines the pattern distribution region of character k, and Ak prevents the samples not belonging to character k from being ambiguously recognized as character k. A novel algorithm to derive the appropriate thresholds Ak and Rk is developed so that a better recognition reliability can be obtained through iterative learning. Experiments performed on the ITRI printed Chinese character database have achieved highly reliable recognition performance (such as 0.999 reliability with a 95.14% recognition rate), which shows the feasibility and effectiveness of the proposed method.
Typeface Identification for Printed Chinese Characters
Authors: Tseng Yi-Hong , Kuo Chi-Chang , Lee Hsi-Jian
Published: July 2003

In this paper, we propose a methodology for identifying typefaces of printed Chinese characters in documents. Three kinds of features, stroke width means, stroke width variations, and aspect ratio, are first used to classify character typefaces as: Black, Li, Kai-Round, or Ming-Song. Each of the last two groups contains two typefaces. Vertical/horizontal stroke width ratios are used to distinguish between the Ming and Song typefaces and accumulative pixel ratio to distinguish between the Kai and Round typefaces. Six different typeface feature distributions measured from 5401 printed Chinese characters are considered, and a trapezoid-shaped membership function is constructed for each distribution. Based on these membership functions, we determine what typeface each input character belongs to using a two-level decision tree. To increase the identification rate, the typeface of a certain character is adjusted according to the typeface identification results of the front and the next characters. In the character recognition system, we use two statistical features: crossing counts and contour directional counts. We achieved an 89.87% typeface identification rate in our experiments, and a 95.60% character recognition rate.
Printed Chinese Character Similarity Measurement Using Ring Projection and Distance Transform
Authors: Yuen P. C. , Feng G. C. , Tang Y. Y.
Published: July 2003

This paper presents a new Chinese character similarity measurement method based on the ring projection algorithm and distance transform. The ring projection algorithm is used to transform a character image with two independent variables into a function of one independent variable in the ring projection space. This representation of character in the ring projection space has been proved to be in orientation and scale invariant. However, this representation will be distorted nonlinearly in the presence of noise. Therefore, common linear metrics such as Euclidean distance, cannot be applied to measure distance. To solve the nonlinear distortion problem, distance transform is proposed as a nonlinear metric. The similarity measurement is performed using the distance transformed image in the ring projection space. A number of Chinese characters are selected to evaluate the capability of the proposed measurement scheme and the results are encouraging.
Segmentation and Recognition of Continuous Handwriting Chinese Text
Authors: Hong Chen , Loudon Gareth , Wu Yimin , Zitserman Ruslana
Published: July 2003

This article introduces the basic segmentation problems in Chinese handwriting and also several prior work to solve these problems. A new segmentation method is proposed, which is applicable to both on-line and off-line systems for free-format handwritten Chinese character sentences. This method performs basic segmentation and fine segmentation based on the varying spacing thresholds and the minimum variance criteria. The five most probable ways of segmentation are derived from this stage and all the possible segments are extracted and recognized. A lattice is created from all the segments and searched using a viterbi based algorithm to find the most likely character sequence. The algorithm presented in this paper provides large flexibility and robustness to handle free-format continuous Chinese handwriting and is a promising solution for a natural and fast Chinese pen input system. The character accuracy is 85.0% for on-line and 77.4% for the off-line test data.
Network-Based Approach to Korean Handwriting Analysis
Authors: Sin Bong-Kee , Kim Jin H.
Published: July 2003

It is well known that the stochastic approach using the HMM and dynamic programming-based search is particularly suited to the analysis of time series signals including on-line handwriting. The starting point of this research is a network of HMMs which models the whole set of characters. Then it is followed by the assertion that the HMM for the on-line script can be applied to not only on-line character recognition but also to the handwriting synthesis and even pen-trajectory recovery in off-line character images. The solutions to these problems are based on the single network of HMMs and the single principle of DP-based state-observation alignment. Given an observation sequence, the search for the best path in the network corresponds to the recognition. Given a character model, the search for the best observation sequence corresponds to the handwriting generation. The proposed framework has been shown to work nicely through a set of tests on Korean characters.
Hermes Autonomous Vision System
Authors: Bourbakis N. G. , Barlos F. , Mertoguno J. S.
Published: July 2003

This paper deals with the modeling of an extended quartet multiprocessor kernel used for the evaluation of the Hermes system. Hermes is a multiprocessor hybrid system architecture used as a machine vision system. The functionality of Hermes requires an asynchronous information flow upwards and downwards, where "orders"(in a form of code and data) go down and "abstracted" or processed picture information goes up along the system's hierarchy. Moreover, the overall functional behavior of the Hermes system can be considered as an extended quartet kernel in an abstracted manner. The extended quartet kernel studied here presents either a memory-to-memory (M-M) or a bus-to-bus (B-B) connectivity. The evaluation of these two kernel's configurations (M-M, B-B) defines that the structural design of the Hermes system will be based on the M-M scheme. When failures occur on the quartet kernel, however, the M-M scheme will be converted into a B-B one by using a failure recovery procedure. The evaluation of the quartet kernel configurations is based on a probabilistic model. Failures and recovery procedures on the quartet kernel are discussed and the performance evaluation of the kernel (under failures) is also provided.
A Vision System with Real-Time Feature Extractor and Relaxation Network
Authors: Kubota Toshiro , Huntsberger Terry , Alford Cecil O.
Published: July 2003

This paper reviews our ongoing efforts on designing a real-time vision system. The system consists of a real-time feature extractor and a relaxation network. The filter system is capable of performing multiple 2D non-separable filters, multi-resolution decomposition, and steerable transform. The relaxation network is capable of performing various relaxation, diffusion and minimization operations. The paper shows several vision tasks which can be implemented effectively on the vision system.
Fingerprint Sub-Classification and Singular Point Detection
Authors: Drets Gustavo , Liljenström Hans
Published: July 2003

This paper presents a novel approach to fingerprint singular point detection. Singular points (cores and deltas) are used for fingerprint classification, sub-classification and registration. This method exploits the stability of the directional field pattern extracted from singular point regions at different resolution levels. The procedure is invariant to translations, scaling and small rotations. A fingerprint sub-classification procedure was built based on the proposed singular point detection method. Two kinds of tests were conducted on a subset consisting of 955 NIST-14 fingerprint images. First, automatic and forensic expert sub-classifications were compared. Second, the consistency of the proposed method was measured comparing automatic sub-classification for two different rolls of the same fingerprint.
Texture Classification with Single- and Multiresolution Co-Occurrence Maps
Authors: Valkealahti Kimmo , Oja Erkki
Published: July 2003

We have developed methods for the classification of textures with multidimensional co-occurrence histograms. Gray levels of several pixels with a given spatial arrangement are first compressed linearly and the resulting multidimensional vectors are quantized using the self-organizing map. Histograms of quantized vectors are classified by matching them with precomputed texture model histograms.

In the present study, a multiple resolution technique in linear compression of pixel values is evaluated. The multiple resolution linear compression was made with a local wavelet transform. The vectors were quantized with the tree-structured variant of the self-organizing map. In the tree-structured self-organizing map, the quantization error is reduced, in comparison to the traditional tree-structured codebook, by limited lateral searches in topologically-ordered neighborhoods. The performance of multiresolution texture histograms was compared with single-resolution histograms. The histogram method was compared with three well-established methods: co-occurrence matrices, Gaussian Markov random fields, and multiresolution Gabor energies. The results for a set of natural textures showed that the performance of single- and multiresolution texture histograms was similar. Thus, the benefit of multiresolution analysis was overridden by the multidimensionality of our texture models. Our method gave significantly higher classification accuracies than the three other methods.
A Regularization Method for Non-Trivial Codes in Polychotomous Classification
Authors: Utschick Wolfgang
Published: July 2003

Polychotomous classification is a widespread task in pattern recognition. A classifier relates an input pattern to a class Ck element of a fixed number K > 2 of classes C1, C2, …, CK. Neural networks for classification are generally based on the trivial 1-out-of-K coding. The advantage of non-trivial codes for discrimination of multiple classes lies in the increased Hamming distance between reference vectors, which makes error detection and even error correction feasible. Dichotomies from non-trivial codes are not as well arranged as from 1-out-of-K coding and may even have worse classification results after training. The code becomes superior to the trivial alternative only if its growing Hamming distance compensates for the increased tendency of single output errors. In this paper, the design of non-trivial error-correcting codes is based on the maximization of a cost function Φ, where the cost function is given by a trade-off between the empirical risk on training samples and a regularization term in the decision space. The introduced algorithm is based on a semi-implicit optimization of the given cost function. The results are related to the coding of multiple classes from a real classification task in handwritten character recognition. The complete system consists of parallel neural networks, each consisting of hidden neurons in the first layer and a Boolean function in the second layer. A comparison between optimized non-trivial codes and the trivial 1-out-of-K code is presented. It is shown that the generally applied 1-out-of-K code is not optimal. The optimum is reached by non-trivial coding without increasing the size of the classification system.
Land Cover/Land Use Classification of Urban Areas
Authors: Heikkonen Jukka , Varfis Aristide
Published: July 2003

This paper proposes a method for remote sensing based land cover/land use classification of urban areas. The method consists of the following four main stages: feature extraction, feature coding, feature selection and classification. In the feature extraction stage, statistical, textural and Gabor features are computed within local image windows of different sizes and orientations to provide a wide variety of potential features for the classification. Then the features are encoded and normalized by means of the Self-Organizing Map algorithm. For feature selection a CART (Classification and Regression Trees) based algorithm was developed to select a subset of features for each class within the classification scheme at hand. The selected subset of features is not attached to any specific classifier. Any classifier capable of representing possible skewed and multi-modal feature distributions can be employed, such as multi-layer perceptron (MLP) or k-nearest neighbor (k-NN). The paper reports experiments in land cover/land use classification with the Landsat TM and ERS-1 SAR images gathered over the city of Lisbon to show the potentials of the proposed method.
Application of Neural Networks in 3D Object Recognition System
Authors: Dyduch Tadeusz , Dudek-Dyduch Ewa
Published: July 2003

The authors' objective is to create a system of automatic recognition of 3D objects. The authors propose the application of the projection moiré method for acquisition of data about shapes of 3D objects. Neural nets play a crucial role in the system. Three basic parts of the system are presented in the paper. The first part is a data acquisition subsystem which contains an optic projection moiré system and receiving module with the CCD camera and the frame grabber computer card. The second part is the feed-forward neural network and other image processing software that pre-processes fringe pattern images obtained from the interferometry. The third part is the neural network realizing Self Organizing Map (SOM) clustering. The role of this net is automatic classification of particular parts of the image obtained at the second stage. Experiments confirmed that the proposed approach to recognition of 3D objects is very promising.
Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods
Authors: Gascuel Olivier , Bouchon-Meunier Bernadette , Caraux Gilles , Gallinari Patrick , Guénoche Alain , Guermeur Yann , Lechevallier Yves , Marsala Christophe , Miclet Laurent , Nicolas Jacques , Nock Richard , Ramdani Mohammed , Sebag Michèle , Tallur Basavanneppa , Venturini Gilles , Vitte Patrick
Published: July 2003

Supervised classification has already been the subject of numerous studies in the fields of Statistics, Pattern Recognition and Artificial Intelligence under various appellations which include discriminant analysis, discrimination and concept learning. Many practical applications relating to this field have been developed. New methods have appeared in recent years, due to developments concerning Neural Networks and Machine Learning. These "hybrid" approaches share one common factor in that they combine symbolic and numerical aspects. The former are characterized by the representation of knowledge, the latter by the introduction of frequencies and probabilistic criteria. In the present study, we shall present a certain number of hybrid methods, conceived (or improved) by members of the SYMENU research group. These methods issue mainly from Machine Learning and from research on Classification Trees done in Statistics, and they may also be qualified as "rule-based". They shall be compared with other more classical approaches. This comparison will be based on a detailed description of each of the twelve methods envisaged, and on the results obtained concerning the "Waveform Recognition Problem" proposed by Breiman et al.,4 which is difficult for rule based approaches.
Parallel Genetic-Based Hybrid Pattern Matching Algorithm for Isolated Word Recognition
Authors: Kwong S. , He Q. H. , Man K. F. , Tang K. S. , Chau C. W.
Published: July 2003

Dynamic Time Warping (DTW) is a common technique widely used for nonlinear time normalization of different utterances in many speech recognition systems. Two major problems are usually encountered when the DTW is applied for recognizing speech utterances: (i) the normalization factors used in a warping path; and (ii) finding the K-best warping paths. Although DTW is modified to compute multiple warping paths by using the Tree-Trellis Search (TTS) algorithm, the use of actual normalization factor still remains a major problem for the DTW. In this paper, a Parallel Genetic Time Warping (PGTW) is proposed to solve the above said problems. A database extracted from the TIMIT speech database of 95 isolated words is set up for evaluating the performance of the PGTW. In the database, each of the first 15 words had 70 different utterances, and the remaining 80 words had only one utterance. For each of the 15 words, one utterance is arbitrarily selected as the test template for recognition. Distance measure for each test template to the utterances of the same word and to those of the 80 words is calculated with three different time warping algorithms: TTS, PGTW and Sequential Genetic Time Warping (SGTW). A Normal Distribution Model based on Rabiner23 is used to evaluate the performance of the three algorithms analytically. The analyzed results showed that the PGTW had performed better than the TTS. It also showed that the PGTW had very similar results as the SGTW, but about 30% CPU time is saved in the single processor system.
Using Neural Networks to Detect Dominant Points in Chain-Coded Contours
Authors: Sanchiz J. M. , Pla F. , Iñesta J. M.
Published: July 2003

A novel approach for dominant point detection in chain-coded contours is presented. Classical operations, such as computing a measurement of the curvature from the (x, y) co-ordinates of the contour points, finding curvature maxima, etc., are substituted by a neural network that traverses the contour, and gives a measurement of the relevance of every point. Further and straight-forward processing of the network output provides the dominant points. Two translations of the Freeman chain-code are presented, that easily provide the network input from the chain link values. Results with real and test images are presented that show the feasibility of the proposed algorithm. The simulation of the neural computations on a sequential machine makes the execution time of this algorithm of the same order as that of existing algorithms, but the cost can be significantly reduced by executing these computations on a neural-oriented hardware.
Edge Detection
Authors: Vitulano S. , Ruberto C. Di , Nappi M.
Published: July 2003

The paper describes a technique called ISE for image segmentation using entropy. The relation between the entropy of an image domain and the entropy of its subdomains is explored as a uniformity predicate. Such entropy is obtained from the analysis of the image histogram associating a Gaussian distribution to the maximum frequency of gray levels.

In order to implement the model, we have introduced a well-known technique of Problem Solving. In our model, the most important roles are played by the Evaluation Function (EF) and the Control Strategy. The EF is related to the ratio between the entropy of one region or zone of the picture and the entropy of the entire picture, while the Control Strategy determines the optimal path in the search tree (quadtree) so that the nodes in the optimal path have minimal entropy.

The paper shows some comparisons between ISE and classical edge detection techniques.
A "Generalized" Lexical Functional Grammar-Based Processing of an Indian Language — Bangla
Authors: Sengupta P. , Chaudhuri B. B.
Published: July 2003

An efficient LFG parser implementation of Indian languages in general and Bangla in particular has been discussed. It has been shown that the classical technique of nonconfigurational syntactic encoding principles lead to too many disjunctive constraints to be satisfied by the parser. Noting that most of the disjunctions do not exist if an a priori knowledge of the verb is available, a "delayed" syntactic encoding formalism has been proposed. The points of syntactic encoding of noun phrases have been treated a "forward references" that are to be temporarily maintained in a "symbol table" for later precipitation. The proposed solution has two parts. The first part deals with identification of forward reference points, which is done by introducing a new metavariable and augmenting the scope of the Locate operator. The second part deals with precipitation of forward references through special schemata called m-structure schemata projected by the verb. An extension of the solution to one type of Bangla complex sentences has also been proposed (in the Appendix).

Implementation notes based on object-oriented programming principles have been provided.
A Preclassification Method for Handwritten Chinese Character Recognition Via Fuzzy Rules and Seart Neural Net
Authors: Lee Hahn-Ming , Lin Chin-Chou , Chen Jyh-Ming
Published: July 2003

In this paper, a method of character preclassification for handwritten Chinese character recognition is proposed. Since the number of Chinese characters is very large (at least 5401s for daily use), we employ two stages to reduce the candidates of an input character. In stage I, we extract the first set of primitive features from handwritten Chinese characters and use fuzzy rules to create four preclassification groups. The purpose in stage I is to reduce the candidates roughly. In stage II, we extract the second set of primitive features from handwritten Chinese characters and then use the Supervised Extended ART (SEART) as the classifier to generate preclassification classes for each preclassification group created in stage I. Since the number of characters in each preclassification class is smaller than that in the whole character set, the problem becomes simpler. In order to evaluate the proposed preclassification system, we use 605 Chinese character categories in the textbooks of elementary school as our training and testing data. The database used is HCCRBASE (provided by CCL, ITRI, Taiwan). In samples 1–100, we select the even samples as the training set, and the odd samples as the testing set. The characters of the testing set can be distributed into correct preclassification classes at a rate of 98.11%.
A Graph-Rewriting Paradigm for Discrete Relaxation
Authors: Fahmy H. , Blostein D.
Published: July 2003

In image analysis, recognition of the primitives plays an important role. Subsequent analysis is used to interpret the arrangement of primitives. This subsequent analysis must make allowance for errors or ambiguities in the recognition of primitives. In this paper, we assume that the primitive recognizer produces a set of possible interpretations for each primitive. To reduce this primitive-recognition ambiguity, we use contextual information in the image, and apply constraints from the image domain. This process is variously termed constraint satisfaction, labeling or discrete relaxation. Existing methods for discrete relaxation are limited in that they assume a priori knowledge of the neighborhood model: before relaxation begins, the system is told (or can determine) which sets of primitives are related by constraints. These methods do not apply to image domains in which complex analysis is necessary to determine which primitives are related by constraints. For example, in music notation, we must recognize which notes belong to one measure, before it is possible to apply the constraint that the number of beats in the measure should match the time signature. Such constraints can be handled by our graph-rewriting paradigm for discrete relaxation: here neighborhood-construction is interleaved with constraint-application. In applying this approach to the recognition of simple music notation, we use approximately 180 graph-rewriting rules to express notational constraints and semantic-interpretation rules for music notation. The graph rewriting rules express both binary and higher-order notational constraints. As image-interpretation proceeds, increasingly abstract levels of interpretation are assigned to (groups of) primitives. This allows application of higher-level constraints, which can be formulated only after partial interpretation of the image.
A Fuzzy-Like Approach for Smoothing and Edge Detection in Color Images
Authors: Moghaddamzadeh A. , Goldman D. , Bourbakis N.
Published: July 2003

Edge detection is one of the most important image processing steps towards image understanding. It is desired that edges be continuous and that the resultant regions or segments be completely isolated from their neighbors. Initially, images must first be smoothed to remove noise. In this paper, a novel fuzzy-like smoother algorithm is presented which removes camera noise and enhances edge contrast. The edge detection algorithm, which is applied on the smoothed image, is then presented. In this algorithm normalized hue in HSI space and color contrast in RGB space are combined using an aggregate operator. Pixels considered to be at least "nearly" locally maximum (defined within) are then found for all edge directions and the results are combined.
A Stable and Accurate Algorithm for Computing Epipolar Geometry
Authors: Boufama Boubakeur S. , Mohr Roger
Published: July 2003

This paper addresses the problem of computing the fundamental matrix which describes a geometric relationship between a pair of stereo images: the epipolar geometry. In the uncalibrated case, epipolar geometry captures all the 3D information available from the scene. It is of central importance for problems such as 3D reconstruction, self-calibration and feature tracking. Hence, the computation of the fundamental matrix is of great interest. The existing classical methods14 use two steps: a linear step followed by a nonlinear one. However, in some cases, the linear step does not yield a close form solution for the fundamental matrix, resulting in more iterations for the nonlinear step which is not guaranteed to converge to the correct solution. In this paper, a novel method based on virtual parallax is proposed. The problem is formulated differently; instead of computing directly the 3 × 3 fundamental matrix, we compute a homography with one epipole position, and show that this is equivalent to computing the fundamental matrix. Simple equations are derived by reducing the number of parameters to estimate. As a consequence, we obtain an accurate fundamental matrix with a stable linear computation. Experiments with simulated and real images validate our method and clearly show the improvement over the classical 8-point method.
Refining Region Estimates
Authors: Rosin Paul L.
Published: July 2003

A method for improving the segmentation of images is presented. It involves taking an initial segmentation provided by some other means, and modifying the region boundaries depending on the estimated region models until an equilibrium is reached. The advantages of this technique are: (1) no parameters are required, (2) it is invariant under constant scalings of the image intensities, and (3) it is relatively insensitive to the position and topology of the initial segmentation. Examples are given of its application to single and multi-scale intensity images, textured images, range images and multi-band satellite images.
Extracting Common Subtrees from Decision Trees
Authors: Lazarescu Mihai Mugurel , Caelli Terry , Venkatesh Svetha
Published: July 2003

This paper explores an efficient technique for the extraction of common subtrees in decision trees. The method is based on a Suffix Tree string matching process and the algorithm is applied to the problem of finding common decision rules in path planning.
A Line Segment Based Approach for 3D Motion Estimation and Tracking of Multiple Objects
Authors: Foresti G. L.
Published: July 2003

A line segment based approach for 3D motion estimation and tracking of multiple objects from a monocular image sequence is presented. Objects are described by means of 3D line segments, and their presence in the scene is associated with the detection of 2D line segments on the image plane. A change detection algorithm is applied to detect moving objects on the image plane and a Hough-based algorithm is used to individuate 2D line segments. 3D parameters of each line segment are estimated, at each time instant, by means of an extended Kalman filter (EKF), whose observations are the displacements of 2D line segment endpoints on the image plane. Results on both synthetic and real scenes are presented.
Scene Classification by Fuzzy Local Moments
Authors: Cheng H. D. , Desai Rutvik
Published: July 2003

The identification of images irrespective of their location, size and orientation is one of the important tasks in pattern analysis. The use of global moment features has been one of the most popular techniques for this purpose. We present a simple and effective method for gray-level image representation and identification which utilizes fuzzy radial moments of image segments (local moments) as features as opposed to global features. A multilayer perceptron neural network is employed for classification. Fuzzy entropy measure is applied to optimize the parameters of the membership function. The technique does not require translation, scaling or rotation of the image. Furthermore, it is suitable for parallel implementation which is an advantage for real-time applications. The classification capability and robustness of the technique are demonstrated by experiments on scaled, rotated and noisy gray-level images of uppercase and lowercase characters and digits of English alphabet, as well as the images of a set of tools. The proposed approach can handle rotation, scale and translation invariance, noise and fuzziness simultaneously.
Near Point Light Sources for Shape from Shading
Authors: Kao Sheng-Liang , Fuh Chiou-Shann
Published: July 2003

In this paper, a linear algorithm3,4 is proposed to recover shape information from multiple images, each of them is taken under the environment that all of the object surfaces are illuminated by a known near point light source. In this method, an approximate range of the distance for the objects to the viewer (e.g. camera) is previously defined. Using this predefined value, the absolute depth map of the objects can be found out.
Pattern Recognition in Alphabets of Oriya Language using Kohonen Neural Network
Authors: Mohanty S.
Published: July 2003

Here a computerized reading of alphabets of Oriya language is attempted using the Kohonen neural network and its unsupervized competitive learning capacity as self-organizing map or the Kohonen feature map. The proposed pattern recognition does not treat a pattern as an n-dimensional feature vector or a point in n-dimensional space as is done in the traditional pattern recognition theory. We have tried with all the Oriya alphabets and have presented the study with respect to five of them in this paper along with their average distance per pattern in each cycle till we reach the permissible average distance. In the output picture the variation of the weight vector with respect to the alphabets is clearly observed.
Teaching Computer Vision to Computer Scientists
Authors: Maxwell Bruce A.
Published: July 2003

Computer vision is a broad-based field of computer science that requires students to understand and integrate knowledge from numerous disciplines. Computer science (CS) majors, however, do not necessarily have an interdisciplinary background. In the rush to integrate, we can forget, or fail to plan for the fact that our students may not possess a broad undergraduate education. To explore the appropriateness of our education materials, this paper begins with a discussion of what we can expect CS majors to know and how we can use that knowledge to make a computer vision course a more enriching experience. The paper then provides a review of a number of the currently available computer vision textbooks. These texts differ significantly in their coverage, scope, approach, and audience. This comparative review shows that, while there are an increasing number of good textbooks available, there is still a need for new educational materials. In particular, the field would benefit from both an undergraduate computer vision text aimed at computer scientists and from a text with a stronger focus on color computer vision and its applications.
Teaching Image Computation in an Upper Level Elective on Robotics
Authors: Murphy Robin R.
Published: July 2003

This article offers a case study of how to teach image computation in an upper level elective course on robotics with a significant number of non-Computer Science majors. The MACS 415 course at the Colorado School of Mines is required for the popular interdisciplinary undergraduate minor in Robotics and AI. It is mandated to provide a broad survey of the artificial intelligence tools available to roboticists, including image computation.

Teaching image computation in a robotics elective is challenging both because of the limited time that can be spent on computer vision, and because of the attributes of the students. Non-CS majors typically do not have enough programming experience to program DSP algorithms, yet the students' preferred learning style is "hands-on." In order to reconcile this dilemma, we (1) cover a broad set of topics in class, (2) have several laboratory assignments using khoros, and (3) require the students to complete a robot project involving computer vision. The article summarizes the lessons learned to date, which are expected to be applicable to any course with non-majors involving image computation.
Illumination of Orthogonal Polygons with Orthogonal Floodlights
Authors: Abello James , Estivill-Castro Vladimir , Shermer Thomas , Urrutia Jorge
Published: July 2003

We provide the first tight bound for covering an orthogonal polygon with n vertices and h holes with vertex floodlights (guards with restricted angle of vision). In particular, we provide tight bounds for the number of orthogonal floodlights, placed at vertices or on the boundary, sufficient to illuminate the interior or the exterior of an orthogonal polygon with holes. Our results lead directly to very simple linear, and thus optimal, algorithms for computing a covering of an orthogonal polygon.
Two-Guard Walkability of Simple Polygons
Authors: Tseng L. H. , Heffernan P. , Lee D. T.
Published: July 2003

A pair of points s and g on the boundary of a simple polygon P admits a walk if two guards can simultaneously walk along the two boundary chains of P from s to g such that they are always visible to each other. The walk is a counter-walk if one guard moves from s to g while the other moves from g to s in the same direction along the boundary and they are always visible to each other. The (counter-)walk is straight if no backtracking is necessary during the (counter-)walk. In this paper, we show that, given a polygon with n vertices, to test if there exists (s,g) that admits a (straight) (counter-)walk can be solved in time O(n log n) and in linear space. Also we compute all (s,g)'s that admit a (straight) walk in O(n log n) time and all vertex pairs that admit a (straight) counter-walk in O(n log n + m), where m is O(n2).
Approximating Voronoi Diagrams of Convex Sites in Any Dimension
Authors: Vleugels Jules , Overmars Mark
Published: July 2003

Generalized Voronoi diagrams of objects are difficult to compute in a robust way, especially in higher dimensions. For a number of applications an approximation of the real diagram within some predetermined precision is sufficient. In this paper we study the computation of such approximate Voronoi diagrams. The emphasis is on practical applicability, therefore we are mainly concerned with fast (in terms of running time) computation, generality, robustness, and easy implementation, rather than optimal combinatorial and computational complexity. Given a set of disjoint convex sites in any dimension, we describe a general algorithm that approximates their Voronoi diagram with arbitrary precision. The only primitive operation that is required is the computation of the distance from a point to a site. The method is illustrated by its application to motion planning using retraction. To justify our claims on practical applicability, we provide experimental results obtained with implementations of the method in two and three dimensions.
Determining Weak Visibility of a Polygon from an Edge in Parallel
Authors: Chen Danny Z.
Published: July 2003

The problem of determining the weak visibility of an n-vertex simple polygon P from an edge e of P is that of deciding whether every point in P is weakly visible from e. In this paper we present an optimal parallel algorithm for solving this problem. Our algorithm runs in O( log n) time using O(n/ log n) processors in the CREW PRAM computational model, and is very different from the sequential algorithms for this problem. We also show how to solve optimally, in parallel, several other problems that are related to the weak visibility of simple polygons.
Finding the Convex Hull of Discs in Parallel
Authors: Chen Wei , Wada Koichi , Kawaguchi Kimio , Chen Danny Z.
Published: July 2003

We present a parallel method for finding the convex hull of planar discs in the EREW PRAM model. We show that the convex hull of n discs in the plane can be computed in O( log 1+ε n) time using O(n/ log ε n) processors or in O( log n log log n) time using O(n log 1+ε n) processors for any positive constant ε. The first result achieves cost optimal and the second one runs faster. We also show that the convex hull of planar discs can be constructed in O( log n) time using O(n) processors when the number of different kinds of radii is restricted to 2O( log α n) for any positive constant α with 0 < α < 1. Finally, we show that our method can be generalized to computing the convex hull of a large class of planar curves. We use a technique called multi-level divide-and-conquer in our algorithm.
Dog Bites Postman
Authors: Devillers Olivier , Golin Mordecai J.
Published: July 2003

In this paper, we discuss two variations of the two-dimensional post-office problem that arise when the post-offices are n postmen moving with constant velocities. The first variation addresses the question: given a point q0and time t0who is the nearest postman to q0at time t0? We present a randomized incremental data structure that answers the query in expected O( log2n) time. The second variation views a query point as a dog searching for a postman to bite and finds the postman that a dog running with speed vdcould reach first. While it is quite straightforward to design a data structure for the first problem, designing one for the second appears more difficult. We show that if the dog is quicker than all of the postmen then there is a nice correspondence between the problems. This correspondence will permit us to use the data structure developed for the first problem to solve the second one in O( log2n) time as well.

The proposed structure is semi-dynamic, that is the set of postmen can be modified by inserting new postmen. A fully dynamic structure that also supports deletions can be obtained, but in that case the query time becomes O( log3n).
Triangulation of Branching Contours Using Area Minimization
Authors: Hong Mu , Sederberg Thomas W. , Klimaszewski Krzysztof S. , Kaneda Kazufumi
Published: July 2003

This paper presents a new method for reconstructing piecewise linear surfaces from planar polygonal contours that branch. For non-branching contours, experience has shown that the piecewise linear surface of minimum surface area which connects a pair of contours often provides a good solution. The current algorithm extends this idea by searching for the surface of minimal area which connects two contours comprised of more than one polygon. Several examples that justify this heuristic are provided.
Implicit Methods for Geometry Creation
Authors: Ensz Mark T. , Storti Duane W. , Ganter Mark A.
Published: July 2003

In this paper, we briefly review the existing direct implicit geometry generation methods, which include creation of blending surfaces, a limited family of sweeps, and reconstruction of solid geometry from a sample set of surface data points. A broader approach is presented, utilizing the properties of the graph of the implicit defining function, to systematically construct swept solids and perform morphing between sections by implicit methods.
Specified–Precision Computation of Curve/Curve Bisectors
Authors: Farouki Rida T. , Ramamurthy Rajesh
Published: July 2003

The bisector of two plane curve segments (other than lines and circles) has, in general, no simple — i.e., rational — parameterization, and must therefore be approximated by the interpolation of discrete data. A procedure for computing ordered sequences of point/tangent/curvature data along the bisectors of polynomial or rational plane curves is described, with special emphasis on (i) the identification of singularities (tangent–discontinuities) of the bisector; (ii) capturing the exact rational form of those portions of the bisector with a terminal footpoint on one curve; and (iii) geometrical criteria the characterize extrema of the distance error for interpolants to the discretely–sample data. G1 piecewise– parabolic and G2 piecewise–cubic approximations (with O(h4) and O(h6) convergence) are described which, used in adaptive schemes governed by the exact error measure, can be made to satisfy any prescribed geometrical tolerance.