bluuebunny/crossref_metadata_2025 huggingface dataset:
Methods of Combining Multiple Classifiers with Different Features and Their Applications to Text-Independent Speaker Identification
Authors: Chen Ke , Wang Lan , Chi Huisheng
Published: October 2003

In practical applications of pattern recognition, there are often different features extracted from raw data which needs recognizing. Methods of combining multiple classifiers with different features are viewed as a general problem in various application areas of pattern recognition. In this paper, a systematic investigation has been made and possible solutions are classified into three frameworks, i.e. linear opinion pools, winner-take-all and evidential reasoning. For combining multiple classifiers with different features, a novel method is presented in the framework of linear opinion pools and a modified training algorithm for associative switch is also proposed in the framework of winner-take-all. In the framework of evidential reasoning, several typical methods are briefly reviewed for use. All aforementioned methods have already been applied to text-independent speaker identification. The simulations show that results yielded by the methods described in this paper are better than not only the individual classifiers' but also ones obtained by combining multiple classifiers with the same feature. It indicates that the use of combining multiple classifiers with different features is an effective way to attack the problem of text-independent speaker identification.
Designing Hopfield Type Networks Using Genetic Algorithms and Its Comparison with Simulated Annealing
Authors: Pal Sankar K. , De Susmita , Ghosh Ashish
Published: October 2003

An application of Genetic Algorithms (GAs) to evolve Hopfield type optimum neural network architectures for object extraction problem is demonstrated. Different optimizing functions involving minimization of energy value of the network, maximization of percentage of correct classification of pixels (pcc), minimization of number of connections of the network (noc), and a combination of pcc and noc have been considered. The noc value of the evolved (sub)optimal architectures is seen to be reduced to two-third of that required for the fully connected version. The performance of GA is seen to be better than that of Simulated Annealing for this problem.
Automatic Bankcheck Processing: A New Engineered System
Authors: Dimauro G. , Impedovo S. , Pirlo G. , Salzo A.
Published: October 2003

A new bankcheck processing system is presented in this paper. A full exploitation of the contextual knowledge, together with a multi-expert approach, have been used both to analyze the complex shape of handwritten text and to design the system.

Several processing modules have been integrated in the system. Some of the most relevant are those for data acquisition, preprocessing, machine-printed numeral recognition, layout analysis, courtesy amount recognition, legal amount recognition, amount validation, and signature verification. Some combination techniques have also been used in the system.

Reuse and maintenance of the system were two of the main goals of the designing process and the Khoros software tool was used for this purpose.
The A2iA Intercheque System: Courtesy Amount and Legal Amount Recognition for French Checks
Authors: Knerr S. , Anisimov V. , Baret O. , Gorski N. , Price D. , Simon J. C.
Published: October 2003

We developed a check reading system, termed INTERCHEQUE, which recognizes both the legal (LAR) and the courtesy amount (CAR) on bank checks. The version presented here is designed for the recognition of French, omni-bank, omni-scriptor, handwritten bank checks, and meets industrial requirements, such as high processing speed, robustness, and extremely low error rates.

We give an overview of our recognition system and discuss some of the pattern recognition techniques used. We also describe an installation which processes of the order of 70,000 checks per day. Results on a data base of about 170,000 checks show a recognition rate of about 75% for an error rate of the order of 1/10,000 checks.
A Prototype for Brazilian Bankcheck Recognition
Authors: Lee Luan L. , Lizarraga Miguel G. , Gomes Natanael R. , Koerich Alessandro L.
Published: October 2003

This paper describes a prototype for Brazilian bankcheck recognition. The description is divided into three topics: bankcheck information extraction, digit amount recognition and signature verification. In bankcheck information extraction, our algorithms provide signature and digit amount images free of background patterns and bankcheck printed information. In digit amount recognition, we dealt with the digit amount segmentation and implementation of a complete numeral character recognition system involving image processing, feature extraction and neural classification. In signature verification, we designed and implemented a static signature verification system suitable for banking and commercial applications. Our signature verification algorithm is capable of detecting both simple, random and skilled forgeries. The proposed automatic bankcheck recognition prototype was intensively tested by real bankcheck data as well as simulated data providing the following performance results: for skilled forgeries, 4.7% equal error rate; for random forgeries, zero Type I error and 7.3% Type II error; for bankcheck numerals, 92.7% correct recognition rate.
A Fault-Tolerant Chinese Check Recognition System
Authors: Su Hui , Zhao Bin , Ma Feng , Wang Song , Xia Shaowei
Published: October 2003

In this paper, a complete fault-tolerant check recognition system is proposed which has no check substitution error under the secret code verification. The fault-tolerant recognition method proposed in this paper creates all possible candidates for verification under the limited fault-tolerant rate, and with three classifiers of high isolated digit recognition rate, the system can always find out the correct recognition results of checks if there exist the correct labels of all the digits. Since the three classifiers are designed independently by different methods and they extract different features of handwritten digits, they can compensate each other when confusing digits are met. The segmentation stage combines the three most popular strategies, and gives out a way for segmenting unconstrained handwritten numeral strings on Chinese checks.
Multi-Bank Check Recognition System: Consideration on the Numeral Amount Recognition Module
Authors: Heutte L. , Barbosa-Pereira P. , Bougeois O. , Moreau J. V. , Plessis B. , Courtellemont P. , Lecourtier Y.
Published: October 2003

This paper presents a complete numeral amount recognition module which is integrated in an automatic system aimed at reading all types of French checks. This module is combined with an automatic reading system of literal amounts. This complete working system, called LIREChèques, is developed by MATRA MS&I and is now in advanced test at SERINTEL, a pilot site.

Two aspects of the numeral amount recognition system are particularly emphasized: the numeral recognition stage itself and the syntactic analysis stage. The numeral recognition module relies on a combination of two individual classifiers, the first one is based on concavity measurements, the second one on both statistical and structural features. The syntactic analysis, called syntactic/contextual analysis, is combined with contextual information to take into account the segmentation behaviour and the presence of literal entities in the numeral amount.

We demonstrate that very good performances can be obtained on digits such as those extracted from numeral amounts since a substitution rate of 0.06% while still preserving a recognition rate of near 87% can be achieved. As for the syntactic/contextual analysis stage, results obtained on a test set (containing checks from more than 40 different banks and 15/ of typed checks, thus being a good representation of the real tests realized on site) show clearly that introduction of contextual information in association with syntactic analysis allows to process much more numeral amounts than a simple syntactic analysis and increases perceptibility of the recognition rate.
Automatic Reading of Handwritten Amounts on French Checks
Authors: Leroux Manuel , Lethélier Edouard , Gilloux Michel , Lemarié Bernard
Published: October 2003

We describe an implemented system which reads amounts on French checks images. This system is made of two modules which recognize independently the courtesy and the legal amount. The first one is based on a segmentation-by-recognition approach while the second uses hidden Markov models of words. The outputs of these two modules are combined into a system decision. A high reliability is achieved through this combination since the errors made by the recognition modules should be mostly uncorrelated.
Check Amount Recognition Based on the Cross Validation of Courtesy and Legal Amount Fields
Authors: Dzuba Gregory , Filatov Alexander , Gershuny Dmitry , Kil Igor , Nikitin Vadim
Published: October 2003

Check amount recognition is one of the most promising commercial applications of handwriting recognition. This paper is devoted to the description of the check reading system developed to recognize amounts on American personal checks. Special attention is paid to a reliable procedure developed to reject doubtful answers. For this purpose the legal (worded) amount on a personal check is recognized along with the courtesy (digit) amount. For both courtesy and legal amount fields, a brief description of all recognition stages beginning with field extraction and ending with the recognition itself are presented. We also present the explanation of problems existing at each stage and their possible solutions. The numeral recognizer used to read the amounts written in figures is described. This recognizer is based on the procedure of matching input subgraphs to graphs of symbol prototypes. Main principles of the handwriting recognizer used to read amounts written in words are explained. The recognizer is based on the idea of describing the handwriting with the most stable handwriting elements. The concept of the optimal confidence level of the recognition answer is introduced. It is shown that the conditional probability of the answer correctness is an optimal confidence level function. The algorithms of the optimal confidence level estimation for some special cases are described. The sophisticated algorithm of cross validation between legal and courtesy amount recognition results based on the optimal confidence level approach is proposed. Experimental results on real checks are presented. The recognition rate at 1% error rate is 67%. The recognition rate without reject is 85%. Significant improvement is achieved due to legal amount processing in spite of a relatively low recognition rate for this field.
Bankcheck Recognition using Cross Validation Between Legal and Courtesy Amounts
Authors: Kim Gyeonghwan , Govindaraju Venu
Published: October 2003

A bankcheck reading system using cross validation of both the legal and the courtesy amounts is presented in this paper. Some of the challenges posed by the task are

(i) segmentation of the legal amount into words,

(ii) location of boundaries between dollars and cents amounts, and

(iii) high accuracy in terms of recognition performance.

Word segmentation in the legal amount is a serious issue because of the nature of the data and patrons' writing habits which tend to clump words together. We have developed a word segmentation algorithm based on the character segmentation results to address this issue. The list of possible amounts generated by the word segmentation hypotheses is used as lexicon for the courtesy amount recognition. The order of magnitude of the amount is estimated during legal amount recognition. We treat the courtesy amount as a numeral string and apply the same word recognition scheme as used for the legal amount.

Our approach to check recognition differs from traditional methods in two significant aspects: First, our emphasis on both the legal and the courtesy amounts is balanced. We use an accurate word recognizer which performs equally well on alpha words and digit strings. Second, our combination strategy is serial rather than the commonly used parallel method. Experimental results show that 43.8% of check images are correctly read with an error rate of 0%.
Automatic Extraction of Baselines and Data From Check Images
Authors: Liu Ke , Suen Ching Y. , Cheriet M. , Said Joseph N. , Nadal Christine , Tang Yuan Y.
Published: October 2003

A novel approach to extract data from check images is proposed based on the determination of baselines of checks, a priori information about the positions of data on checks, and a layout-driven item extraction method. Several techniques and algorithms have been developed in this approach including check image preprocessing, the extraction and identification of baselines, the extraction of the strokes of handwritten legal amounts, courtesy amounts and date, and the separation of strokes connected to baselines. A complete working system has been developed. The results of both testing experiments and on-line applications show that this approach is effective and the proposed techniques and algorithms perform well.
Extraction of User Entered Components from a Personal Bankcheck Using Morphological Subtraction
Authors: Okada Minoru , Shridhar Malayappan
Published: October 2003

In this paper we propose a new method to extract user entered components from a personal bankcheck. The proposed technique utilizes a pair of personal bankcheck images: (i) blank check image referred to as the reference image and (ii) a filled-in check image referred to as the sample image. This pair of check images (reference image and sample image) differ only at specific regions of the check reserved for insertion of handwritten (machine printed) components. A sophisticated technique of inter-image subtractions can extract the user entered components. Also the background patterns of reference image and sample images are not identical because of statistical variations in printing and scanning conditions. The authors propose a morphological inter-image subtraction that consists of a GLS-fusion procedure and a logical subtraction procedure in gray-level space (GLS) (x, y, f(x, y)) to extract user entered components from the filled bankcheck. The user entered components are extracted by this method in a very robust manner and the extracted components are suitable for posterior word/string recognition step. This method with morphological subtraction can also be used for other forms.
An Evolutionary Approach to the Use of Neural Networks in the Segmentation of Handwritten Numerals
Authors: Westall J. M. , Narasimha M. S.
Published: October 2003

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.
Recognition of Handwritten Worded Amounts on Checks Using Symbolic and Neural Classifiers
Authors: Dodel Jean-Pierre , Shinghal Rajjan
Published: October 2003

This paper proposes symbolic and neural classifiers to read unconstrained handwritten worded amounts in bankchecks. Features are extracted from the binary image of the worded amount. Depending on the features extracted, some words are recognized entirely symbolically, some words entirely neurally, and the remaining both symbolically and neurally. Results of experiments at word level and check level are provided.
An Off-Line Cursive Handwritten Word Recognition System and Its Application to Legal Amount Interpretation
Authors: Han Ke , Sethi Ishwar K.
Published: October 2003

Off-line cursive script recognition has got increasing attention during the last three decades since it is of interest in several areas such as banking and postal service. An off-line cursive handwritten word recognition system is described in this paper and is used for legal amount interpretation in personal checks. The proposed recognition system uses a set of geometric and topologic features to characterize each word. By considering the spatial distribution of these features in a word image, the proposed system maps each word into two strings of finite symbols. A local associative indexing scheme is then used on these strings to organize a vocabulary. When presented with an unknown word, the system uses the same indexing scheme to retrieve a set of candidate words likely to match the input word. A verification process is then carried out to find the best match among the candidate set. The performance of the proposed system has been tested with a legal amount image database from real bankchecks. The results obtained indicate that the proposed system is able to recognize legal amounts with great accuracy.
High Performance Unconstrained Word Recognition System Combining HMMs and Markov Random Fields
Authors: Saon George , Belaïd Abdel
Published: October 2003

In this paper we present a system for the recognition of handwritten words on literal check amounts which advantageously combine HMMs and Markov random fields (MRFs). It operates at pixel level, in a holistic manner, on height normalized word images which are viewed as random field realizations. The HMM analyzes the image along the horizontal writing direction, in a specific state observation probability given by the column product of causal MRF-like pixel conditional probabilities. Aspects concerning definition, training and recognition via this type of model are developed throughout the paper. We report a 90.08% average word recognition rate on 2378 words and a 79.52% amount rate on 579 amounts of the SRTP* French postal check database (7031 words, 1779 amounts, different scriptors).
Optimal Order of Markov Models Applied to Bankchecks
Authors: Olivier C. , Paquet T. , Avila M. , Lecourtier Y.
Published: October 2003

The aim of this study is to show that the optimal order of Markov Model of cursive words can be rigorously stated in order to fit the structural properties of the observed data using Akaike information criterion. The method has been tested on French Postal check amounts up to order 4. An original structural representation of cursive words based on graphemes is used. The conditional probability to have a word model given an observed sequence of graphemes is computed independently of the length of the sequence. The recognition results obtained confirm the optimal order found using Akaike criterion.
A Cognitive Approach to Off-Line Signature Verification
Authors: Murshed Nabeel A. , Sabourin Robert , Bortolozzi Flávio
Published: October 2003

This paper presents an Off-line Signature Verification System for identifying random forgeries aimed at banking application. The cognitive information learning process in the proposed system is inspired by some characteristics of human learning. Four features distinguish the proposed system from those proposed thus far. First, the verification task is accomplished without a priori knowledge of the class of random forgeries. Second, no explicit modeling or making geometrical measurements are used to represent the signature. Third, the decision of the system is made throughout the use of two-stage verification process by which a global and/or local analysis are performed on the unknown signature. The global analysis is concerned with the overall shape of the unknown signature, whereas, the local analysis is concerned with the local features composing the unknown signature. Fourth, these analysis are performed at the boundary or within a predefined search region called the identity grid designed for each writer in the system. The proposed system is evaluated with a data base of 800 signatures.
A Multi-Expert Signature Verification System for Bankcheck Processing
Authors: Dimauro G. , Impedovo S. , Pirlo G. , Salzo A.
Published: October 2003

In this paper a multi-expert signature verification system is presented. The system has been specifically designed for applications in the field of bankcheck processing. For this purpose, it combines three different algorithms for signature verification. A wholistic approach is used in the first algorithm, a component-oriented approach is used in the second and third algorithms. The second algorithm is based on a structure-based procedure, the third algorithm uses a highly-adaptive neural network. The three algorithms are combined in the multi-expert system by a voting strategy.
Hand Gesture Recognition using Ensembles of Radial Basis Function (RBF) Networks and Decision Trees
Authors: Gutta Srinivas , Imam Ibrahim F. , Wechsler Harry
Published: October 2003

Hand gestures are the natural form of communication among people, yet human-computer interaction is still limited to mice movements. The use of hand gestures in the field of human-computer interaction has attracted renewed interest in the past several years. Special glove-based devices have been developed to analyze finger and hand motion and use them to manipulate and explore virtual worlds. To further enrich the naturalness of the interaction, different computer vision-based techniques have been developed. At the same time the need for more efficient systems has resulted in new gesture recognition approaches. In this paper we present an hybrid intelligent system for hand gesture recognition. The hybrid approach consists of an ensemble of connectionist networks — radial basis functions (RBF) — and inductive decision trees (AQDT). Cross Validation (CV) experimental results yield a false negative rate of 1.7% and a false positive rate of 1% while the evaluation takes place on a data base including 150 images corresponding to 15 gestures of 5 subjects. In order to assess the robustness of the system, the vocabulary of the gestures has been increased from 15 to 25 and the size of the database from 150 to 750 images corresponding now to 15 subjects. Cross Validation (CV) experimental results yield a false negative rate of 3.6% and a false positive rate of 1.8% respectively. The benefits of our hybrid architecture include

(i) robustness via query by consensus as provided by ensembles of networks when facing the inherent variability of the image formation and data acquisition process,

(ii) classifications made using decision trees,

(iii) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds and

(iv) interpretability of the way classification and retrieval is eventually achieved.
The United Adaptive Learning Algorithm for the Link Weights and Shape Parameter in RBFN for Pattern Recognition
Authors: Huang De-Shuang
Published: October 2003

This paper proposes a united training method of the link weights of the Gaussian radial basis function networks (GRBFN) and the shape parameter α of the RBF. The training method corresponding to the former is a kind of recursive least squares backpropagation (RLS-BP) learning algorithm which is an accurately recursive method, the training method corresponding to the latter is an adaptive gradient descending (AGD) searching algorithm which is an approximately approaching method. We use the one-dimensional images of radar targets to study the effect of the shape parameter α on the rate of recognition, and survey the changes of the shape parameter αs of radial basis functions corresponding to different hidden nodes, and present the judgement confidence curves of different radar targets. In addition, the forgotten factor λ which makes the effects on the speed of convergence is also discussed. The experimental results are presented.
Manipulating images by using Run-length Morton Codes
Authors: Huang Chi-Yen , Chung Kuo-Liang
Published: October 2003

In this paper, we first present a variation of the 2-dimensional run-encoding, called the run-length Morton code encoding scheme, for compressing binary images, then we present efficient algorithms for manipulating set operations and performing conversions between the proposed encoding scheme and some well-known spatial data structures. The time complexities of set operations are linearly proportional to the size (number) of the run-length Morton codes and the time complexities of conversions are linearly proportional to the number of the nodes in the corresponding quadtree/bintree with respect to the run-length Morton codes.
Learning to Recognize 3D Objects using Sparse Depth and Intensity Information
Authors: MccCane Brendan , Caelli Terry , de Vel Olivier
Published: October 2003

In this paper we further explore the use of machine learning (ML) for the recognition of 3D objects in isolation or embedded in scenes. Of particular interest is the use of a recent ML technique (specifically CRG — Conditional Rule Generation) which generates descriptions of objects in terms of object parts and part-relational attribute bounds. We show how this technique can be combined with intensity-based model and scene–views to locate objects and their pose. The major contributions of this paper are: the extension of the CRG classifier to incorporate fuzzy decisions (FCRG), the application of the FCRG classifier to the problem of learning 3D objects from 2D intensity images, the study of the usefulness of sparse depth data in regards to recognition performance, and the implementation of a complete object recognition system that does not rely on perfect or synthetic data. We report a recognition rate of 80% for unseen single object scenes in a database of 18 non-trivial objects.
Empirical Design of a Multi-Classifier Thresholding/Control Strategy for Recognition of Handwritten Street Names
Authors: Madhvanath S. , Kleinberg E. , Govindaraju V.
Published: October 2003

A central task in the interpretation of handwritten US postal addresses is the off-line recognition of the street name. A lexicon of candidate street names may be extracted from a database of postal delivery points (DPF) by first locating and recognizing numeric fields such as the ZIP code and strewet number. The off-line handwritten word recognition (HWR) task is made difficult by the unconstrained, omni-scriptor nature of the input, and incomplete lexicons resulting from errors in processing numeric fields and intrinsic deficiencies in the DPF. In this paper, we describe an empirical approach to the design of a multi-classifier HWR Thresholding/Control module which forms part of a real-time handwritten address interpretation (HWAI) system. The decisions of two word classifiers are combined in a hierarchical manner to improve recognition and error-rejection performance, while meeting real-time requirements. The design employs logistic regression and agreement for evidence combination, and lexicon reduction for improved throughput as well as performance. The paper concludes with experimental results and directions for future research.
Efficient Morphological Set Transformations on Line Drawings
Authors: Cardoner Rafael , Thomas Federico
Published: October 2003

Image compression techniques have been recently used not only for reducing storage requirements, but also computational costs when processing images on low cost computers. This approach might be also of interest for processing large engineering drawings, where feature extraction techniques must be intensively applied for their segmentation into regions of interest for subsequent analysis. This paper explores this alternative using a simple run-length compression, leading to excellent results.

Although this approach is not new and can be classified within the decomposition paradigm used since the early stages of line drawing image processing, the developed formalism allows directional morphological set transformations to be performed, on a low cost personal computer, faster than on costly parallel computers for the same, but uncompressed, images. This good performance is proved in two different applications: the generation of homotopic skeletons through thinning processes, and the extraction of linear features through serializing multiangle parallelism operations.
ORACLE: An Integrated Learning Approach for Object Recognition
Authors: Ming John , Bhanu Bir
Published: October 2003

Model-based object recognition has become a popular paradigm in computer vision research. In most of the current model-based vision systems, the object models used for recognition are generally a priori given (e.g. obtained using a CAD model). For many object recognition applications, it is not realistic to utilize a fixed object model database with static model features. Rather, it is desirable to have a recognition system capable of performing automated object model acquisition and refinement. In order to achieve these capabilities, we have developed a system called ORACLE: Object Recognition Accomplished through Consolidated Learning Expertise. It uses two machine learning techniques known as Explanation-Based Learning (EBL) and Structured Conceptual Clustering (SCC) combined in a synergistic manner. As compared to systems which learn from numerous positive and negative examples, EBL allows the generalization of object model descriptions from a single example. Using these generalized descriptions, SCC constructs an efficient classification tree which is incremently built and modified over time. Learning from experience is used to dynamically update the specific feature values of each object. These capabilities provide a dynamic object model database which allows the system to exhibit improved performance over time. We provide an overview of the ORACLE system and present experimental results using a database of thirty aircraft models.
Towards Developing a Practical System to Recover Light, Reflectance and Shape
Authors: Bakshi Sanjay , Yang Yee-Hong
Published: October 2003

Due to the complexity of the shape-from-shading problem, most solutions rely on idealistic conditions. Orthographic imaging, a known distant point light source, and known surface reflectance properties are usually assumed. Furthermore, most real surfaces are neither perfectly diffuse (Lambertian) nor ideally specular (mirror-like); however most shape-from-shading algorithms assume Lambertian reflectance.

The behavior of shape-from-shading algorithms that rely on idealistic conditions is unpredictable in real imaging situations. In this paper, the LIRAS (LIght, Reflectance, And Shape) Recovery System is proposed. LIRAS is a practical approach to the shape-from-shading problem, as many of these assumptions are relaxed. LIRAS is also a modular system: there is a component that recovers the surface reflectance properties, thus the assumption of Lambertian reflectance is relaxed. Rather than assume a known illuminant direction, a component exists that can recover the light orientation. Once the reflectance map is determined, another LIRAS module can use this information to recover the shape for non-Lambertian surfaces. Each of these modules is described and a discussion of how the components cooperate to recover three-dimensional shape information in real environments is given. Extensive experimental evaluation is conducted using both synthetic and real images and the results are very encouraging. The contributions of this paper include the design and implementation of LIRAS and the extensive quantative and qualitative experimental results, which can provide guidelines for future refinements of other shape recovery systems.
Nonclosure Properties of Two-Dimensional One-Marker Automata
Authors: Ito Akira , Inoue Katsushi , Wang Yue
Published: October 2003

Several nonclosure properties of each class of sets accepted by two-dimensional alternating one-marker automata, alternating one-marker automata with only universal states, nondeterministic one-marker automata, deterministic one-marker automata, alternating finite automata, and alternating finite automata with only universal states are shown. To do this, we first establish the upper bounds of the working space used by "three-way" alternating Turing machines with only universal states to simulate those "four-way" non-storage machines. These bounds provide us a simplified and unified proof method for the whole variants of one-marker and/or alternating finite state machine, without directly analyzing the complex behavior of the individual four-way machine on two-dimensional rectangular input tapes. We also summarize the known closure properties including Boolean closures for all the variants of two-dimensional alternating one-marker automata.
Parallel Distributed Detection of an Invariant Feature Associated with Self-Similar Patterns
Authors: Kamejima Kohji
Published: October 2003

A parallel distributed scheme is presented for extracting a computable feature associated with self similar patterns. Observed patterns are assumed to be specified in terms of a set of contraction mappings that evokes an "avalanche of exploration" in image field. This intrinsically non-deterministic imaging process yields a conditional probability that is represented on a diffusion system. For identifying mapping set, a parallel projection algorithm is designed on a computable set of local minimums of the conditional distribution. The scheme is applied to dynamic detection of fractal patterns.
Parallel Tools for Colored Image Processing
Authors: Lozano Vincent , Ubéda Stéphane , Vigouroux Xavier
Published: October 2003

Over recent years clusters of workstations have begun to replace systems of mainframes. These workstations can be used to behave as a parallel machine, and then could supply supercomputing performance at modest cost. We report an experiment of such a network within PVM framework for an industrial image analysis application.

The goal of the application is to build an automated process of textile color pattern analysis. We plan to build several tools for parallel image analysis, and among those tools, we present here first the parallelization of a quantization algorithm and a way to parallelize a color image pyramid using the Parallel Virtual Machine environment.

One important point is the use of the distributed storage that represents a set of workstations. This distribution allows the complete parallelization of the Read/Write operations.
Asynchronous Relaxation of Morphological Operators: A Joint Algorithm-Architecture Perspective
Authors: Robin F. , Privat G. , Renaudin M.
Published: October 2003

Asynchronous dynamics are introduced for the iterative computation of morphological image filters. We propose a fine-grain asynchronous algorithmic- architectural model that exploits these new possibilities to improve the convergence efficiency of such operators over standard sequential-recursive or parallel-synchronous update modes. We conclude with VLSI design perspectives and show how asynchronism can be exploited both at functional and architectural levels.
Fast Local and Global Illuminations Through a Simd Z-Buffer
Authors: Renaud C.
Published: October 2003

The z-buffer is a well-known hidden-part removal technique commonly used by local illumination algorithms. Some global illumination approaches use this technique too, in order to approximate energy exchanges. In this paper we propose a massively parallel implementation of the z-buffer on the MP-1 machine. Efficiency is achieved by precisely studying the different stages of the algorithm, and by taking care in correctly using the SIMD control of the architecture. Local illumination models are then applied to the z-buffer algorithm, by using Gouraud and Phong's interpolations. Finally, the parallel z-buffer is used in a massively parallel radiosity algorithm. The results obtained allow to provide quickly illuminated images by decreasing dramatically the computation time required for global illumination.
A Load Balanced Parallel Ground Visualization Tool
Authors: Contassot-Vivier Sylvain
Published: October 2003

We propose in this paper, a parallel implementation of a ground visualization algorithm. Our input data consist in a Digital Elevation Model (DEM) covering a rectangular region, together with a raster image of the same area (the texture). The goal of the algorithm is to compute in parallel, images of the DEM from any point of view while mapping the texture onto the surface. The main originality of our approach concerns the distribution of the data, leading to a load-balanced and scalable parallel algorithm. We use a workload estimation to partition the output image, and then redistribute the input data according to this division. Special attention is paid on the data structures used for minimizing the cost of communications.
Modeling 3D Deformable Object with the Active Pyramid
Authors: Reissman Pierre-Jean , Magnin Isabelle E.
Published: October 2003

Medical imaging is a powerful mean to access dynamic function of 3D deformable organs of the body. Due to the flow of incoming data, multiresolution methods on parallel computers is the only way to achieve complex processings in reasonable time. We present an active pyramid to model dynamic volumes. This pyramid is built on the first volume of a sequence and contains a binary model of the objects of interest. Previous knowledge is introduced within this binary model. The structure of the pyramid is rigid, but its main interest is that its components are deformed to fit the data using energetical constraints. A multiresolution algorithm based on self-organizing maps is then applied to deform the model through time. This algorithm matches the different levels of the pyramid in a coarse to fine approach. The output of the matching process is the field of deformation, modeling the transformations. This pyramid is applied to real data in the result section. The rigid structure of the pyramid is suitable for massively parallel architectures.
Complexity Analysis of a Parallel Implementation of the Marching-Cubes Algorithm
Authors: Miguet Serge , Nicod Jean-Marc
Published: October 2003

This paper presents a load-balanced parallelization of the well known Marching-Cubes algorithm, that aims at constructing an iso-surface in a 3D image. We first derive a modelization for the computation time as a function of the generated surface complexity. The workload associated to each slice of the input data is evaluated by counting the number of vertices that will be generated on that slice. The slices are then locally redistributed to ensure a balanced workload. We give an upper bound on the number of polygons of the triangulation, and present a family of surfaces whose number of triangles tends to this bound. This analysis allows us to foresee (and thus to allocate) the memory size needed for the data structures and to assign to each vertex a unique global reference.

Experiments done on an Intel Paragon machine are given both for synthetic and medical images. They show the usefulness of our dynamic data redistribution scheme.
Neuroanatomical Segmentation in MRI: Technological Objectives
Authors: Worth Andrew J. , Makris Nikos , Caviness Jr. Verne S. , Kennedy David N.
Published: October 2003

This paper offers a definition of precise, comprehensive, robust and practical neuroanatomical segmentation in magnetic resonance brain images with the goal of performing quantitative morphometric analyses. The main types of difficulties experienced with such problems are described, including those relating to the classification of MR signal intensities and the fact that there is insufficient information in the 2D image. To illustrate the details of obtaining a morphometric description, a case study of semi-automated methods is presented for segmenting the lateral ventricles and caudate nucleus in T1 coronal MR image data. The most significant remaining difficulties are summarized and are offered as objectives for further research.
An Automated Technique for Statistical Characterization of Brain Tissues in Magnetic Resonance Imaging
Authors: Pham Dzung , Prince Jerry L. , Xu Chenyang , Dagher Azar P.
Published: October 2003

A procedure for estimating the joint probability density function (pdf) of T1, T2 and proton spin density (PD) for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) in the brain is presented. The pdf's have numerous applications, including the study of tissue parameter variability in pathology and across populations. The procedure requires a multispectral, spin echo magnetic resonance imaging (MRI) data set of the brain. It consists of five automated steps:

(i) preprocess the data to remove extracranial tissue using a sequence of image processing operators;

(ii) estimate T1, T2 and PD by fitting the preprocessed data to an imaging equation;

(iii) perform a fuzzy c-means clustering on the same preprocessed data to obtain a spatial map representing the membership value of the three tissue classes at each pixel location;

(iv) reject estimates which are not from pure tissue or have poor fits in the parameter estimation, and classify the remaining estimates as either GM, WM or CSF;

(v) compute statistics on the classified estimates to obtain a probability mass function and a Gaussian joint pdf of the tissue parameters for each tissue class.

Some preliminary results are shown comparing computed pdf's of young, elderly and Alzheimer's subjects. Two brief examples applying the joint pdf's to pulse sequence optimization and generation of computational phantoms are also provided.
A Statistical Method for Efficient Segmentation of MR Imagery
Authors: Kaufhold J. , Schneider M. , Willsky A. S. , Karl W. C.
Published: October 2003

Magnetic resonance imaging (MRI) has become a widely used research and clinical tool in the study of the human brain. The ability to robustly and accurately quantify repeatable morphological measures from such data is aided by the ability to accurately segment the MRI data set into homogeneous regions such as gray matter, white matter, and cerebro spinal fluid. The large amount of data associated with typical MRI scans makes completely manual segmentation prohibitive on a large scale. In this paper an efficient approach to the segmentation of such MR imagery is presented. The approach uses an estimation-theoretic interpretation of the segmentation problem to develop a computationally efficient, statistically-based recursive technique for its solution. Being statistically based, the method also provides associated measures of uncertainty of the resulting estimates, which are extremely important both for evaluation of the estimates as well as their combination with other sources of information.
Segmentation of MR Images using Curve Evolution and Prior Information
Authors: Pien Homer H. , Desai Mukund , Shah Jayant
Published: October 2003

Segmentation of anatomic structures of the human brain from MR images is important for assessing treatment efficacy, screening for anomalies, and improving our understanding of human development. The labor intensive nature of manual segmentation, however, makes such a technique viable only in selected cases. In this paper we present a new approach to segmentation that involves only minimal human interactions. The technique utilizes a variational formulation to obtain an edge-strength function over the region of interest, and uses curve evolution and a pre-segmented atlas to guide the actual segmentation process. The approach is demonstrated via both phantoms and actual MR images, and when applied to the lateral ventricles and caudate nucleus, showed a size accuracy error of 5%–20% with respect to manual segmentation, depending on the manual segmentation method utilized.
An Integrated Approach for Locating Neuroanatomical Structure from MRI
Authors: Staib Lawrence H. , Chakraborty Amit , Duncan James S.
Published: October 2003

The wide availability of high resolution magnetic resonance images (MRI) of the brain has facilitated tremendous progress in neuroscience. Accurate automated segmentation and quantification of neuroanatomical structure from such images is crucial for the advancement of the understanding of brain morphology, both in normal variation and in disease. Gradient-based deformable surface finding is a powerful technique for locating structure in three-dimensional images. However, it often suffers from poorly defined edges and noise. This paper proposes a gradient-based deformable surface finding approach that integrates region information. This makes the resulting procedure more robust to noise and improper initialization. In addition, prior shape information may be incorporated. The algorithm uses Gauss's Divergence theorem to find the surface of a homogeneous region-classified area in the image and integrates this with a gray-level gradient-based surface finder. Experimental results on synthetic and MR brain images show that a significant improvement is achieved as a consequence of the use of this extra information. Further, these improvements are achieved with little increase in computational overhead, an advantage derived from the application of Gauss's Divergence theorem.
Animal: Validation and Applications of Nonlinear Registration-Based Segmentation
Authors: Collins D. L. , Evans A. C.
Published: October 2003

Magnetic resonance imaging (MRI) has become the modality of choice for neuro-anatomical imaging. Quantitative analysis requires the accurate and reproducible labeling of all voxels in any given structure within the brain. Since manual labeling is prohibitively time-consuming and error-prone we have designed an automated procedure called ANIMAL (Automatic Nonlinear Image Matching and Anatomical Labeling) to objectively segment gross anatomical structures from 3D MRIs of normal brains. The procedure is based on nonlinear registration with a previously labeled target brain, followed by numerical inverse transformation of the labels to the native MRI space. Besides segmentation, ANIMAL has been applied to non-rigid registration and to the analysis of morphometric variability.

In this paper, the nonlinear registration approach is validated on five test volumes, produced with simulated deformations. Experiments show that the ANIMAL recovers 64% of the nonlinear residual variability remaining after linear registration. Segmentations of the same test data are presented as well.

The paper concludes with two applications of ANIMAL using real data. In the first, one MRI volume is nonlinearly matched to a second and is automatically segmented using labels, predefined on the second MRI volume. The automatic segmentation compares well with manual labeling of the same structures. In the second application, ANIMAL is applied to seventeen MRI data sets, and a 3D map of anatomical variability estimates is produced. The automatic variability estimates correlate well (r =0.867, p = 0.01) with manual estimates of inter-subject variability.