This paper describes a Speaker Verification System based on the use of multi resolution classifiers in order to cope with performance degradation due to natural variations of the excitation source and of the vocal tract. The different resolution representations of the speaker are obtained by considering multiple frame lengths in the feature extraction process and from these representations a single Pseudo-Multi Parallel Branch (P-MPB) Hidden Markov Model is obtained. In the verification process, different resolution representations of the speech signal are classified by multiple P-MPB systems: the final decision is obtained by means of different combination techniques. The system based on the Weighted Majority Vote technique considerably outperforms baseline systems: improvements are between 15%and 38%. The execution time of the verification process is also evaluated and it proves to be very acceptable, thus allowing the use of the approach for applications in real time systems.

This paper presents a new class of monotone functions that can be computed from the Residue Number System (RNS) to the integers. On the basis of these functions new implementations are proposed for residue-to-binary conversion and magnitude comparison that are superior to traditional techniques, if a modulus of the kind 2k (k integer) is included in the set of RNS moduli.

A new electronic diagnosis device for neuromuscular disease investigation, based on handwritten text analysis, is presented in this paper. More specifically, a new non-invasive tool for the diagnosis of a degenerative disability is here proposed. It is useful to follow the progress of a neuromuscular disease, not only to evaluate it in the early stages, but also to follow the changes related to therapy effectiveness. In fact, by tracking the disease a twofold benefit can be achieved: first for patients from the clinical point of view and second from the pharmaceutical point of view, by monitoring the effectiveness of the treatment. Specifically, in this paper the measure of local stability in handwritten words, by using the Pearson coefficient, is adopted, in order to detect stability in the handwriting process. The experimental results of a comparison between healthy people and patients affected by Alzheimer’s disease are reported in the paper.

Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.

Traffic light detection is an important matter in urban environments during the transition to fully autonomous driving. Many literature has been generated in the recent years approaching different pattern recognition strategies. In this paper we present a survey summarizing relevant works in the field of detection of both suspended and supported traffic light. This survey organizes different methods highlighting main reasearch areas in the computer vision field. © Springer International Publishing Switzerland 2015.

Training a system for pattern recognition is a task that require a large amount of labeled data. However, the creation of such training set is often difficult, expensive and time consuming because it requires the efforts of experienced human annotators. On the other hand, unlabeled data may be relatively easy to collect, but there are few ways to use them. Semi-Supervised learning is a useful approach to reduce human labor and improve accuracy using unlabeled data, together with labeled data. This paper proposes three methods in order to re-train classifiers in a multi-expert scenario, when new (unknown) data are available. In fact, when a multi-expert system is adopted, the collective behavior of classifiers can be used both for recognition aims and also selection of the most profitable samples for system re-train. More specifically a misclassified sample for a particular expert can be used to update the expert itself if the collective behavior of the multi-expert system allows to classify the sample with high confidence. In addition, this paper provides a comparison between the new approach and those available in literature for semi-supervised learning using the SVM classifier by taking into account four different combination techniques at abstract and measurement level. The experimental results, that have been obtained using the handwritten digits of the CEDAR database, demonstrate the effectiveness of the proposed approach.

This paper introduces a new score normalization technique - based on Dynamic Time Warping (DTW) - for output integration in multi-classifier systems. More precisely, DTW is used to match the score cumulative distribution of each individual classifier against a standard cumulative distribution. The warping function allows optimal alignment of the scores provided by the individual classifiers with the scores on the standard cumulative distribution. Furthermore, in order to adapt the normalization process to the behaviour of the individual classifiers and to the decision fusion rule, a new class of fuzzy cumulative distributions is introduced and a genetic approach is used to select the optimal distribution to be used as standard cumulative distribution for score normalization. The experimental tests report better results for the fuzzy normalization technique than for those obtained with other approaches present in the literature.

— In the field of hand-written character recognition, image zoning is a widespread technique for feature extraction since it is rightly considered able to cope with hand-written pattern variability. As a matter of fact, the problem of zoning design has attracted many researchers that have proposed several image zoning topologies, according to static and dynamic strategies. Unfortunately, little attention has been paid so far to the role of feature-zone membership functions, that define the way in which a feature influences different zones of the zoning method. The results is that the membership functions defined to date follow non-adaptive, global approaches that are unable to model local information on feature distributions. In this paper, a new class of zone-based membership functions with adaptive capabilities is introduced and its effectiveness is shown. The basic idea is to select, for each zone of the zoning method, the membership function best suited to exploit the characteristics of the feature distribution of that zone. In addition, a genetic algorithm is proposed to determine – in a unique process - the most favorable membership functions along with the optimal zoning topology, described by Voronoi tessellation. The experimental tests show the superiority of the new technique with respect to traditional zoning methods.

This paper reports some recent advancements in the field of use of dynamic programming both in handwriting recognition, and signature verification, carried out in the last years at the University of Bari. The first part of the paper describes the main advancements in the field of zoning, that is an one of the most effective techniques for feature extraction, and reports the most recent approaches for zoning design, based on genetic algorithms, well-suited zoning representation techniques and membership functions. In the second part of the paper, the problem of static signature verification is addressed and some new advancements based on stability analysis in static signatures are presented.

Questo articolo presenta Co.S.M.O.S. (Collaborative Social and Multimedia Operating System), un prototipo di ambiente virtuale di supporto ad attività collaborative real-time sviluppato con la finalità di venire incontro alle crescenti esigenze degli utenti in termini di Gestione della Conoscenza, Collaborazione e Socializzazione. Più precisamente Co.S.M.O.S. è un ambiente integrato, costituito da un WebOS, da un ambiente per la realizzazione di attività di collaborazione e da un social network, in grado supportare efficacemente collaborazioni real-time e quindi di favorire lo sviluppo di social communities attraverso funzioni avanzate di condivisione e cooperazione nei processi di Knowledge Management. Inoltre Co.S.M.O.S. è facilmente accessibile attraverso una applet Java che, incorporata in una pagina web, offre agli utenti la possibilità di utilizzare le diverse funzionalità fornite.

This paper presents a new technique for the analysis of stability in static signature images. The technique uses an equimass segmentation approach to non-uniformly split signatures into a standard number of regions. Successively, a multiple matching technique is adopted to estimate stability of each region, based on cosine similarity. The GPDS database has been considered for the experimental test. The results demonstrate the validity of the novel approach and highlight some directions for further research.

The evaluation of combination methods for multi-classifier systems is a difficult problem. In many cases multi-classifier combination methods are too complex to be formally studied and the experimental approach is the unique possible strategy. Of course, in order to simulate a multitude of real working conditions, sets of artificial classifiers with diverse characteristics must be generated. This paper presents an effective technique for generating sets of artificial classifiers with different characteristics both at the individual-level (i.e. recognition performance) and at the collective-level (i.e. degree of similarity). In the experimental tests, sets of artificial classifiers simulating different working conditions are generated and the performances of abstract-level combination methods are estimated. The results points out the effectiveness of the new technique for generating sets of artificial classifiers with different characteristics and their usefulness in estimating the performances of combination methods.

Three different strategies in order to re-train classifiers, when new labeled data become available, are presented in a multi-expert scenario. The first method is the use of the entire new dataset. The second one is related to the consideration that each single classifier is able to select new samples starting from those on which it performs a miss- classification. Finally, by inspecting the multi expert system behavior, a sample misclassified by an expert, is used to update that classifier only if it produces a miss-classification by the ensemble of classifiers. This paper provides a comparison of three approaches under different conditions on two state of the art classifiers (SVM and Naive Bayes) by taking into account four different combination techniques. Experiments have been performed by considering the CEDAR (handwritten digit) database. It is shown how results depend by the amount of the new training samples, as well as by the specific combination decision schema and by classifiers in the ensemble.

The stability of handwritten signatures is a crucial characteristic for both investigating the nature of the signature apposition process and improving systems for automatic signature verification. In this paper, a new technique for the analysis of stability in static signature images is discussed. The technique adopts a feature-based strategy to derive regional information from a static signature image and uses cosine similarity to estimate the degree of regional stability among genuine signatures, according to a multiple matching strategy. The experimental test carried out using signatures in the GPDS database has demonstrated the validity of this novel approach in obtaining stability information and deriving significant signer-independent and signer-dependent properties of the signing process, useful for verification aims.

A new layout-based document image retrieval system is presented in this paper. The system is specifically designed for commercial form retrieval and uses mathematical morphology to extract structural components from the document image. Document layout description is performed by the Radon Transform whereas Dynamic Time Warping is used for matching. The experimental results have been carried out on both real and simulated data sets. They demonstrate the effectiveness of the proposed approach and their robustness with respect to different classes of commercial forms and shifted/rotated document images.

At current time, a revolution has started. A revolution called Big Data. This revolution is not limited to volume of data: this revolution requires us to change everything about technology those we daily use. It is necessary to change the architecture of modern data centres, the programming technologies available, and the way we see (and use) sensors and other instruments, and finally, requires changing the application of Computer Science and Engineering to these data. The aim of this work is to how this change is big and how important it is. Moreover, perspectives, challenges and future work directions will be examined.

In zoning-based classification, a membership function defines the way a feature influences the different zones of the zoning method. This paper presents a new class of membership functions, named Fuzzy Membership Functions (FMFs), for zoning-based classification. These FMFs can be easily adapted to the specific characteristics of a classification problem in order to maximize classification performance. In this study, a real-coded genetic algorithm is presented to find, in a single optimization procedure, the optimal FMF together with the optimal zoning described by Voronoi Tessellation. The experimental results, carried out in the field of handwritten digit and character recognition, indicate that optimal FMF performs better than other membership functions based on abstract-level, ranked-level and measurement-level weighting models, which can be found in the literature.

This paper addresses the problem of multiclassifier system evaluation by artificially generated classifiers. For the purpose, a new technique is presented for the generation of sets of artificial abstract-level classifiers with different characteristics at the individual-level (i.e. recognition performance) and at the collective-level (i.e. degree of similarity). The technique has been used to generate sets of classifiers simulating different working conditions in which the performance of combination methods can be estimated. The experimental tests demonstrate the effectiveness of the approach in generating simulated data useful to investigate the performance of combination methods for abstract-level classifiers.

Based on neuromuscular transfer function of the handwriting system, in this paper a non invasive pre diagnosis system for Alzheimer disease alert is proposed. It is well known in fact, that writing originates from spike trains produced within the Central Nervous System (CNS) and more specifically, inside the 4-th and the 6-th regions of the Bradman's map and then transmitted through the first and second order axons to the spinal cord to control the muscles involved in the handwriting as the arm, the forearm, the hand and the pen or pencil utilized for the writing. More specifically, in this work is proposed a new method, not invasive, for early diagnosis of degenerative disability, it can be also useful for monitoring activities related to the progression of neuromuscular disease in order to evaluate the changing related also to the efficiency of the therapies used. Benefit can be obtained not only for the medical field but also for the pharmaceutical developments. Specifically in the paper, the results of some experiments have been focused by considering a certain number of persons some of which affect by Alzheimer disease.

Recently, research in handwritten signature verification has been considered with renewed interest. In fact, in the age of e-society, handwritten signature still represents an extraordinary means for personal verification and the possibility of using automatic signature verification in a range of applications is becoming a reality. This paper focuses on some of the most remarkable aspects the field and highlights some recent research directions. A list of selected publications is also provided for interested researchers.

In real world applications, signature verification systems should be able to learn continuously, as new signatures providing additional information become available. In fact, new data are not equally relevant for system improvement and a suitable data filtering strategy is generally required. In this context, instance selection is an important task for signature verification systems in order to select useful signatures to be considered for updating system knowledge, removing irrelevant and/or redundant instances from new data. This paper proposes a new feedback-based learning strategy to update the knowledge-base in multi-expert signature verification system. In particular, the collective behavior of classifiers is considered to select the samples for updating system knowledge. Evaluation tests provide a comparison between our (not naïve) approach and the traditional approach, which uses the entire new dataset for feedback. For the purpose, two state-of-the-art classifiers (NB and k-NN) and two abstract level combination techniques (MV and WMV) were used. The experimental results, carried out considering the SUSig database, demonstrate the effectiveness of the new strategy.

This paper presents Co.S.M.O.S. (Social and Collaborative Multimedia Operating System), a prototype of a virtual environment to support real-time collaborative activities developed with the aim to meet the growing needs of users in terms of Knowledge Management, Collaboration, and Socialization. More precisely Co.S.M.O.S. is an integrated environment consisting of a WebOS, an environment for the realization of collaborative activities and a social network which can efficiently support real-time collaboration and thus facilitate the development of social communities through advanced sharing and cooperation in the process of Knowledge Management. Co.S.M.O.S. is easily accessible via a Java applet, embedded in a web page, offering users all its features.

In the context of sustainability of document management technologies, this paper presents a new system for layout-based document retrieval specifically designed for commercial form retrieval. The system first uses a technique based on mathematical morphology to extract grid-based structural components from the document image. Successively, Radon Transform is used for document layout description. A document matching technique based on dynamic time warping is finally adopted. The experimental results carried out on real and simulated data set, demonstrate the effectiveness of the approach with respect to different classes of commercial forms.

This paper presents three different learning iterative strategies, in a multi-expert system. In first strategy entire new dataset is used. In second strategy each single classifier selects new samples starting from those on which it performs a misclassification. Finally, the collective behavior of classifiers is studied to select the most profitable samples for knowledge base updating. The experimental results provide a comparison of three approaches under different operating conditions and feedback process. A classifier SVM and four different combination techniques were used by considering the CEDAR (handwritten digit) database. It is shown how results depend by the iterations on the feedback process, as well as by the specific combination decision schema and by data distribution.

Updating of reference information is a crucial task for automatic signature verification. In fact, signature characteristics vary in time and whatever approach is considered the effectiveness of a signature verification system strongly depends on the extent to which reference information is able to model the changeable characteristics of users’ signatures. This paper addresses the problem of knowledge-base updating in multi-expert signature verification sys-tems and introduces a new strategy which exploits the collective behavior of classifiers to select the most profitable samples for knowledge-base updating. The experimental tests, carried out using the SUSig database, demonstrate the effectiveness of the new strategy.

Classifier combination is a powerful paradigm to deal with difficult pattern classification problems. As matter of this fact, multi-classifier systems have been widely adopted in many applications for which very high classification performance is necessary. Notwithstanding, multi-classifier system design is still an open problem. In fact, complexity of multi-classifiers systems make the theoretical evaluation of system performance very difficult and, consequently, also the design of a multi-classifier system. This paper presents a new approach for the design of a multi-classifier system. In particular, the problem of feature selection for a multiclassifier system is addressed and a genetic algorithm is proposed for automatic selecting the optimal set of features for each individual classifier of the multi-classifier system. The experimental results, carried out in the field of handwritten digit recognition, demonstrate the effectiveness of the proposed approach.

In handwritten character recognition, zoning is one of the most effective approaches for features extraction. When a zoning method is considered, the pattern image is subdivided into zones each one providing regional information related to a specific part of the pattern. The design of a zoning method concerns the definition of zoning topology and membership function. Both aspects have been recently investigated and new solutions have been proposed, able to increase adaptability of the zoning method to different application requirements. In this paper some of the most recent results in the field of zoning method design are presented and some valuable directions of research are highlighted.

This paper presents a static signature verification system based on a region-oriented strategy. For this purpose, a suitable technique is proposed for the analysis of local stability in static signature and stable regions are selected during the enrollment phase. In the running phase, an unknown specimen is verified through the analysis of the selected regions, on the basis of a well-defined similarity measure. The experimental results, carried out on signatures from the GPDS database, demonstrate the proposed approach.

In this paper a new on-line signature verification technique is proposed. Differently from previous works, this approach classifies a signature using a multi-domain strategy. In particular, based on the stability model of each signer, the signature is splitted into different segments and for each segment the most profitable domain of representation for verification purpose is detected. In the verification stage, Dynamic Time Warping (DTW) is used to evaluate the genuinity of each segment of the unknown signature, using the specific domain of representation. The experimental results, carried out on signatures of the SUSIG database, demonstrate the effectiveness of the proposed approach when compared to other approaches in literature.

In this paper a new system for dynamic signature verification is presented. It is based on the consideration that each region of an handwritten signature can convey personal characteristics in diverse domains. Therefore, a multi-expert approach is considered in which each stroke of the signature is evaluated in the most profitable domain of representation. The experimental results demonstrate the effectiveness of the proposed approach.

In recent years, biometric-based authentication systems have been widely used in many applications which require reliable identification scheme. Among others, handwritten signature is one of the most interesting biometric means, that is being considered with renewed interest. This paper presents some of the most relevant advances in the field of offline signature identification and highlights some directions for further research.

Multi-expert approach is a well-known paradigm to support complex decisions and several decision fusion techniques have been considered. In this field score normalization is a fundamental step to allow high performances and hence several techniques have been proposed so far. This paper addresses the problem of score normalization and presents a new technique based on Dynamic Time Warping. The experimental results, carried out in the field of pattern classification, show the superiority of the new technique with respect to other approaches in the literature, based on MIN-MAX, z-score and characteristics functions.

Questo articolo presenta il sistema SignVerify per l'analisi di firme manoscritte a supporto dell'attività forense. Il sistema infatti ha la finalità di estrarre da immagini di firme manoscritte, autentiche e/o contraffatte, caratteristiche utili per supportare l'esperto forense a giudicare una firma di test. Le caratteristiche estratte, anche se derivate da campioni di firme statiche, sono principalmente riferite ad informazioni legate al processo dinamico di apposizione delle firme. I risultati sperimentali ottenuti da immagini di firme manoscritte del CEDAR database, confermano la validità dell'approccio proposto e la sua utilità per finalità di analisi forense.

This paper presents a new approach for the analysis of local stability in online signature. Conversely to previous approaches in the literature, the analysis of stability is here performed by considering the characteristics of the processes underlying signature generation. For this purpose, the Sigma-Lognormal model developed in the context of the kinematic theory of rapid human movements is considered. It allows the representation of the information of the neuromuscular system involved in the production of complex movements like signatures. The experimental results were obtained using the SuSig database. They demonstrate that the new approach provides useful information on stability of online signatures and allows to better understand some characteristics of human behavior in signing.

The objective of this paper is twofold. First, it presents an experimental investigation on stability of dynamic signatures. For the purpose, a well-suited index for estimating local stability is considered, based on a multiple matching strategy performed by Dynamic Time Warping. Second, the stability index is used to estimate local stability of dynamic signatures in different representation domains and to select the most profitable domain for automatic signature verification. The experimental results demonstrate the effectiveness of the new approach in estimating personal stability is signing and its utility in selecting the most profitable representation domain for signature verification.

This paper presents a static signature verification system based on the concept of local stability. Stable regions are detected in the signatures, during the enrolling phase, and are considered to be those regions affected by low variations of features among the training set. The stability evaluation is based on the Hamming distance. Stable regions are successively used for verification, in the running phase. A region-oriented verification strategy is considered, based on a well-defined similarity measure which takes into account the variability in signing of the writer. The experimental results, carried out on signatures from the GPDS database, demonstrate the viability of proposed approach.

Stability analysis of handwritten signatures is very relevant for automatic signature verification. In this paper the analysis of stability is performed by considering the characteristics of the processes underlying signature generation. More precisely, the analysis of stability is performed by considering the Sigma-Lognormal parameters, according to the Kinematic Theory. The experimental tests, carried out using the SUSig database, demonstrate that the new technique can provide useful information both for a deep understanding of the processes of signature generation and for the improvement of the processes for automatic signature verification.

This paper presents a new approach for static signature verification based on optical flow. In the first part of the paper, optical flow is used for estimating local stability of static signatures. In the second part, signature verification is performed by the analysis of optical flow, using an alternating decision tree. The experimental tests, carried out on signature of the GPDS database, demonstrate the validity of this approach and highlight some direction for further research.

This paper presents three strategies in order to re-train classifiers in a multi-expert scenario when new labeled data become available. The simplest possibility is the use of the entire new dataset. The second possibility is related to the consideration that each single classifier is able to select new patterns starting from those on which it performs a miss-classification. Finally, the multi expert system behavior can be inspected to select profitable samples. More specifically a misclassified sample, for a particular classifier, is used to update that classifier only if it produces a miss-classification by the ensemble of classifiers. The three approaches are compared under different conditions on two different state of the art performing classifiers by considering the CEDAR (handwritten digit) database. It is shown how results depend by the amount of the new training samples, as well as by the specific combination decision schema.

In this paper the Similarity Index variability range is investigated. Depending on the recognition rates of abstract-level classifiers, the lower and upper bounds of the of the Similarity Index variability range is theoretically analysed. The experimental tests, carried out in the field of handwritten numeral classification, confirm the theoretical findings.

A new approach for static signature verification is presented in this paper. The approach uses optical flow to estimate local stability among signatures. In the enrollment stage, optical flow is used to define a stability model of the genuine signatures for each signer. In the verification stage, the stability between the unknown signature and each one of the reference signatures is estimated and consistency with the stability model of the signer is evaluated. The experimental results, carried out on the signatures in the GPDS database, demonstrate the effectiveness of the new approach.

Lo sviluppo e la diffusione dell’e-learning sta imponendo una sempre maggiore attenzione verso la definizione di strategie di valutazione in grado di verificare la rispondenza delle azioni formative sia alle necessità educative che alle esigenze organizzative, normative ed istituzionali. Questo lavoro presenta una semplice ed efficace strategia di tipo “participant-oriented” per la valutazione delle azioni di e-learning. L’applicazione di tale strategia ad attività in e-learning svolte nell’Università degli Studi di Bari ha permesso di evidenziarne la potenzialità e l’utilità.

Questo articolo presenta uno strumento di analisi del manoscritto per lo studio di patologie degenerative neuromuscolari. In particolare si considerano i modelli Delta-Lognormale e Sigma-Lognormale per investigare i processi di generazione della scrittura da parte del sistema neuromuscolare. Successivamente viene presentato un sistema software di analisi della scrittura a mano libera e vengono illustrate alcune sue potenzialità per investigare l’insorgenza di malattie neuromuscolari e per il loro monitoraggio. I risultati sperimentali mostrano la validità dell’approccio proposto e evidenziano alcune promettenti direzioni di ricerca.

In pattern recognition tasks it is frequent that new (labeled) data became available as the specific application scenario evolves. When a multi expert system (E) is adopted, the collective behavior of classifiers can be used to select the most profitable samples in order to update the knowledge base of each individual classifier. More specifically a misclassified sample, for a particular classifier, is used to update that classifier only if that sample produces a misclassification by the ensemble of classifiers. This approach is compared to situation in which the entire new dataset is used for learning as well as the case in which specific samples are selected by the individual classifier. Successful results have been obtained by considering the CEDAR (handwritten digit) database, moreover it is also shown how they depend by the specific combination decision schema, as well as by data distribution.

'Puglia Tremor' and 'Vengo Anch'io' projects built two systems for monitoring geo-referenced events. Reports of events are principally sent by human users that became active citizens in real community. 'Puglia Tremor' and 'Vengo Anch'io' use new dynamics of co-partnership, introduced by new technologies and telecommunication tools. The main idea behind the proposal is that each user of a social network can act as a signalling device/human sensor. So, a decision support system (DSS) can define an assessment level based on information received from users. Through this analysis, the system will be able to identify a multi-level classification of assessment and it will define a visual metaphor which acts as a support for the operator. Finally, this latter will can choose priority and type of surgery based on severity of real event. Copyright © 2015 Inderscience Enterprises Ltd.

The aim of this paper is to explore the properties of a new zoning technique based on Voronoi tessellation for the task of handwritten digit recognition. This technique extracts features according to an optimal zoning distribution, obtained by an evolutionary-strategy based search. Extensive experiments have been conducted on the MNIST dataset to investigate strengths and weakness of the proposed approach. Comparisons with regular square zoning reveal that the presented zoning strategy achieves better results with any type of features. Furthermore, the proposed zoning method, jointly with a suitable choice of features, allows a low complexity classifier to reach excellent performances both in terms of accuracy and speed.

This paper presents a new approach to optimal zoning design. The approach uses a multi-objective genetic algorithm to define, in a unique process, the optimal number of zones of the zoning method along with the optimal zones,defined through Voronoi diagrams. The experimental tests, carried out in the field of handwritten digit recognition, show the superiority of new approach with respect to traditional dynamic approaches for zoning design, based on single objective optimization techniques.

In this paper the use of handwriting artifacts for health investigation is addressed. For the purpose, the paper first presents the Delta-Log and Sigma-Lognormal models to investigate on the handwriting generation processes carried out by the neuromuscular system. Successively, a computational system for handwriting analysis is presented and some considerations are exploited about the use of the model to investigate insurgence and monitoring of some neuromuscular diseases. The experimental results show the validity of the proposed approach and highlight some directions for further research.

In this paper the use of handwriting artifacts for health investigation is addressed. For the purpose, the paper first presents the Delta-Log and Sigma-Lognormal models to investigate on the handwriting generation processes carried out by the neuromuscular system. Successively, a computational system for handwriting analysis is presented and some considerations are exploited about the use of the model to investigate insurgence and monitoring of some neuromuscular diseases. The experimental results show the validity of the proposed approach and highlight some directions for further research.

This paper presents a survey on zoning methods for handwritten character recognition. Through the analysis of the relevant literature in the field, the most valuable zoning methods are presented in terms of both topologies and membership functions. Throughout the paper, diverse zoning topologies are presented based on both static and adaptive approaches. Concerning static approaches, uniform and non-uniform zoning strategies are discussed. When adaptive zonings are considered, manual and automatic strategies for optimal zoning design are illustrated as well as the most appropriate zoning representation techniques. In addition, the role of membership functions for zoning-based classification is highlighted and the diverse approaches to membership function selection are presented. Concerning global membership functions, the paper introduces order-based approaches as well as fuzzy approaches using border-based and ranked-based fuzzy membership values. Concerning local membership functions, the recent parameter-based approaches are described, in which the optimal membership-function is selected for each zone of the zoning method. Finally, a comparative analysis on the performance of zoning methods is presented and the most interesting approaches are focused on in terms of topology design and membership function selection. A list of selected references is provided as a useful tool for interested researchers working in the field.