Andrzej Zolnierek

Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013

The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. This book is the most comprehensive study of this field. It contains a collection of 86 carefully selected articles contributed by experts of pattern recognition. It reports on current research with respect to both methodology and applications. In particular, it includes the following sections:Biometrics Data Stream Classification and Big Data AnalyticsFeatures, learning, and classifiers Image processing and computer vision Medical applications Miscellaneous applications Pattern recognition and image processing in roboticsSpeech and word recognitionThis book is a great reference tool for scientists who deal with the problems of designing computer pattern recognition systems. Its target readers can be the as well researchers as students of computer science, artificial intelligence or robotics.

Three-Stage Method of Text Region Extraction from Diagram Raster Images

In the paper the combined approach to the problem of text region recognition problem is presented. We focused our attention on the chosen case of text extraction problem from specific type of images where text is imposed over graphical layer of vector images (charts, diagrams, etc.). For such images we proposed three-stage method using OCR tools as some kind of feed-back in process of text region searching. Some experimental results and examples of practical applications of recognition method are also briefly described.

Sequential classification via fuzzy relations

In this paper there are developed and evaluated methods for performing sequential classification (SC) using fuzzy relations defined on product of class set and fuzzified feature space. First on the base of learning set, fuzzy relation in the proposed method is determined as a solution of appropriate optimization problem. Next, this relation in the form of matrix of membership degrees is used at successive instants of sequential decision process. Three various algorithms of SC which differ both in the sets of input data and procedure are described. Proposed algorithms were practically applied to the computer-aided recognition of patients acid-base equilibrium states where as an optimization procedure the real-coded genetic algorithm (RGA) was used.

The Empirical Study of the Naive Bayes Classifier in the Case of Markov Chain Recognition Task

In this paper the problems of sequential pattern recognition are considered. As a statistical model of dependence, in the sequences of patterns, the firstorder Markov chain is assumed. Additionally, the assumption about independence between the attributes in the feature vector is made. The pattern recognition algorithms with such assumption are called in the literature “naive Bayes algorithm”. In this paper such approach is made to the pattern recognition algorithm for first-order Markov chain and some results of numerical investigation are presented. The main goal of these investigations was to verify if it is reasonable to make such assumption in the real recognition tasks.

Sequential Pattern Recognition: Naive Bayes Versus Fuzzy Relation Method

In this paper two possibilities of taking into account the dependencies in the sequential pattern recognition task are considered. The first method is naive Bayes attempt adopted to the probabilistic model of sequential decision problem in which, the assumption of Markov dependence in the sequence of recognized patterns is made. The second one is the fuzzy relation approach, in which we omitted such not necessary correct assumptions. Furthermore, both methods were applied to the medical diagnostic task and the results of computer investigations are discussed

Application of rough sets theory to the sequential diagnosis

Sequential classification task is typical in medical diagnosis, when the investigations of the patients state are repeated several times. Such situation takes place in controlling of the drug therapy efficacy. In this paper the methods of sequential classification using rough sets theory are developed and evaluated. The proposed algorithms, using the set of learning sequences, calculate the lower and upper approximations of the set of proper decision formulas and then use them to make final decision. Depending on the input data different algorithms are derived. Next, all presented algorithms were practically applied in computer-aided recognition of the human acid-base state balance and the results of comparative experimental analysis of in respect of classification accuracy are also presented and discussed.

Computer-aided sequential diagnosis using fuzzy relations – comparative analysis of methods

A specific feature of the explored diagnosis task is the dependence between patients states at particular instants, which should be taken into account in sequential diagnosis algorithms. In this paper methods for performing sequential diagnosis using fuzzy relation in product of diagnoses set and fuzzified feature space are developed and evaluated. In the proposed method first on the base of learning set fuzzy relation is determined as a solution of appropriate optimization problem and next this relation in the form of matrix of membership grade values is used at successive instants of sequential diagnosis process. Different algorithms of sequential diagnosis which differ with as well the sets of input data as procedure are described. Proposed algorithms were practically applied to the computer-aided recognition of patients acid-base equilibrium states where as an optimization procedure genetic algorithm was used. Results of comparative experimental analysis of investigated algorithms in respect of classification accuracy are also presented and discussed.

Control of Bio-prosthetic Hand via Sequential Recognition of EMG Signals Using Rough Sets Theory

The paper presents a concept of bio-prosthesis control via recognition of user intent on the basis of miopotentials acquired of his body. We assume, that in the control process each prosthesis operation consists of specific sequence of elementary actions. The contextual (sequential) recognition is considered in which the rough sets approach is applied to the construction of classifying algorithm. Experimental investigations of the proposed algorithm for real data are performed and results are discussed.

Rough sets and fuzzy sets theory applied to the sequential medical diagnosis

Sequential classification task is typical in medical diagnosis, when the investigations of the patients state are repeated several times. Such situation always takes place in the controlling of the drug therapy efficacy. A specific feature of this diagnosis task is the dependence between patients states at particular instants, which should be taken into account in sequential diagnosis algorithms. In this paper methods for performing sequential diagnosis using fuzzy sets and rough sets theory are developed and evaluated. For both soft methodologies several algorithms are proposed which differ in kind of input data and in details of classification procedures for particular instants of decision process. Proposed algorithms were practically applied to the computer-aided medical problem of recognition of patients acid-base equilibrium states. Results of comparative experimental analysis of investigated algorithms in respect of classification accuracy are also presented and discussed.

Hybrid Approach Using Rough Sets and Fuzzy Logic to Pattern Recognition Task

In this paper a hybrid classifier construction using rough sets and fuzzy logic is presented. Nowadays, we tackle with many realistic multi-dimensional problems with continuous values and overlaps in the feature space which require sophisticated recognition algorithms. Many methods have been proposed in the literature to improve classification accuracy, but it is increasingly harder to build new classifier from the scratch. Instead, new fusion methods are proposed to overcome this problem. In our rough-fuzzy approach data pre-processing and crisp discretization have a significant impact on the final classification efficiency. To deal with the problem of finding the optimal cuts in the feature space a genetic algorithm was proposed. After the algorithm description, in this paper also simulation investigations using different datasets from UCI Machine Learning Repository are presented.