Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "fuzzy methods" wg kryterium: Wszystkie pola

  Lista filtrów
Wyrównaj
Wyświetlanie 1-15 z 82
Tytuł:
Artificial Intelligence Approaches to Fault Diagnosis for Dynamic Systems
Autorzy:
Patton, R. J.
Lopez-Toribio, C. J.
Uppal, F. J.
Powiązania:
https://bibliotekanauki.pl/articles/908290.pdf
Data publikacji:
1999
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
metoda sztucznej inteligencji
rozpoznanie błędu
modelowanie rozmyte
system rozmyty
artificial intelligence methods
fault diagnosis
residual generation
fuzzy modelling
neuro-fuzzy systems
Opis:
Recent approaches to fault detection and isolation (FDI) for dynamic systems using methods of integrating quantitative and qualitative model information, based upon artificial intelligence (AI) techniques are surveyed. In this study, the use of AI methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. When quantitative models are not readily available, a correctly trained artificial neural network (ANN) can be used as a non-linear dynamic model of the system. However, the neural network does not easily provide insight into model behaviour; the model is explicit rather than implicit in form. This main difficulty can be overcome using qualitative modelling or rule-based inference methods. For example, fuzzy logic can be used together with state-space models or neural networks to enhance FDI diagnostic reasoning capabilities. The paper discusses the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of real process systems.
Źródło:
International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 471-518
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural methods of knowledge extraction
Autorzy:
Duch, W.
Adamczak, R.
Grąbczewski, K.
Jankowski, N.
Powiązania:
https://bibliotekanauki.pl/articles/206250.pdf
Data publikacji:
2000
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
diagnostyka medyczna
optymalizacja
reguła logiczna
reguła rozmyta
wspomaganie decyzji
data mining
decision support
fuzzy rules
logical rules
medical diagnosis
optimization
Opis:
Contrary to the common opinion, neural networks may be used for knowledge extraction. Recently, a new methodology of logical rule extraction, optimization and application of rule-based systems has been described. C-MLP2LN algorithm, based on constrained multilayer perceptron network, is described here in details and the dynamics of a transition from neural to logical system illustrated. The algorithm handles real-valued features, determining appropriate linguistic variables or membership functions as a part of the rule extraction process. Initial rules are optimized by exploring the accuracy/simplicity tradeoff at the rule extraction stage and the one between reliability of rules and rejection rate at the optimization stage. Gaussian uncertainties of measurements are assumed during application of crisp logical rules, leading to "soft trapezoidal" membership functions and allowing to optimize the linguistic variables using gradient procedures. Comments are made on application of neural networks to knowledge discovery in the benchmark and real life problems.
Źródło:
Control and Cybernetics; 2000, 29, 4; 997-1017
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Soft methods in statistical quality control
Autorzy:
Grzegorzewski, P.
Hryniewicz, O.
Powiązania:
https://bibliotekanauki.pl/articles/206289.pdf
Data publikacji:
2000
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
statystyczna kontrola jakości
zbiór rozmyty
control charts
fuzzy sets
sampling plans
vague data
Opis:
The paper is devoted to soft methods in statistical quality control. A review of existing tools for dealing with vague data or fuzzy requirements is given. Some new procedures are also proposed.
Źródło:
Control and Cybernetics; 2000, 29, 1; 119-140
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An ε-Insensitive Approach to Fuzzy Clustering
Autorzy:
Łęski, J.
Powiązania:
https://bibliotekanauki.pl/articles/908067.pdf
Data publikacji:
2001
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
programowanie
metoda grupowania
fuzzy clustering
fuzzy c-means
robust methods
varepsilon-insensitivity
fuzzy c-medians
Opis:
Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new varepsilon-insensitive Fuzzy C-Means (varepsilonFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2001, 11, 4; 993-1007
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Improving the Generalization Ability of Neuro-Fuzzy Systems by e-Insensitive Learning
Autorzy:
Łęski, J.
Powiązania:
https://bibliotekanauki.pl/articles/908037.pdf
Data publikacji:
2002
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
informatyka
fuzzy systems
neural networks
tolerant learning
generalization control
robust methods
Opis:
A new learning method tolerant of imprecision is introduced and used in neuro-fuzzy modelling. The proposed method makes it possible to dispose of an intrinsic inconsistency of neuro-fuzzy modelling, where zero-tolerance learning is used to obtain a fuzzy model tolerant of imprecision. This new method can be called e-insensitive learning, where, in order to fit the fuzzy model to real data, the e-insensitive loss function is used. e-insensitive learning leads to a model with minimal Vapnik-Chervonenkis dimension, which results in an improved generalization ability of this system. Another advantage of the proposed method is its robustness against outliers. This paper introduces two approaches to solving e-insensitive learning problem. The first approach leads to a quadratic programming problem with bound constraints and one linear equality constraint. The second approach leads to a problem of solving a system of linear inequalities. Two computationally efficient numerical methods for e-insensitive learning are proposed. Finally, examples are given to demonstrate the validity of the introduced methods.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2002, 12, 3; 437-447
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Segmentation and visualisation MR images of the human brain
Autorzy:
Denkowski, M.
Chlebiej, M.
Mikołajczak, P.
Powiązania:
https://bibliotekanauki.pl/articles/333556.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
metody segmentacji
wizualizacja
przetwarzanie obrazu rezonansu magnetycznego mózgu
logika rozmyta
powierzchnia
interpretacja objętościowa
segmentation methods
visualization
MR brain image processing
thresholding
fuzzy logic
surface
volumetric rendering
Opis:
Segmentation and visualisation of anatomical regions of the brain are fundamental problems in medical image analysis. In this paper, we present a fuzzy-logic segmentation system that is capable of segmenting magnetic resonance (MR) images of a human brain. The presented method consists of two main stages: histogram thresholding and pixel classification using a rule-based fuzzy logic inference. After the segmentation is complete, attributes of different tissue classes may be determined (e.g., volumes), or the classes may be visualised as spatial objects. The implemented system provides many advanced 3D imaging tools.
Źródło:
Journal of Medical Informatics & Technologies; 2004, 7; MIP59-68
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Registration and fuzzy segmentation modules for SemiVis framework
Autorzy:
Denkowski, M.
Chlebiej, M.
Mikołajczak, P.
Powiązania:
https://bibliotekanauki.pl/articles/333861.pdf
Data publikacji:
2005
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
struktura przetwarzania obrazów
rejestracja
rozmyta metoda segmentacji
wizualizacja
przetwarzanie obrazu rezonansu magnetycznego mózgu
image processing framework
registration
fuzzy segmentation methods
visualization
MR brain image processing
Opis:
This paper presents the cross-platform framework for image processing with a focus on medical imaging. It allows a fast addition and testing of new algorithms using a modular structure. New modules can be created by using a platform-independent The C++ class library can be easily integrated with a whole system by a plug-in mechanisms. Together with the system core in the framework medical image processing modules are included. The plug-in mechanism allows to create a processing pipelines of this modules to achieve sophisticated processing functions such as registration or segmentation.
Źródło:
Journal of Medical Informatics & Technologies; 2005, 9; 65-74
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wykorzystanie technik modelowania w metodyce diagnozowania ergonomicznego układu operator-maszyna-otoczenie
Application of modelling techniques in ergonomic diagnostics methods of the operator-machine-material environment system
Autorzy:
Grabarek, I.
Powiązania:
https://bibliotekanauki.pl/articles/291104.pdf
Data publikacji:
2005
Wydawca:
Polskie Towarzystwo Inżynierii Rolniczej
Tematy:
diagnoza ergonomiczna
układ operator-pojazd-otoczenie
modelowanie
zbiory rozmyte
ergonomic diagnosis
operator-vehicle-environment system
modelling
fuzzy sets
Opis:
Przedstawiono problemy diagnozowania ergonomicznego układu operator–pojazd–otoczenie. Podstawą sformułowanych założeń metodycznych procedur diagnozowania ergonomicznego były badania własne i przegląd literatury, w wyniku czego opracowano dwie autorskie metody oceny: wagową, wagowo-rozmytą. Określają one globalnym współczynnikiem diagnozy poziom ergonomicznej jakości układu. Podstawą proponowanych metod są badania ankietowo-ekspertowe, w wyniku których uzyskuje się cechy priorytetowe i ich wagi, charakteryzujące czynniki: ludzki, konstrukcyjno-techniczny i materialnego środowiska pracy oraz cząstkowe wskaźniki poziomu ergonomicznej jakości. Warunkiem zastosowania metody wagowo-rozmytej było opracowanie lingwistycznego modelu heurystycznego oceny (z wykorzystaniem implikacji w dziedzinie zbiorów rozmytych). Metodę wagową i wagowo-rozmytą zweryfikowano na przykładzie stanowisk pracy w lokomotywach elektrycznych, tworząc zbiór cech priorytetowych i ich wag dla badanej grupy pojazdów.
This papers concerns the problems of ergonomic diagnosis in operator-vehicle-environment systems. The author’s own research and solutions derived from the literature of the subject served as a basis for formulating the methodical assumptions informing on ergonomic diagnosis procedures. Concerning the procedures author develops two evaluation methods of her own, referred to respectively as the weighted, weighted-fuzzy. These are designed to asses a system’s ergonomic level using global diagnosis coefficients. In these two methods, questionnaire/expert based surveys are used to determine the priority features (and their weights) which characterize the human factor, the design/technological factor and the material work environment factor as well as individual ergonomic quality components. To use the weighted-fuzzy method, it was necessary to develop a linguistic heuristic evaluation model (using fuzzy set implications). The weighted and weighted-fuzzy methods were verified using the example of electric locomotives as a set of priority features was created and feature weights were assigned for the studied group of vehicles.
Źródło:
Inżynieria Rolnicza; 2005, R. 9, nr 8, 8; 71-79
1429-7264
Pojawia się w:
Inżynieria Rolnicza
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Decision support based methods to facilitate 3D volumetric locking in a new peer to peer based spatial database system
Autorzy:
Vert, G.
Morris, A.
Heaton, J. S.
Powiązania:
https://bibliotekanauki.pl/articles/970482.pdf
Data publikacji:
2006
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
baza danych
logika rozmyta
peer-to-peer
database
fuzzy logic
Opis:
Spatial databases present challenges to data management not found in typical RDBMS. Among these is the desire of users for highly distributed access to data. A new peer to peer database model is being developed that supports high distribution and concurrency of information access. Some aspects of the model are presented in this paper. However the new peer to peer model faces the critical problem of how to lock data as it becomes highly distributed. Conceptual methods borrowed from decision support and data mining are proposed to create a new type of locking model based on 3D volumetrics. The methods and mathematics proposed in this paper can become the basis for the implementation of a new type of peer to peer based spatial database system where spatial information flows to where it is needed on demand.
Źródło:
Control and Cybernetics; 2006, 35, 1; 165-194
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Metody sztucznej inteligencji w zastosowaniach automatyki
Methods of artificial intelligence in applications of automatic control
Autorzy:
Rojek, R.
Bartecki, K.
Powiązania:
https://bibliotekanauki.pl/articles/153320.pdf
Data publikacji:
2006
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
sieci neuronowe
logika rozmyta
identyfikacja
sterowanie automatyczne
neural networks
fuzzy logic
identification
automatic control
Opis:
W artykule przedstawiono wybrane aspekty zastosowania metod sztucznej inteligencji w zagadnieniach automatyki. Zawarto ogólny przegląd metod identyfikacji i sterowania opartych na sztucznych sieciach neuronowych. Wskazano także na możliwość wykorzystania logiki rozmytej do optymalizacji pracy samochodowego układu napędowego.
In this paper, some issues concerning application of neural networks and fuzzy logic in automatic control are presented. Neural identification and control methods are briefly reviewed. Fuzzy logic approach to powertrain control of a passenger car is also shown.
Źródło:
Pomiary Automatyka Kontrola; 2006, R. 52, nr 10, 10; 29-34
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie metod fuzji danych w zarządzaniu zasobami radaru wielofunkcyjnego
The Application of the Data Fusion Methods in the Multifunction Radar Resources Management
Autorzy:
Komorniczak, W.
Kawalec, A.
Pietrasiński, J.
Powiązania:
https://bibliotekanauki.pl/articles/210695.pdf
Data publikacji:
2006
Wydawca:
Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
Tematy:
radar wielofunkcyjny
fuzja danych
sieci neuronowe
systemy rozmyte
multifunction radar
data fusion
neural networks
fuzzy logic
Opis:
W referacie poruszono tematykę związaną z zarządzaniem zasobami radaru wielofunkcyjnego. Jako jeden z elementów tego procesu wyróżniono priorytetyzację (rangowanie) zadań realizowanych przez radar. Rangowanie jest wymuszone przez potencjalnie niedostateczne zasoby wymagane do realizacji wszystkich zadań radaru, stąd konieczność szeregowania obsługiwanych przezeń obiektów zgodnie z ich istotnością. W referacie scharakteryzowano dane źródłowe zasilające proces rangowania oraz przedstawiono algorytmy przetwarzania tych danych. Zaprezentowane algorytmy oparto na wybranych metodach fuzji danych. Przedstawiono przebieg i wyniki badań procesu rangowania oraz wyniki badań wpływu zastosowania rangowania na niektóre parametry zarządzania zasobami radaru wielofunkcyjnego.
The paper deals with the problem of the multifunction radar resources management (RRM). The objectives of RRM are: optimal (from the radar performance point of view) resources allocation and the device operation control. As a result of RRM, it is expected a matrix containing information for the execution systems: " what, when, and how to do. The main constraints to deal with in the radar work are: time and energy limitations. If it is enough resource to execute all the tasks, the tasks execution is feasible. But in real situation one should not expect such a comfort. Typically neither time nor energy is enough and the questions arises what to do in these circumstances. It is obvious that only selected tasks can be executed, the RRM should answer which of them and in what order. To answer these questions, the structure of the RRM was proposed. First of all it is necessary to rank the tasks in order of their priorities, then to select the most important of them and schedule their execution. RRM is decomposed into two sub-problems, e.g.: ranking and task scheduling. The ranking belongs to the identification problems class, while the scheduling can be treated as an optimization task. The paper presents the data fusion approach to the task ranking. There are numerous examples of utilization of the data fusion tools in order to solve the identification problems. The conclusions from these examples can be following: the neural networks which have the ability to learn from the presented examples have also disadvantage of impossibility of extraction of the gathered knowledge. The internal processes of reasoning are neither well described nor studied, so they are not a good tool for military application, which the multifunction radar is. Fuzzy logic systems (based on the fuzzy sets theory and fuzzy logic) have the advantage of good and clear knowledge representation and ability to relatively easy implementation of the expert knowledge. The good side of the fuzzy systems is their possibility of maintaining and fusion of the imperfect knowledge. The disadvantage is the lack of ability to learn whole the knowledge from the examples. Some hybrid solutions are necessary. Four solutions are presented in the paper: neural, fuzzy, fuzzy — neural and probabilistic — fuzzy. In order to implement data fusion tools, the base test platform was designed and implemented. In fact, the test platform is a complex process of multifunction radar resources management, as well as it deals with the task scheduling problem. In order to evaluate the algorithms presented in the paper, some factors of radar work performance were defined. Presented ranking algorithms have capability of learning with use of the registered data learning set. Algorithms with their knowledge bases were tested and compared. The conclusion is following: the use of ranking process gives approximately two times better performance in task removal/delay aspect. On the other hand, the quality of algorithm (its accuracy) has lower influence on the final result. It means that for the use in radar application the algorithm with the best convergence during learning process and stability should be recommended. It is also important that the algorithm should have clear knowledge representation. These requirements meet two of the presented algorithms: neural - fuzzy and probabilistic - fuzzy. The first one was used against the positional data, the second one gave the best results for identification data. It is important, that overall performance of the presented RRM and ranking algorithms was tested with the use of real registered data, what makes it very interesting from the application point of view.
Źródło:
Biuletyn Wojskowej Akademii Technicznej; 2006, 55, 1; 55-75
1234-5865
Pojawia się w:
Biuletyn Wojskowej Akademii Technicznej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fault diagnosis of non-linear dynamical systems using analytical and soft computing methods
Autorzy:
Korbicz, J.
Powiązania:
https://bibliotekanauki.pl/articles/384480.pdf
Data publikacji:
2007
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
fault detection
unknown input observer
dynamical neural networks
neuro-fuzzy systems
evolutionary algorithms
Opis:
The paper deals with the problems of robust fault detection using analytical methods (observers and unknown input observers) and soft computing techniques (neural networks, neuro-fuzzy networks and genetic programming). The model-based approach to Fault Detection and Isolation (FDI) is considered. In particular, observers for non-linear Lipschitz systems and extended unknown input observers are discussed. In the case of soft computing techniques, the main objective is to show how to employ the bounded-error approach to determine the uncertainty of the GMDH and neuro-fuzzy networks. It is shown that based on soft computing models uncertainty defined as a confidence range for the model output, adaptive thresholds can be defined. The final part of the paper presents two illustrative examples that confirm the effectiveness of the unknown input observers and the neuro-fuzzy networks approaches.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2007, 1, 1; 7-23
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Soft computing methods applied to condition monitoring and fault diagnosis for maintenance
Autorzy:
Zio, E.
Powiązania:
https://bibliotekanauki.pl/articles/2069596.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Morski w Gdyni. Polskie Towarzystwo Bezpieczeństwa i Niezawodności
Tematy:
soft computing
artificial neural networks
fuzzy logic
genetic algorithms
condition monitoring
fault diagnosis
maintenance
Opis:
Malfunctions in equipment and components are often sources of reduced productivity and increased maintenance costs in various industrial applications. For this reason, machine condition monitoring is being pursued to recognize incipient faults in the strive towards optimising maintenance and productivity. In this respect, the following lecture notes provide the basic concepts underlying some methodologies of soft computing, namely neural networks, fuzzy logic systems and genetic algorithms, which offer great potential for application to condition monitoring and fault diagnosis for maintenance optimisation. The exposition is purposely kept on a somewhat intuitive basis: the interested reader can refer to the copious literature for further technical details.
Źródło:
Journal of Polish Safety and Reliability Association; 2007, 2; 363--377
2084-5316
Pojawia się w:
Journal of Polish Safety and Reliability Association
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using artificial immune and case-based reasoning methods in classification of treatment effectiveness
Autorzy:
Badura, D.
Ferdynus, D.
Powiązania:
https://bibliotekanauki.pl/articles/333874.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
wnioskowanie bazujące na przykładach
sztuczne systemy immunologiczne
sieci neuronowe rozmyte
case-based reasoning
artificial immune system
fuzzy neural nets
Opis:
The article concerns the analysis of classification of medical data by use of selected method of artificial intelligence: case-based reasoning. The subject of the research is the assessment of effective treatment, being one of the most important medical problems. The basis work of the assessment system should be one of the classification methods. The aim of the attempted research is to study which of the enumerated method will be able to group data containing incomplete information in the best way. The classified data are descended from the patients with nephroblastoma and patients with backbone pain. The final aim of the research is to work out the functioning method of the learning system, assisting the doctor with making a decision during working out on patient's treatment therapy, and making analyses of the treatment effectiveness. On the basis of the medical tests, the system will classify the data assigning them to the appropriate therapy groups. Moreover, in the system will be used artificial immunology as the method of generalizing or extrapolating of the gathering and considering so far cases.
Źródło:
Journal of Medical Informatics & Technologies; 2007, 11; 221-226
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Accumulation methods in the processing of difficult images
Autorzy:
Chmielewski, L.J.
Powiązania:
https://bibliotekanauki.pl/articles/332880.pdf
Data publikacji:
2008
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
analiza obrazu
gromadzenie dowodów
metody rozmyte
histogram
transformata Hough'a
image analysis
evidence accumulation
fuzzy methods
Hough transform
Opis:
The accumulation methods emerged in close relation to the development of the Hough transform (HT). The application of some far reaching generalizations of the HT will be presented. The accumulation principle will be taken as a starting point: Accumulate the relevant data from possibly many, possibly competent sources. This principle is known and widely used in image processing, mainly in the methods related to the HT. The principle is in opposition to the tendency to compress the image data as early in the processing as possible. The accumulation principle is a recommendation to utilize the redundancy in the image data in a specific way and should be applied when the images are difficult to process due to their low quality. The basic data structure is the fuzzy histogram, which is in fact an experimentally obtained approximation of the probability density of the phenomenon of interest. The concepts of a degree of fuzzification and the weakly and strongly fuzzified histograms will be introduced. A number of solutions found with the use of the accumulation principle will be presented. In the examples and tests, biomedical images will be used. Such images are challenging because the objects imaged are irregular and the quality of the images is usually limited in a natural way by the imaging modalities used. The accumulation methods are a good solution to the problem of analysis of such images.
Źródło:
Journal of Medical Informatics & Technologies; 2008, 12; 11-21
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 82

Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies