Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition

Chi, Zheru; Yan, Hong; Pham, Tuan D.

doi:10.1142/3132

Cited by 267 publications

(320 citation statements)

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“…The method was developed further by Lindblad (16) and a kernel density estimate was used for approximation of the distributions. Based on the thresholds given by maxima in the second derivative of the kernel density estimate, a fuzzy class membership value was found for each cell (17). Cells were classified as strongly negative, negative, neutral, positive, or strongly positive.…”

Section: Resultsmentioning

confidence: 99%

Sequential immunofluorescence staining and image analysis for detection of large numbers of antigens in individual cell nuclei

et al. 2002

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Background: Visualization of more than one antigen by multicolor immunostaining is often desirable or even necessary to explore spatial and temporal relationships of functional significance. Previously presented staining protocols have been limited to the visualization of three or four antigens. Methods: Immunofluorescence staining was performed both on slices of formalin-fixed tissue and on cells in culture. Images of the stained material were recorded using digital imaging fluorescence microscopy. The primary and secondary antibodies, as well as the fluorophores, were thereafter removed using a combination of denaturation and elution techniques. After removal of the fluorescence stain, a new immunofluorescence staining was performed, visualizing a new set of antigens. The procedure was repeated up to three times. A method for

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Section: Resultsmentioning

confidence: 99%

Sequential immunofluorescence staining and image analysis for detection of large numbers of antigens in individual cell nuclei

et al. 2002

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“…Additionally, we also included the C4.5 decision tree learner (Quinlan, 1993) as a wellknown benchmark classifier and, moreover, added two fuzzy rule-based classifiers from the KEEL suite (Alcalá-Fernandez et al, 2008): The CHI algorithm is based on Chi et al (1995Chi et al ( , 1996 and uses rule weighing as proposed by Ishibuchi and Yamamoto (2005). 6 The SLAVE algorithm makes use of genetic algorithms to learn a fuzzy classifier Perez, 1999, 2001).…”

Section: Classification Accuracymentioning

confidence: 99%

“…They are not very flexible and suffer from the "curse of dimensionality" in the case of many input variables but may have advantages with respect to interpretability (Guillaume, 2001). A well-known representative of this kind of approach is the CHI algorithm that we also used in our experiments (Chi et al, 1995(Chi et al, , 1996. It proceeds from a fuzzy partition for each attribute and learns a rule for every grid cell.…”

Section: Related Workmentioning

confidence: 99%

FURIA: an algorithm for unordered fuzzy rule induction

Hühn

Hüllermeier

2009

Data Min Knowl Disc

382

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This paper introduces a novel fuzzy rule-based classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the well-known RIPPER algorithm, a state-of-the-art rule learner, while preserving its advantages, such as simple and comprehensible rule sets. In addition, it includes a number of modifications and extensions. In particular, FURIA learns fuzzy rules instead of conventional rules and unordered rule sets instead of rule lists. Moreover, to deal with uncovered examples, it makes use of an efficient rule stretching method. Experimental results show that FURIA significantly outperforms the original RIPPER, as well as other classifiers such as C4.5, in terms of classification accuracy.

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“…They are based on fuzzy set theory [7][8] end extract fuzzy regions (subsets of pixels) from the fuzzy image. In soft segmentation approaches each pixel can be qualified into multiple regions with different degree of membership [7][9] [10].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Segmentation Method for Images of Heat-Emitting Objects

Fabijańska

2009

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. In this paper a problem of soft image segmentation is considered. An approach for segmenting images of heat-emitting specimens is introduced. Proposed algorithm is an extension of fuzzy C-means (FCM) clustering method. Results of applying the algorithm to exemplary images of heat-emitting specimens are presented and discussed. Moreover the comparison with results of standard fuzzy C-means clustering is provided.

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Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition

Cited by 267 publications

References 0 publications

Sequential immunofluorescence staining and image analysis for detection of large numbers of antigens in individual cell nuclei

Sequential immunofluorescence staining and image analysis for detection of large numbers of antigens in individual cell nuclei

FURIA: an algorithm for unordered fuzzy rule induction

A Fuzzy Segmentation Method for Images of Heat-Emitting Objects

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