Fuzzy-Bildverarbeitung

Tizhoosh, Hamid R.

doi:10.1007/978-3-642-58742-9

Cited by 27 publications

(8 citation statements)

References 32 publications

(39 reference statements)

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“…Indices of fuzziness and cluster validity were also introduced, with the main drawback of providing better results for crisp classifications, which contradicts the initial claimed need for fuzzy classes. The book by Tizhoosh [149] also mentions some work in geometry and mathematical morphology applied on fuzzy sets. It mainly focuses on low level processing, for edge detection and image quality improvement, using rules applied on local neighborhoods of pixels, or minimizing a fuzziness index.…”

Section: A Short Review Of Existing Textbooksmentioning

confidence: 98%

Fuzzy sets for image processing and understanding

Bloch

2015

Fuzzy Sets and Systems

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Section: A Short Review Of Existing Textbooksmentioning

confidence: 98%

Fuzzy sets for image processing and understanding

Bloch

2015

Fuzzy Sets and Systems

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“…ED works best for edge detection under LUV space but in general is sensitive to intensity variations. [28] ED, however, is not sensitive to variations in hue and saturation; therefore, it can be used in similar image processing and computer vision applications that contain little or no intensity variation.…”

Section: License Plate Locating Modulementioning

confidence: 98%

Color edge enhancement based fuzzy segmentation of license plates

Syed

Sarfraz

2005

Ninth International Conference on Information Visualisation (IV'05)

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The area of Automatic License Plate Recognition (ALPR) offers a big scope in numerous applications and a lot of techniques have been proposed. Most of the techniques work under restricted and supervised conditions. The proposed ALPR technique consists of two main modules: Plate Locating Module and Plate Segmentation Module. In the initial stage, search is being made for a prospective license plate on the basis of some of the local features contained in its fuzzy geometry. The second module utilizes a fuzzy C means based clustering over the finalized plate-patch to cluster the eight-connected components in it into desired and undesired regions. Segmentation proceeds only over the cluster containing the desired plate regions. In the experiment on locating license plates, 852 images were taken at various backgrounds and conditions. Of these, 10 images failed to locate the license plates; the rate of success was 98.82%. Experiments for character segmentation were carried out on the remaining 842 plates. Of which, 39 plates were not properly segmented; the success rate was 95.36%. The combined rate for the two phases of our License Plate Identification Algorithm is 94.24%.

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“…The elements that can make a mathematical expression for the fusion of information follow the hypothesis posited by Sugeno are the utilization of fuzzy membership functions as integrands, their weighting through fuzzy measures, and the binding of these two elements through a combination ofT-and S-norms [36]. The fuzzification of the data from the information sources x i is made through the application of fuzzy membership functions, which are named here as h i(X).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…In the theoretical framework of Fuzzy Logic the new degree of softness is achieved through the parameterization of the aggregation, e.g. T-and S-norms [36], or the consideration of the ranking as a factor upon which the already mentioned a priori importance can be modified. This strategy is used in OWAs and increases the adaptivity of the operators and its capability concerning compatibil- ity, partial aggregation and reinforcement [41].…”

Section: From Hard To Soft Fusion Operatorsmentioning

confidence: 99%

“…Moreover Kallman Filtering [3], Laplacian Pyramids [7] and Wavelet Transform [17] are some of the general purpose image processing methodologies used also for Data Fusion. Finally a set of operators is derived from the fruitful activity in operator research that is developed in the theoretical framework of Fuzzy Systems: fuzzy connectives, as T-, S-and Uni-norms [9], weighted ranking operators [36], Ordered Weighted Averaging (OWA) operators [15], and fuzzy integrals [35].…”

Section: Image Processing With Data Fusion Operatorsmentioning

confidence: 99%

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Soft Data Fusion in Image Processing

Soria-Frisch

2002

Soft Computing and Industry

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Abstract. Data fusion is a long tenn of research in image processing that is becoming more and more relevant owing to the complementary developments of computer and sensory technologies. Although operator research related to soft-computing, specially in the field of fuzzy systems, has evolved considerably during this last two decades, implemented frameworks of data fusion for image processing take seldom into consideration this kind of operators. Most of pattern recognition systems with image fusion are still based in basic operators, e.g. minimum or product. The purpose of the here presented tutorial is to analyze this fact, present some of the fuzzy aggregation operators in the context of data fusion for image processing and show some applications where the usage of the fuzzy integral, one of these operators, increased the perfonnance of image processing systems considering data fusion.

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Fuzzy-Bildverarbeitung

Cited by 27 publications

References 32 publications

Fuzzy sets for image processing and understanding

Fuzzy sets for image processing and understanding

Color edge enhancement based fuzzy segmentation of license plates

Soft Data Fusion in Image Processing

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