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Journal of Flow Visualization and Image Processing

Erscheint 4 Ausgaben pro Jahr

ISSN Druckformat: 1065-3090

ISSN Online: 1940-4336

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.6 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00013 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.14 SJR: 0.201 SNIP: 0.313 CiteScore™:: 1.2 H-Index: 13

Indexed in

CELLULAR NEURAL NETWORK IMAGE EDGE DETECTION BASED ON HYPERBOLIC TANGENT FUNCTION − FROM PHOTOGRAPHIC IMAGE TO FLOW VISUALIZATION

Volumen 22, Ausgabe 1-3, 2015, pp. 151-163
DOI: 10.1615/JFlowVisImageProc.2016017209
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ABSTRAKT

This paper proposes the architecture of cellular neural network (CNN) for image edge detection based on a hyperbolic tangent function. This network gives better results compared to other methods and is capable of application in real time due to the parallel processing nature of the CNN. The paper first discusses the equilibrium point of the hyperbolic tangent function, and thereafter the values of a template suitable for cellular neural network are determined. The method effectively defines the template of CNN and uses MATLAB simulation to verify the effectiveness of the edge detection of wood defect images and fluid images. The experimental results show that the improved CNN model can significantly reduce the number of iterations in the edge extraction, and can more accurately determine the edge.

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