Edge extraction by FIRE operators

Russo, F.; Ramponi, Giovanni

doi:10.1109/fuzzy.1994.343744

Cited by 50 publications

(30 citation statements)

References 10 publications

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“…işareti ile gösterilen pikseller kenar çizgisi kabul edilirse sekiz farklı yönde kenar durumu oluşmaktadır. Bu kenar durumlarının oluşturduğu Bulanık Kural şablonları Şekil 1'de gösterilmektedir [13].…”

Section: Bulanık Mantık Tabanlı Kenar çıKarmaunclassified

Bulanık kurallara ve kenar devamlılığı kurallarına dayalı kenar tespiti iyileştirilmesi

Kara

Ulutaş

2017

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Section: Bulanık Mantık Tabanlı Kenar çıKarmaunclassified

Bulanık kurallara ve kenar devamlılığı kurallarına dayalı kenar tespiti iyileştirilmesi

Kara

Ulutaş

2017

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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“…In [15] described the method of allocation of contours, based on the FIRE (fuzzy inference ruled by else-actionfuzzy conclusion on the basis of OTHERWISE-action) paradigm, which is relatively immune to noise. It uses the differences of the gray levels in a neighborhood of 3x3 as inputs in the fuzzy rules.…”

Section: Allocation Of the Circuitsmentioning

confidence: 99%

Approaches to Image Processing using the Tools of Fuzzy Sets

Khamdamovich¹,

Khamdamov²,

Saliyev³

2018

IJCSEITR

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“…Bloch [17] proposed a morphological operation based fuzzy set for edge detection in images. Russo and Ramponi [18] used fuzzy rule-based operators built on the classic IF-THEN-ELSE rule-based architecture for edge detection. A drawback of this algorithm is that the global information has been neglected.…”

Section: Fuzzy Logic and Its Usesmentioning

confidence: 99%

Real-Time Traffic Control System using Fuzzy Logic based Edge Detector for Images

Datta¹,

Mukherjee²

2015

IJCA

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With the rising traffic congestion, an efficient method to control traffic and ensuring proper signalling has become very important. A number of methods are used worldwide to control and signal road traffic. We propose a method based on image processing to determine the density of vehicles on a particular lane of a road and control the signal accordingly. For each road crossing, screenshots will be taken from live feeds to determine the traffic density of a road and then based on an efficient fuzzy logic based edge detector; a comparative analysis of traffic density will be performed. Traffic signalling will be done so as to ensure that the busiest road gets the green signal at the earliest and for the longest duration. The second busiest road will get the green signal next and for a lesser duration. Signalling of other roads will be done accordingly. We have utilised a fuzzy rule based edge detection algorithm which is accurate and efficient. General TermsImage Processing, Fuzzy Logic.

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Edge extraction by FIRE operators

Cited by 50 publications

References 10 publications

Bulanık kurallara ve kenar devamlılığı kurallarına dayalı kenar tespiti iyileştirilmesi

Bulanık kurallara ve kenar devamlılığı kurallarına dayalı kenar tespiti iyileştirilmesi

Approaches to Image Processing using the Tools of Fuzzy Sets

Real-Time Traffic Control System using Fuzzy Logic based Edge Detector for Images

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