Edge-Detection Method for Image Processing Based on Generalized Type-2 Fuzzy Logic

Melín, Patricia; González, Claudia I.; Castro, Juan R.; Mendoza, Olivia; Castillo, Oscar

doi:10.1109/tfuzz.2013.2297159

Cited by 243 publications

(87 citation statements)

References 42 publications

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“…For instance, the haar cascade arithmetic [12] is used to detect single or multiple faces and both eyes. To automatically find out and track the eye pupils' movement, the object detection [14], edge detection [9], and pattern recognition [8] are further employed in this study. Figure 2 shows the hardware architecture with all the main components, including power supply, Raspberry-Pi board, cameras, motors, and sensors.…”

Section: Previous Workmentioning

confidence: 99%

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Prototyping An Autonomous Eye-Controlled System (AECS) using Raspberry-Pi on Wheelchairs

Thota¹,

Vangali²,

Yang³

2017

IJCA

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In order to help physically disabled persons to make their life independent, this paper proposes an autonomous eye controlled system (AECS) on wheelchairs. In this work, several OpenCV image processing algorithms are employed to track the eye motion to coordinate the wheelchair moving left, right, and straight forward. We use the Raspberry-Pi B+ board as the system center to process the images and control the motors via GPIO. Experimental results show that the ACES system can be effectively used in the prototype, and outperforms the hand gesture controlled system by 25% processing latency reduction. General TermsComputer engineering, embedded system design

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Section: Previous Workmentioning

confidence: 99%

“…Consequently, the system use the canny edge detection and corner edge recognition algorithms [9] to decide the edges of the eye pupils, which is shown in Figure 6(b). In what follows, a circle is then drew on the eye pupils using the hough circle transform arithmetic [19].…”

Section: Edge Detection and Eye Pupil Circlingmentioning

confidence: 99%

Prototyping An Autonomous Eye-Controlled System (AECS) using Raspberry-Pi on Wheelchairs

Thota¹,

Vangali²,

Yang³

2017

IJCA

View full text Add to dashboard Cite

show abstract

“…Other work using an alpha-planes representation has been applied, e.g. as a method for edge-detection [35] and a learning method to forecast Mackey-Glass time-series [41] . The latter showed a better performance of general type-2 fuzzy logic systems using a simpler model known as "triangle quasi-type-2 fuzzy logic system" first presented in [40] .…”

Section: Type-2 Fuzzy Sets and Uncertainty Modelingmentioning

confidence: 99%

Learning of interval and general type-2 fuzzy logic systems using simulated annealing: Theory and practice

Almaraashi

John

Hopgood

et al. 2016

Information Sciences

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a b s t r a c tThis paper reports the use of simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of interval and general type-2 fuzzy logic systems to maximize their modeling ability. The combination of simulated annealing with these models is presented in the modeling of four benchmark problems including real-world problems. The type-2 fuzzy logic system models are compared in their ability to model uncertainties associated with these problems. Issues related to this combination between simulated annealing and fuzzy logic systems, including type-2 fuzzy logic systems, are discussed. The results demonstrate that learning the third dimension in type-2 fuzzy sets with a deterministic defuzzifier can add more capability to modeling than interval type-2 fuzzy logic systems. This finding can be seen as an important advance in type-2 fuzzy logic systems research and should increase the level of interest in the modeling applications of general type-2 fuzzy logic systems, despite their greater computational load.

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“…Canny's edge detection algorithm is achieved to segment the gradient image [19]. An edge-detection system is carried out based on the morphological gradient method and comprehensive type-2 fuzzy logic [20]. The theory of alpha planes is utilized to implement generalized type-2 fuzzy logic for edge detection.…”

Section: Related Workmentioning

confidence: 99%

Edge Detection Method for Latent Fingerprint Images Using Intuitionistic Type-2 Fuzzy Entropy

Ezhilmaran

Manickam

2016

Cybernetics and Information Technologies

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A latent fingerprint is an interesting issue because of it has attained from crime places and moreover contained a low quality image, less number of features and unwanted noises. It is necessity to extract the original image with exact boundary from the surface for further processing such as authentication, identification and matching. In this work, a new distance measure has been proposed for latent fingerprint edge detection using Intuitionistic Type-2 Fuzzy Entropy (IT2FE) and a comprehensible definition is made for Intuitionistic Type-2 Fuzzy Sets (IT2FS). IT2FS takes into account of uncertainty in the form of membership function which is also termed as Intuitionistic

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Edge-Detection Method for Image Processing Based on Generalized Type-2 Fuzzy Logic

Cited by 243 publications

References 42 publications

Prototyping An Autonomous Eye-Controlled System (AECS) using Raspberry-Pi on Wheelchairs

Prototyping An Autonomous Eye-Controlled System (AECS) using Raspberry-Pi on Wheelchairs

Learning of interval and general type-2 fuzzy logic systems using simulated annealing: Theory and practice

Edge Detection Method for Latent Fingerprint Images Using Intuitionistic Type-2 Fuzzy Entropy

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