Image Edge Detection based on Soft Computing Approach

Khaire, Pushpajit; Thakur, Nileshsingh V.

doi:10.5120/8061-1426

Cited by 5 publications

(2 citation statements)

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“…This work is demonstrated along with the existing Sobel and prewitt edge detector. [5] proposed image edge detection based on soft computing approach which enhances the edge detection where the histogram is applied with fuzzy logic. [6] proposed edge detection using fuzzy logic in matlab.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED: An Improved Fuzzy Based Algorithm for Detecting Text from Images Using Stroke Width Transform

Thilagavathy¹,

Chilambuchelvan²

2016

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Retraction type (multiple responses allowed):  Unreliable findings   Lab error  Inconsistent data  Analytical error  Biased interpretation   Other:  Irreproducible results  Failure to disclose a major competing interest likely to influence interpretations or recommendations  Unethical research   Fraud   Data fabrication  Fake publication  Other:  Plagiarism  Self plagiarism  Overlap  Redundant publication *  Copyright infringement  Other legal concern:  Editorial reasons   Handling error  Unreliable review(s)  Decision error  Other: X Other: The journal Circuits and Systems is not in the authors' university's list. Results of publication (only one response allowed): Xare still valid.  were found to be overall invalid. Author's conduct (only one response allowed):  honest error  academic misconduct Xnone (not applicable in this casee.g. in case of editorial reasons) * Also called duplicate or repetitive publication. Definition: "Publishing or attempting to publish substantially the same work more than once." History Expression of Concern:  yes, date: yyyy-mm-dd Xno Correction:  yes, date: yyyy-mm-dd Xno Comment: This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's Retraction Guidelines. Aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.

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Section: Related Workmentioning

confidence: 99%

RETRACTED: An Improved Fuzzy Based Algorithm for Detecting Text from Images Using Stroke Width Transform

Thilagavathy¹,

Chilambuchelvan²

2016

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“…Several bio-inspired algorithms have been used for optimisation of digital filters. Particle swarm optimization (PSO) [4,10,14,15,16], Genetic Algorithm (GA) [17,18], Fuzzy Logic [19], and Adaptive neuro-fuzzy inference system (ANFIS) [20]. Hybrid techniques such as Simulated annealing and PSO [21], Artificial Neural Network (ANN), Genetic Algorithms and Fuzzy Set [22], Cat Swarm Optimisation (CSO) and Fast Library for Approximate Nearest Neighbours (FLANN) [23], and the combination of GA, NN, and Fuzzy Logic [24].…”

Section: Introductionmentioning

confidence: 99%

Moth Flame Optimization Algorithm for Optimal FIR Filter Design

Adamu¹,

Dada²,

Bassi³

2021

IJISA

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This paper presents the application of Moth Flame optimization (MFO) algorithm to determine the best impulse response coefficients of FIR low pass, high pass, band pass and band stop filters. MFO was inspired by observing the navigation strategy of moths in nature called transverse orientation composed of three mathematical submodels. The performance of the proposed technique was compared to those of other well-known high performing optimization techniques like techniques like Particle Swarm Optimization (PSO), Novel Particle Swarm Optimization (NPSO), Improved Novel Particle Swarm Optimization (INPSO), Genetic Algorithm (GA), Parks and McClellan (PM) Algorithm. The performances of the MFO based designed optimized FIR filters have proved to be superior as compared to those obtained by PSO, NPSO, INPSO, GA, and PM Algorithm. Simulation results indicated that the maximum stop band ripples 0.057326, transition width 0.079 and fitness value 1.3682 obtained by MFO is better than that of PSO, NPSO, INPSO, GA, and PM Algorithms. The value of stop band ripples indicated the ripples or fluctuations obtained at the range which signals are attenuated is very low. The reduced value of transition width is the rate at which a signal changes from either stop band to pass band of a filter or vice versa is very good. Also, small fitness value in an indication that the values of the control variable of MFO are very near to its optimum solutions. The proposed design technique in this work generates excellent solution with high computational efficiency. This shows that MFO algorithm is an outstanding technique for FIR filter design.

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