Image Edge Detection with Fuzzy Ant Colony Optimization Algorithm

Dorrani, Zohreh; Farsi, Hassan; Mohamadzadeh, Sajad

doi:10.5829/ije.2020.33.12c.05

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Zhang et al [40] developed an adaptive neuro-fuzzy system for edge detection. An edge detection mechanism based on ACO and fuzzy logic was proposed by Dorrani et al [41] to minimize false edges. An edge detection approach, based on Kalman filtering and ANN was proposed by Siddharth et al [42].…”

Section: Fl Based Image Edge Detectionmentioning

confidence: 99%

An Expert System Based on Type-1 Fuzzy Logic and Digital Image Processing for Knowledge Based Edge and Contour Detection

Ranjan

Avasthi

2023

IJE

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In computer vision, contour/edge detection is a crucial phenomenon. Edge detection is an important step in contour detection, which is helpful in the identification of important data. The accuracy of the edge detection process is heavily dependent on edge localization and orientation. In recent years, due to their versatility, soft computing approaches have been considered effective edge detection strategies. Broadly, edge detection accuracy is deeply affected by weak and dull edges. In recent works, edge detection based on fuzzy logic (FL) was proposed, and image edges were improved using guided filtering. However, guided image filtering (GIF) does not take into account the local features of an image. To include local features of an image for edge detection, an improved version, i.e., an offset enable sharpening-guided filter is used in this paper, and FL is used for edge detection. The figure of merit (FoM) and F-score are used to evaluate the method's accuracy. Using visual representations and performance metrics, the results are compared with those from cutting-edge techniques.

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Section: Fl Based Image Edge Detectionmentioning

confidence: 99%

An Expert System Based on Type-1 Fuzzy Logic and Digital Image Processing for Knowledge Based Edge and Contour Detection

Ranjan

Avasthi

2023

IJE

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“…There is a wide range of particlebased algorithms. Algorithms like PSO, BA, GOA and ACO are all particle-based [14,32,33,34]. One of the newest algorithms that was proposed in recent years is WOA or Whale Optimization Algorithm.…”

Section: Previous Studiesmentioning

confidence: 99%

Presenting a New Method for Earthquake Relief Center Location Allocation based on Whale Optimization Algorithm

Ashkezari

Yazdi

2021

IJE

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Despite the regulations set for the reinforcement of structures, many buildings in the world are vulnerable to earthquakes. Local governments try to be prepared to cope with this possible crisis through establishing earthquake relief centers. Considering the budget provision for creating n relief centers in a certain region, the main problem is yet in which place these centers should be constructed in order to achieve the highest speed and quality in rescuing after an earthquake. The enormous number of points that need relief, and of the many locations that could be candidates for constructing a relief center have caused this problem to be considered as an NP-Complete problem. In this article, there is a focus on solving the location allocation of the earthquake relief center problem. In order to find a reasonable solution, Whale Optimization Algorithm has been used. Classic Whale functions have been modified for this research dedicatedly. Results of the algorithm implementation and its execution on the map of region 1 of the city of Tehran show that with 9 relief centers, the average distance between each point and the center is roughly equal to 760 meters, showing almost 1.9% optimization compared to the best recent articles.

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“…2. Increasing the accuracy is often accompanied by decreasing the speed, which has been suggested to reduce the time of edge detection [13,14]. 3.…”

Section: Introductionmentioning

confidence: 99%

Shadow Removal in Vehicle Detection Using ResUNet-a

Dorrani

Farsi

Mohamadzadeh

2023

IJEE

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In traffic monitoring for video analysis systems, vehicle shadows have a negative effect on their performance. Shadow detection and removal are essential steps in accurate vehicle detection. In this paper, a new method is proposed for shadow detection using a novel convolution neural network architecture. In the proposed method, the edges of the image are first extracted. Edge extraction reduces calculation, and accelerates the execution of the method. The background of the frame is then removed and the main features are extracted using the ResUNet-a architecture. This architecture consists of two parts: the encoder and the decoder, which detect the shadow at the decoder output and then remove it. Deep learning is used to detect shadows, which increases the accuracy of the analysis. The ResUNet-a architecture can learn complex, hierarchical, and appropriate features from the image for accurate feature detection and discarding the irrelevant shadow, thereby outperforming conventional filters.The results show that the proposed method provides better performance on NJDOT traffic video, highway-1, and highway-3 datasets than popular shadow removal methods. Also, the method improves the evaluation criteria such as F-measure and runtime. The F-measure is 94 and 93% for highway-1 and highway-3, respectively.

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Image Edge Detection with Fuzzy Ant Colony Optimization Algorithm

Cited by 5 publications

References 17 publications

An Expert System Based on Type-1 Fuzzy Logic and Digital Image Processing for Knowledge Based Edge and Contour Detection

An Expert System Based on Type-1 Fuzzy Logic and Digital Image Processing for Knowledge Based Edge and Contour Detection

Presenting a New Method for Earthquake Relief Center Location Allocation based on Whale Optimization Algorithm

Shadow Removal in Vehicle Detection Using ResUNet-a

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