scholarly journals Modified Artificial Bee Colony Algorithm with Multilevel Threshold Segmentation and Boundaries Evaluation for Shadow Detection

2019 ◽  
Vol 8 (11) ◽  
pp. 1395-1399
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Data Preprocessing ◽  
Shadow Detection ◽  
Threshold Segmentation ◽  
Bee Colony ◽  
Response Gradient ◽  
Curvature Gradient ◽  
Edge Response

The appearance of shadows typically causes severe problems in pc vision. There are various methods have already put forward but scope in this field is open. In this article Shadow Detection and Removal Using Modified artificial bee colony (MABC) Algorithm with Multilevel Threshold segmentation is proposed. The proposed method uses three threshold and corresponding boundaries, associated curvature, edge response, gradient, and MABC algorithm. First data preprocessing is applied to find the correlation between the pixels then three threshold and corresponding boundaries evaluated to accurately differentiate pixels as foreground. The edge response, curvature, gradient are applied to find the boundaries. Finally, MABC has been applied for detecting the shadow. The results show improvement in comparison with other existing methods

scholarly journals Optimization of Shadow Detection and Removal Using Multilevel Thresholds and Improved Artificial Bee Colony Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 5023-5028
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Median Filter ◽  
Visual Surveillance ◽  
Shadow Detection ◽  
Bee Colony ◽  
True Location ◽  
Computer Vision Applications ◽  
Curvature Gradient ◽  
Edge Response

Shadow Detection and removal from images is a challenging task in visual surveillance and computer vision applications. The appearance of shadows creates severe problems. There are various methods already exists but scope in this area is wide and open. In this paper, Optimization of Shadow Detection and Removal using Improved Artificial Bee Colony Algorithm (IABC) is proposed. The proposed method uses edge map, multilevel thresholds, masking, boundaries evaluation and, IABC algorithm. First data pre-processing is applied to find the correlation between the pixels then three level low, medium and high value of thresholds and the corresponding value of masking and boundaries are calculated to accurately differentiate pixels as foreground. The edge response, curvature, gradient are applied to find the true location of boundaries. Finally, IABC has been applied for detecting the shadow and median filter is used to remove the shadow. The results show improvement as compared to other existing methods

Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 38 (9A) ◽  
pp. 1384-1395
Author(s):  
Rakaa T. Kamil ◽  
Mohamed J. Mohamed ◽  
Bashra K. Oleiwi
Keyword(s):  
Mobile Robot ◽  
Comparative Research ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Control ◽  
Computational Time ◽  
Bee Colony ◽  
Shortest Distance ◽  
Minimum Number ◽  
Cubic Polynomial

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.

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