An Efficient Artificial bee Colony With Boundary Value Segmentation for Shadow Detection
Rakesh Kumar Das1, Madhu Shandilya2

1Rakesh Kumar Das, Research Scholar, Department of Electronics & Communication Engineering, MANIT Bhopal (M.P), India.
2Madhu Shandilya, Professor, Department of Electronics & Communication Engineering, MANIT Bhopal (M.P), India.

Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 1963-1967 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7625068519/19©BEIESP
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Abstract: In the area of computer vision shadow detection is important as it provides the image with high resolution with pixel clarity. There are several approaches have already be introduced but scope in this area is wide and open. In this paper an efficient artificial bee colony (ABC) with boundary value segmentation for shadow detection has been proposed. The proposed approach is the hybridization of association rules, Otsu approach, gradient segmentation and ABC algorithm. First the data preprocessing has been applied for adjoin and adjacent matrix. Then association rules have been applied for adjoin and associated pixels and joint correlations have been formed. The Otsu’s approach and gradient segmentation have been applied. It is beneficial in threshold value estimation in case of intra class variations also. Finally ABC algorithm has been applied for the final object detection and tracking. The results indicate that the approach is efficient in shadow detection and tracking.
Keywords: Shadow Detection, Artificial Bee Colony, Association Rules, Boundary Value Segmentation.

Scope of the Article: Artificial Life and Societies