Rough Set and Otsu Approach based Hybrid Image Classification under Uneven Lighting Conditions

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2021 by IJETT Journal
Volume-69 Issue-12
Year of Publication : 2021
Authors : Mamata Wagh, Pradipta Kumar Nanda
DOI :  10.14445/22315381/IJETT-V69I12P211


MLA Style: Mamata Wagh, Pradipta Kumar Nanda "Rough Set and Otsu Approach based Hybrid Image Classification under Uneven Lighting Conditions" International Journal of Engineering Trends and Technology 69.12(2021):92-102. 

APA Style:Mamata Wagh, Pradipta Kumar Nanda, (2021). Rough Set and Otsu Approach based Hybrid Image Classification under Uneven Lighting Conditions. International Journal of Engineering Trends and Technology, 69(12), 92-102.

Uneven lighting creates vagueness in pixel intensities of the real-world images. This poses a great challenge in the process of image classification. Further, image classification is the fundamental step in the robot vision system. In order to handle the vagueness created due to uneven lighting conditions, a Rough set and Otsu approach-based Hybrid (RSOH) image segmentation scheme is proposed in this research work. In the proposed RSOH scheme, the Otsu and rough set approaches are hybridized in such a way that the resultant hybridized scheme overcomes the limitations of the rough set and Otsu approach and makes use of advantages of both the approaches. Hence, RSOHmodel has the potential to give accurate image classification performance. Through the window growing approach, the image is partitioned into different sub-images, i. e., windows, and inside each window, the proposed RSOHimage segmentation scheme is employed to make the thresholding adaptive. The proposed scheme is compared with granular computing as well as non-granular techniques. The performance of the proposed technique is quantified using four performance indexes such as PM, DC, VI, and BHD. From the performance indexes, it is evident that the proposed RSOH scheme is found to be superior to the existing techniques.

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Image segmentation, Uneven lighting conditions, Adaptive windowing, Granular computing, Rough set, Otsu thresholding.