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Image Thresholding using 2D Tsallis-Havrda-Charvaat Entropy and Local Binary Pattern (LBP)


Affiliations
1 Department of Information Technology, RCC Institute of Information Technology, Canal South Road, Beliaghata, Kolkata - 700015, West Bengal, India
 

This paper proposes an automatic global thresholding method based on 2D Tsallis-Havrda-Charva+-t entropy and histogram of Local Binary Patterns (LBP). Tsalli-Havrda-Charvat entropy is obtained from 2D histogram, which has determined by using the LBP decimal value and the average decimal value of its neighborhood. Based on this entropy we obtain the optimal threshold pair by maximizing the criterion function. LBP histogram is adopted to capture the texture information. LBP's high performance for texture characterization helps to make our method more suitable for thresholding the images in problem. In this paper we report the effectiveness of our thresholding method when applied to some real-world and synthetic images, and experiments show that the performance of our proposed method is promising, robust and fast.

Keywords

Image Segmentation, 2D Histogram, Local Binary Pattern, Thresholding.
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  • Image Thresholding using 2D Tsallis-Havrda-Charvaat Entropy and Local Binary Pattern (LBP)

Abstract Views: 766  |  PDF Views: 307

Authors

Rakhi Tewari
Department of Information Technology, RCC Institute of Information Technology, Canal South Road, Beliaghata, Kolkata - 700015, West Bengal, India
Soumyadip Dhar
Department of Information Technology, RCC Institute of Information Technology, Canal South Road, Beliaghata, Kolkata - 700015, West Bengal, India
Hiranmoy Roy
Department of Information Technology, RCC Institute of Information Technology, Canal South Road, Beliaghata, Kolkata - 700015, West Bengal, India

Abstract


This paper proposes an automatic global thresholding method based on 2D Tsallis-Havrda-Charva+-t entropy and histogram of Local Binary Patterns (LBP). Tsalli-Havrda-Charvat entropy is obtained from 2D histogram, which has determined by using the LBP decimal value and the average decimal value of its neighborhood. Based on this entropy we obtain the optimal threshold pair by maximizing the criterion function. LBP histogram is adopted to capture the texture information. LBP's high performance for texture characterization helps to make our method more suitable for thresholding the images in problem. In this paper we report the effectiveness of our thresholding method when applied to some real-world and synthetic images, and experiments show that the performance of our proposed method is promising, robust and fast.

Keywords


Image Segmentation, 2D Histogram, Local Binary Pattern, Thresholding.

References