GMM Based Adaptive Thresholding for Uneven Lighting Image Binarization

Image binarization of uneven lighted images, using thresholding techniques, is still a challenging task. Adaptive thresholding methods are the widely adopted approaches for binarization of uneven lighting images. However, the efficacy of these adaptive thresholding methods is highly sensitive to the criteria function used for measuring the bimodal property of the gray level distribution of a local region. In this paper, we propose Gaussian Mixture Model (GMM) which is based on adaptive thresholding for binarizing uneven lighting images. The proposed GMM based criteria function efficiently partitioning the uneven light images into bimodal and unimodal subimages with low uneven light effect. At first, the bimodal subimages are binarized using Otsu's thresholding approach, followed by unimodal subimages being thresholded using the bilinear interpolation of neighbouring thresholds of bimodal subimages. Next a fast Expectation Maximization(EM) algorithm is developed to reduce the computational complexity of the GMM. Experimental results on different uneven light images demonstrate that the proposed adaptive thresholding outperforms the other considered methods with an avg. misclassification error of 1.68 % and an average computation time of 1.50 seconds. The computational time can be further reduced by a specially purposed hardware and parallel processing of each subimages for real time applications.

Automated summary

The paper proposes an adaptive thresholding method based on Gaussian Mixture Model for binarizing uneven lighting images. The method efficiently partitions the images into bimodal and unimodal subimages with low uneven light effects, demonstrating superior performance compared to other methods in terms of misclassification error and computation time.