Multilevel thresholding for image segmentation through a fast statistical recursive algorithm

By Siddharth Arora, Jayadev Acharya, Amit Verma, Prasanta K. Panigrahi. Pattern Recognition Letters, 2006

318 citations recorded by the source corpus.

A novel algorithm is proposed for segmenting an image into multiple levels using its mean and variance. Starting from the extreme pixel values at both ends of the histogram plot, the algorithm is applied recursively on sub-ranges computed from the previous step, so as to find a threshold level and a new sub-range for the next step, until no significant improvement in image quality can be achieved. The method makes use of the fact that a number of distributions tend towards Dirac delta function, peaking at the mean, in the limiting condition of vanishing variance. The procedure naturally provides for variable size segmentation with bigger blocks near the extreme pixel values and finer divisions around the mean or other chosen value for better visualization. Experiments on a variety of images show that the new algorithm effectively segments the image in computationally very less time.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c5ddde0a6717b8d3adf675d58c586f7ad3df5e26

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In Computer Vision and Pattern Recognition

Citations in the corpus

Otsu Method and K-means (2009, methodology): “So there are many multilevel thresholding methods [2] [3] [6] [8] [13] [14] [15], which perform better than bi-level thresholding method when the picture has complex objects or background.”