AN ACCURATE IMAGE SEGMENTATION USING REGION SPLITTING TECHNIQUE
Angeline Lydia · 2011
This paper aims to show the good accuracy in an image segmentation using split and merge method. In an existing MRF (Markov Random Field) based unsupervised segmentation, the MRF model parameters are typically estimated. Those global statistics are far from accurate for local areas if the image is highly non-stationary, and hence will generate false boundaries. So, the proposed region splitting method provides the possibility of building a hierarchical representation of the image content and allows various region features and even domain knowledge to be incorporated in the segmentation process. The algorithm has been successfully tested on several artificial images. Thus our proposed method is an improvement over the method called the iterative region growing using semantics (IRGS).