Abstract:
Probabilistic model-based segmentation uses the hidden Markov random field (HMRF) to represent image class labels. Existing HMRF models consider either the number of neighboring pixels with similar class labels or their spatial distance.
This spatial information only affects image pixel class labels, not parameter estimation. This lowers parameter estimation and segmentation performance. Existing models weight spatial information equally for class label estimation of all pixels in the image, which causes significant misclassification for pixels in image class boundaries.
The project develops a clique potential function and class label distribution using image class parameters. The proposed framework uses a new scaling parameter to adaptively measure spatial information for image pixel class label estimation, unlike HMRF model-based segmentation techniques.
Modifying HMRF-based segmentation shows the framework’s importance. The proposed class label distribution benefits regardless of intensity distributions.
The proposed and existing class label distributions in HMRF model are compared qualitatively and quantitatively for brain MR image segmentation, HEp-2 cell delineation, natural image and object segmentation.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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