Robust Energy-Based Image Segmentation Using Nonparametric Joint Shape and Feature Priors

• Author(s): Dr. B. Hemanth Kumar; Mopuri Srinivas; Shaik Asadhulla; Pammi Pranay; Panguluri Rajesh
• Paper ID: 1715105
• Page: 1049-1055
• Published Date: 16-03-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 9 March-2026

Abstract

Image segmentation is a key task in computer vision that identifies important structures in images. Traditional methods like thresholding and region-based segmentation often struggle with noise and changes in shape. This paper introduces a segmentation approach that uses nonparametric joint shape and feature priors to enhance accuracy. Shape priors are learned from the MNIST dataset, and feature representations are extracted through Principal Component Analysis (PCA). We formulate the segmentation as an energy minimization problem that combines data fidelity, shape, and feature terms. Experimental results, measured using Dice coefficient and Hausdorff distance, show the effectiveness of this method.

Keywords

Image Segmentation, Shape Priors, Feature Priors, Principal Component Analysis (PCA), Energy Minimization, MNIST Dataset, Dice Coefficient.

Citations

IRE Journals:
Dr. B. Hemanth Kumar, Mopuri Srinivas, Shaik Asadhulla, Pammi Pranay, Panguluri Rajesh "Robust Energy-Based Image Segmentation Using Nonparametric Joint Shape and Feature Priors" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 1049-1055 https://doi.org/10.64388/IREV9I9-1715105

IEEE:
Dr. B. Hemanth Kumar, Mopuri Srinivas, Shaik Asadhulla, Pammi Pranay, Panguluri Rajesh "Robust Energy-Based Image Segmentation Using Nonparametric Joint Shape and Feature Priors" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715105