Work

Modeling and Suppression of Inhomogeneous Intensity Edible Bird Nest Images for Impurity Segmentation Using β-Variational Autoencoder

Year: 2025

Type: article

Abstract: This study proposes a β-variational autoencoder (β-VAE) method to address intensity inhomogeneity (IIH) in edible bird’s nest (EBN) images, which creates an uneven intensity that obscures fine impurity details and reduces segmentation accuracy. First, the β-VAE is used to learn the feature distribution of EBN images by mapping them into a latent space. This latent space is then disentangled through selective filtering and penalization of specific latent dimensions. This unsupervised learning approach effectively captures and isolates IIH in EBN images. Additionally, enabling precise segmentation of EBN and impurities without requiring annotated datasets. It also enhances robustness in handling unseen image instances. The proposed method achieves an intersection over union of 73.08% (equivalent to a Dice coefficient of 84.44%), surpassing existing segmentation techniques. By resolving IIH, this method improves the reliability and adaptability of automated EBN inspection systems for practical applications.

Source: Proceedings of Engineering and Technology Innovation

DOI: 10.46604/peti.2024.13998

Authors: Khairul Firdaus Mohd Talib, Hasnida Ab-Samat, Lai Hoong Cheng, Kin Sam Yen

Institution: Universiti Sains Malaysia

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: diamond

Funders: Iran Telecommunication Research Center, National Institute of Information and Communications Technology