Work

Infrared-optical image segmentation using a potts prior and hierarchical bayesian model

Year: 2025

Type: article

Abstract: The modern industrial equipment are widely used in various industries. Its performance is notably affected by temperature variations, making temperature monitoring crucial. To achieve this, we employ a combination of optical and infrared sensors to visualize and measure the temperature field. This paper presents a new infrared and visible image segmentation forward model, along with a Bayesian estimation framework featuring a specially designed Markov-Potts prior model. This model addresses uncertainties in the forward model errors of both infrared and visible images. We use a joint maximum a posteriori (JMAP) criterion and an alternate optimization algorithm to estimate both fused image, its segmentation, and all the parameters of the probabilistic model. The proposed method is tested on benchmark datasets and industrial scenarios, demonstrating its robustness and effectiveness.

Source: EURASIP Journal on Image and Video Processing

DOI: 10.1186/s13640-025-00681-4

Authors: Wang Li, Zhao Puhan, Chu Ning, Mohammad-Djafari Ali

Institutions: Central South University, Eastern Institute of Technology, Ningbo, Centre National de la Recherche Scientifique

Language: English

Topic: Advanced Image Fusion Techniques

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,790

Funder: National Natural Science Foundation of China