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

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.