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The demand for cone-beam computed tomography (CBCT) imaging in clinics, particularly in dentistry, is rapidly increasing. Preoperative surgical planning is crucial to achieving desired treatment outcomes for imaging-guided surgical navigation. However, the lack of surface texture hinders effective communication between clinicians and patients, and the accuracy of superimposing a textured surface onto CBCT volume is limited by dissimilarity and registration based on facial features. To address these issues, this study presents a CBCT imaging system integrated with a monocular camera for reconstructing the texture surface by mapping it onto a 3D surface model created from CBCT images. The proposed method utilizes a geometric calibration tool for accurate mapping of the camera-visible surface with the mosaic texture. Additionally, a novel approach using 3D-2D feature mapping and surface parameterization technology is proposed for texture surface reconstruction. Experimental results, obtained from both real and simulation data, validate the effectiveness of the proposed approach with an error reduction to 0.32 mm and automated generation of integrated images. These findings demonstrate the robustness and high accuracy of our approach, improving the performance of texture mapping in CBCT imaging.

In today’s smart world of the Internet of Everything, cameras are essential to the information acquisition system. The image sensor is a crucial component in camera production, and its quality directly impacts imaging quality. However, the vignetting phenomenon can interfere with defect detection in image sensors, leading to inaccurate results. To address this issue, this paper proposes for the first time a method using multi-channel decomposition and logarithmic intensity entropy to solve the vignetting interference problem. The proposed method first creates a vignetting model using Fourier functions to simulate changes in illumination. Then, by calculating the minimum logarithmic intensity entropy of target images, optimal parametric solutions that fit the variation laws of the vignetting function were obtained. Finally, multichannel decomposition was used to perform vignetting correction and defect enhancement of the image sensor. Experimental results demonstrate that this method effectively recovers luminance information from vignetting regions, corrects different degrees of vignetting phenomena, and enhances detail information about defects. This lays the foundation for subsequent defect detection of image sensors and will improve the accuracy of the overall system performance in the future.

Urban sensing has become prevalent for monitoring dynamic urban status due to the development of intelligent computing. Fast sensing and accurate analysis of soil quality promote data-driven urban planning and management. Soil contamination hiders the sustainable development of the city’s environmental ecology. Decentralized sensing based on an Internet of Things (IoTs) architecture is a state-of-art technology. Portable spectral detection serves for immediate quantitative analysis of target components. In this case, portable sensing combined with federated learning simply shares the informative features extracted from the IoT-based spectral sensing data. However, the modeling technique for the analysis of spectroscopic sensing data faces several challenges due to the emerge of the big data problem in IoT-based sceneries. The consideration of possible federal learning of multinode collective data should ask for deep studies of modeling methodologies. In this article, we built up an IoT-based portable spectral sensing system for simultaneous sensing and detecting soil data at distributed sensing places. The collaborative training model was established for dealing with the dynamic sensing data, by the fusion design of a broad learning network (BLN) and a fuzzy partial least square (fPLS) model. The BLN output feature variables are produced from the scalable pseudo input layer with training linking weights adaptively, and the number of fuzzy rules is scalable. In the experiment, the decentralized sensing system and the fusion modeling framework carry out the model optimization for the portable near-infrared (NIR) spectroscopic sensing data of heavy metals in soil samples. The quantification of two elements demonstrated that the proposed framework yielded superior prediction results to the conventional PLS model for the training and evaluation of the dynamic sensing data. In interaction with the IoT architecture, the framework is prospectively expected as an advanced intelligent technical support for fast analysis of portable spectroscopic sensing data.