With the rapid development of smart medical care, copyright security for medical images is becoming increasingly important. To improve medical images storage and transmission safety, this paper proposes a robust zero-watermarking algorithm for medical images by fusing Dual-Tree Complex Wavelet Transform (DTCWT), Hessenberg decomposition, and Multi-level Discrete Cosine Transform (MDCT). First, the low-frequency sub-band of the medical image is obtained through the DTCWT and MDCT. Then Hessenberg decomposition is used to construct the visual feature vector. Meanwhile, the encryption of the watermarking image by combining cryptographic algorithms, third-party concepts, and chaotic sequences enhances the algorithm’s security. In the proposed algorithm, zero-watermarking technology is utilized to assure the medical images’ completeness. Compared with the existing algorithms, the proposed algorithm has good robustness and invisibility and can efficiently extract the watermarking image and resist different attacks.
The in vitro reactivity of different glasses, with 55mol% SiO 2 and MgO/Na 2 O molar ratios ranging from 1/8 to 8/1, was investigated. Despite the same amount of SiO 2 , the glasses exposed different reactivities, from very reactive (low MgO/Na 2 O ratio) to inert (high MgO/Na 2 O ratio). These results are interpreted in terms of a cross-link disruption by Na 2 O.
Although deep-learning-based approaches have demonstrated impressive performance in object detection tasks, the requirement for large datasets of annotated training images limits the feasibility of deep neural networks. For example, obtaining a large number of crack images of a dam is unlikely, particularly in the absence of open-source datasets. To address this problem, the authors have developed three synthetic data generators based on virtual scene simulation and image processing for generating large amounts of labeled dam surface crack data. These synthetic data combined with public-available images of cracks on pavement and concrete are further used to train a state-of-the-art object detection neural network, resulting in a 29.2% improvement in the overall crack detection mean average precision (mAP) compared to using only images of cracks on pavement and concrete. Furthermore, given the necessity for further analysis of some critical cracks, an image-processing-based approach for segmenting the crack in each detected bounding box and estimating its length and thickness is provided.
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