To address the issue of image denoising algorithms blurring image details during the denoising process, we propose an adaptive denoising algorithm for the 3D reconstruction of 2D images. This algorithm takes into account the inherent visual characteristics of human eyes and divides the image into regions based on the entropy value of each region. The background region is subject to threshold denoising, while the target region undergoes processing using an adversarial generative network. This network effectively handles 2D target images with noise and generates a 3D model of the target. The proposed algorithm aims to enhance the noise immunity of 2D images during the 3D reconstruction process and ensure that the constructed 3D target model better preserves the original image's detailed information. Through experimental testing on 2D images and real pedestrian videos contaminated with noise, our algorithm demonstrates stable preservation of image details. The reconstruction effect is evaluated in terms of noise reduction and the fidelity of the 3D model to the original target. The results show an average noise reduction exceeding 95% while effectively retaining most of the target's feature information in the original image. In summary, our proposed adaptive denoising algorithm improves the 3D reconstruction process by preserving image details that are often compromised by conventional denoising techniques. This has significant implications for enhancing image quality and maintaining target information fidelity in 3D models, providing a promising approach for addressing the challenges associated with noise reduction in 2D images during 3D reconstruction.
Although several factors such as viral and bacterial pathogens, and drugs have been widely reported to be associated with immune thrombocytopenic purpura (ITP), brucellosis is an unusual cause of this disorder. Here, we describe a patient with brucellosis with fever and purpura as the first manifestation of her illness due to immune-mediated severe thrombocytopenia. In this case, ITP responded well to the anti-Brucella treatment with platelet recovery within three days.
Human posture equipment technology has advanced significantly thanks to advances in deep learning and machine vision. Even the most advanced models may not be able to predict all body joints accurately. This paper proposes an adaptive generative adversarial network to improve the human posture detection algorithm in order to address this issue. GAN is used in the algorithm to detect human posture improvement. The algorithm uses OpenPose to detect and connect keypoints and then generates heat maps in the GAN system model. During the training process, the confidence evaluation mechanism is added to the system model. The generator predicts posture, while the resolver refines human joints over time. And, by using normalization technologies in the confidence evaluation mechanism, the generator can pay more attention to the prominent body joints, improving the algorithm's body detection accuracy of nodes. In MPII, LSP, and FLIC datasets, the proposed algorithm has shown to have a good detection effect. Its positioning accuracy is about 95.37 percent, and it can accurately locate the joints of the entire body. Several other algorithms are outperformed by this one. The algorithm described in this article has the best simultaneous runtime in the LSP dataset.
Algorithms , Image Processing, Computer-Assisted , Humans , Posture
Objective: Cow's milk allergy (CMA) is a common allergic disease. Probiotics have been suggested as a treatment for CMA, with Lactobacillus rhamnosus GG (LGG) being one of the important predominant choices. Despite reports on this topic, the effectiveness of application in CMA remains to be firmly established. Methods: To assess the effects of LGG on CMA in children, the PubMed/Medline, Embase, Cochrane Library, and Web of Science databases were searched for studies on LGG in treatment of CMA, which were published in the English language. Results: Ten studies were finally included. Significantly higher tolerability rates favoring LGG over controls were observed [risk ratio (RR), 2.22; 95% confidence interval (CI), 1.86-2.66; I 2 = 0.00; moderate-quality evidence]. There were no significant differences in SCORAD values favoring LGG over the placebo (mean difference, 1.41; 95% CI, -4.99-7.82; p = 0.67; very low-quality evidence), and LGG may have improved fecal occult blood (risk ratio, 0.36; 95% CI, 0.14-0.92; p = 0.03; low-quality evidence). Conclusion: We found that LGG may have moderate-quality evidence to promote oral tolerance in children with CMA and may facilitate recovery from intestinal symptoms. However, this finding must be treated with caution, and more gpowerful RCTs are needed to evaluate the most effective dose and treatment time for children with CMA. Registration number: CRD42021237221.
