Automated 3D Scenes Reconstruction Using Multiple Stereo Pairs from Portable Four-Camera Photographic Measurement System

An effective automatic 3D reconstruction method using a portable four-camera photographic measurement system (PFCPMS) is proposed. By taking advantage of the complementary stereo information from four cameras, a fast and highly accurate feature point matching algorithm is developed for 3D reconstruction. Specifically, we first utilize a projection method to obtain a large number of dense feature points. And then a reduction and clustering treatment is applied to simplify the Delaunay triangulation process and reconstruct a 3D model for each scene. In addition, a 3D model stitching approach is proposed to further improve the performance of the limited field-of-view for image-based method. The experimental results tested on the 172 cave in Mogao Grottoes indicate that the proposed method is effective to reconstruct a 3D scene with a low-cost four-camera photographic measurement system

Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement

Low-light image enhancement strives to improve the contrast, adjust the visibility, and restore the distortion in color and texture. Existing methods usually pay more attention to improving the visibility and contrast via increasing the lightness of low-light images, while disregarding the significance of color and texture restoration for high-quality images. Against above issue, we propose a novel luminance and chrominance dual branch network, termed LCDBNet, for low-light image enhancement, which divides low-light image enhancement into two sub-tasks, e.g., luminance adjustment and chrominance restoration. Specifically, LCDBNet is composed of two branches, namely luminance adjustment network (LAN) and chrominance restoration network (CRN). LAN takes responsibility for learning brightness-aware features leveraging long-range dependency and local attention correlation. While CRN concentrates on learning detail-sensitive features via multi-level wavelet decomposition. Finally, a fusion network is designed to blend their learned features to produce visually impressive images. Extensive experiments conducted on seven benchmark datasets validate the effectiveness of our proposed LCDBNet, and the results manifest that LCDBNet achieves superior performance in terms of multiple reference/non-reference quality evaluators compared to other state-of-the-art competitors. Our code and pretrained model will be available.Comment: 14 pages, 16 figure

Knowledge Domains and Emerging Trends of Osteoblasts-Osteoclasts in Bone Disease From 2002 to 2021: A Bibliometrics Analysis and Visualization Study

BackgroundOsteoblasts-Osteoclasts has been a major area in bone disease research for a long time. However, there are few systematic studies in this field using bibliometric analysis. We aimed to perform a bibliometric analysis and visualization study to determine hotspots and trends of osteoblasts-osteoclasts in bone diseases, identify collaboration and influence among authors, countries, institutions, and journals, and assess the knowledge base to develop basic and clinical research in the future.MethodsWe collected articles and reviews for osteoblasts-osteoclasts in bone diseases from the Web of Science Core Collection. In addition, we utilized scientometrics software (CiteSpace5.8 and VOSviewer1.6.18) for visual analysis of countries/regions, institutions, authors, references, and keywords in the field.ResultsIn total, 16,832 authors from 579 institutions in 73 countries/regions have published 3,490 papers in 928 academic journals. The literature in this field is rapidly increasing, with Bone publishing the most articles, whereas Journal of Bone and Mineral Research had the most co-cited journals. These two journals mainly focused on molecular biology and the clinical medicine domain. The countries with the highest number of publications were the US and China, and the University of Arkansas for Medical Sciences was the most active institution. Regarding authors, Stavros C. Manolagas published the most articles, and Hiroshi Takayanagi had the most co-cited papers. Research in this field mainly includes molecular expression and regulatory mechanisms, differentiation, osteoprotection, inflammation, and tumors. The latest research hotspots are oxidative stress, mutation, osteocyte formation and absorption, bone metabolism, tumor therapy, and in-depth mechanisms.ConclusionWe identified the research hotspots and development process of osteoblasts-osteoclasts in bone disease using bibliometric and visual methods. Osteoblasts-osteoclasts have attracted increasing attention in bone disease. This study will provide a valuable reference for researchers concerned with osteoblasts-osteoclasts in bone diseases

Deformable channel non‐local network for crowd counting

Abstract Both global dependency and local correlation are crucial for solving the scale variation of crowd. However, most of previous methods fail to take two factors into consideration simultaneously. Against the aforementioned issue, a deformable channel non‐local network, abbreviated as DCNLNet for crowd counting, which can simultaneously learn global context information and adaptive local receptive field is proposed. Specifically, the proposed DCNLNet consists of two well‐crafted designed modules: deformable channel non‐local block (DCNL) and spatial attention feature fusion block (SAFF). The DCNL encodes long‐range dependencies between pixels and the adaptive local correlation with channel non‐local and deformable convolution, respectively, benefiting for improving the spatial discrimination of features. While the SAFF aims to aggregate the cross‐level information, which interacts these features from different depths and learns specific weights for the feature maps with spatial attention. Extensive experiments are performed on three crowd counting benchmark datasets and experimental results indicate that the proposed DCNLNet achieves compelling performance compared to other representative counting models

Partially supervised neighbor embedding for example-based image super-resolution

Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding

Modeling optimization for a typical VOCs thermal conversion process

Aiming at the current environmental problems, the thermal oxidation treatment for industrial VOCs emission is a common and effective measure. This paper studies on the optimization effect of one optimization method for direct VOCs thermal oxidation of a color aluminum spraying production line based on Aspen-Plus. According to the direct VOCs thermal oxidation process with a 30000 m³/h circulating air volume, propose the flue gas reflux and coating room drainage technology. Use the second law of thermodynamics, and the exergy flow analysis shows the methane consumption could be reduced 12%. Carbon emissions also decreased significantly, with 3.42% reduction. These findings are practical for industrial production cost saving and environmental protection problems solving

Modeling optimization for a typical VOCs thermal conversion process

Aiming at the current environmental problems, the thermal oxidation treatment for industrial VOCs emission is a common and effective measure. This paper studies on the optimization effect of one optimization method for direct VOCs thermal oxidation of a color aluminum spraying production line based on Aspen-Plus. According to the direct VOCs thermal oxidation process with a 30000 m³/h circulating air volume, propose the flue gas reflux and coating room drainage technology. Use the second law of thermodynamics, and the exergy flow analysis shows the methane consumption could be reduced 12%. Carbon emissions also decreased significantly, with 3.42% reduction. These findings are practical for industrial production cost saving and environmental protection problems solving

Parallel matters: Efficient polyp segmentation with parallel structured feature augmentation modules

Abstract The large variations of polyp sizes and shapes and the close resemblances of polyps to their surroundings call for features with long‐range information in rich scales and strong discrimination. This article proposes two parallel structured modules for building those features. One is the Transformer Inception module (TI) which applies Transformers with different reception fields in parallel to input features and thus enriches them with more long‐range information in more scales. The other is the Local‐Detail Augmentation module (LDA) which applies the spatial and channel attentions in parallel to each block and thus locally augments the features from two complementary dimensions for more object details. Integrating TI and LDA, a new Transformer encoder based framework, Parallel‐Enhanced Network (PENet), is proposed, where LDA is specifically adopted twice in a coarse‐to‐fine way for accurate prediction. PENet is efficient in segmenting polyps with different sizes and shapes without the interference from the background tissues. Experimental comparisons with state‐of‐the‐arts methods show its merits

Experimental study on differences in clivus chordoma bone invasion: an iTRAQ-based quantitative proteomic analysis.

Although a bone tumor, significant differences in the extent of bone invasion exist in skull base chordoma, which directly affect the extent of surgical resection, and have an impact on its prognosis. However, the underlying mechanism of the phenomenon is not clearly understood. Therefore, we used an iTRAQ-based quantitative proteomics strategy to identify potential molecular signatures, and to find predictive markers of discrepancy in bone invasion of clivus chordoma. According to bone invasive classification criteria, 35 specimens of clivus chordoma were calssified to be either endophytic type (Type I) or exophytic type (Type II). An initial screening of six specimens of endophytic type and six of exophytic was performed, and 250 differentially expressed proteins were identified. Through the GO and IPA analysis, we found evidence that the expression of inflammatory activity-associated proteins up-regulated in endophytic type, whereas the expression of cell motility-associated proteins up-regulated in exophytic ones. Moreover, TGFβ1 and mTOR signal pathway seemed to be related with bone invasion. Thus, TGFβ1, PI3K, Akt, mTOR, and PTEN were validated in the following 23 samples by immune histochemistry and Western blot. The expression levels of TGFβ1 and PTEN were significantly lower in the endophytic type than in the exophytic ones. It was found that TGFβ1 may play an important role in its bone invasion. The mechanisms may be related with conducting an increased inflammatory cell response and a decline in cytoskeletal protein expression. PTEN is confirmed to be associated with the degree of bone invasion. The PI3K/AKT/mTOR signaling pathway might be associated with the bone invasion, but still needs a larger sample size to be verified These results, for the first time, not only demonstrate the biological changes that occur in different growth patterns from the perspective of proteomics, but also provide novel markers that may help to reveal the mechanisms behind clivus chordomas