Street-Level Image Localization Based on Building-Aware Features via Patch-Region Retrieval under Metropolitan-Scale

The aim of image-based localization (IBL) is to localize the real location of query image by matching reference image in database with GNSS-tags. Popular methods related to IBL commonly use street-level images, which have high value in practical application. Using street-level image to tackle IBL task has the primary challenges: existing works have not made targeted optimization for urban IBL tasks. Besides, the matching result is over-reliant on the quality of image features. Methods should address their practicality and robustness in engineering application, under metropolitan-scale. In response to these, this paper made following contributions: firstly, given the critical of buildings in distinguishing urban scenes, we contribute a feature called Building-Aware Feature (BAF). Secondly, in view of negative influence of complex urban scenes in retrieval process, we propose a retrieval method called Patch-Region Retrieval (PRR). To prove the effectiveness of BAF and PRR, we established an image-based localization experimental framework. Experiments prove that BAF can retain the feature points that fall on the building, and selectively lessen the feature points that fall on other things. While this effectively compresses the storage amount of feature index, we can also improve recall of localization results; implemented in the stage of geometric verification, PRR compares matching results of regional features and selects the best ranking as final result. PRR can enhance effectiveness of patch-regional feature. In addition, we fully confirmed the superiority of our proposed methods through a metropolitan-scale street-level image dataset.

Magic Symbols (Charaktêres) on North African Curse Tablets as a Regional Feature

This study examines the practice of magic symbols in North Africa to find out whether there was a regional peculiarity in the use of charaktêres that distinguished this area from other parts of the Roman Empire. Two phenomena appear to be more common in North Africa, though they may also occur elsewhere: first, charaktêres as encrypted names, and second, charaktêres as framing devices. First and foremost, though, some introductory remarks concerning charaktêres in general are made.

Environmental Efficiency of Node Cities in Chinese Section of Silk Road Economic Zone and Its Influencing Factors

This paper adopts an environmental data envelopment analysis (DEA) model containing pollution emissions to measure the environmental efficiency of node cities in the Chinese section of Silk Road Economic Zone (SREZ) in 2011–2020 and verifies the convergence of the environmental efficiency. The results show that the ten node cities had an overall low environmental efficiency and a large gap in environmental efficiency, highlighting the necessity of cross-regional cooperation in emission reduction and the promotion of environmental technologies between regions; the environmental efficiency gaps between node cities and between the three regions started to narrow in 2016 and 2018, respectively, showing a certain convergence trend. In addition, the Tobit model was called to analyze the factors affecting environmental efficiency, revealing that per-capita gross domestic product (GDP), foreign trade, and population density promote environmental efficiency, while the proportion of the secondary industry, number of authorized patents, and regional feature significantly suppresses environmental efficiency. Finally, several suggestions were provided to reduce regional pollution emissions and increase China’s environmental efficiency, according to the results of empirical analysis.

Fproi-GAN with Fused Regional Features for the Synthesis of High-Quality Paired Medical Images

The use of medical image synthesis with generative adversarial networks (GAN) is effective for expanding medical samples. The structural consistency between the synthesized and actual image is a key indicator of the quality of the synthesized image, and the region of interest (ROI) of the synthesized image is related to its usability, and these parameters are the two key issues in image synthesis. In this paper, the fusion-ROI patch GAN (Fproi-GAN) model was constructed by incorporating a priori regional feature based on the two-stage cycle consistency mechanism of cycleGAN. This model has improved the tissue contrast of ROI and achieved the pairwise synthesis of high-quality medical images and their corresponding ROIs. The quantitative evaluation results in two publicly available datasets, INbreast and BRATS 2017, show that the synthesized ROI images have a DICE coefficient of 0.981 ± 0.11 and a Hausdorff distance of 4.21 ± 2.84 relative to the original images. The classification experimental results show that the synthesized images can effectively assist in the training of machine learning models, improve the generalization performance of prediction models, and improve the classification accuracy by 4% and sensitivity by 5.3% compared with the cycleGAN method. Hence, the paired medical images synthesized using Fproi-GAN have high quality and structural consistency with real medical images.

Development, characterization, and applications of multi-material stereolithography bioprinting

As a 3D bioprinting technique, hydrogel stereolithography has historically been limited in its ability to capture the spatial heterogeneity that permeates mammalian tissues and dictates structure–function relationships. This limitation stems directly from the difficulty of preventing unwanted material mixing when switching between different liquid bioinks. Accordingly, we present the development, characterization, and application of a multi-material stereolithography bioprinter that provides controlled material selection, yields precise regional feature alignment, and minimizes bioink mixing. Fluorescent tracers were first used to highlight the broad design freedoms afforded by this fabrication strategy, complemented by morphometric image analysis to validate architectural fidelity. To evaluate the bioactivity of printed gels, 344SQ lung adenocarcinoma cells were printed in a 3D core/shell architecture. These cells exhibited native phenotypic behavior as evidenced by apparent proliferation and formation of spherical multicellular aggregates. Cells were also printed as pre-formed multicellular aggregates, which appropriately developed invasive protrusions in response to hTGF-β1. Finally, we constructed a simplified model of intratumoral heterogeneity with two separate sub-populations of 344SQ cells, which together grew over 14 days to form a dense regional interface. Together, these studies highlight the potential of multi-material stereolithography to probe heterotypic interactions between distinct cell types in tissue-specific microenvironments.

Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features

As the capital city of China, Beijing has experienced unprecedented economic and population growth and dramatic impervious surface changes during the last few decades. An application of the classification method combining the spectral and textural features based on Random Forest was conducted to monitor the spatial and temporal changes of Beijing’s impervious surfaces. This classification strategy achieved excellent performance in the impervious surface extraction in complex urban areas, as the Kappa coefficient reached 0.850. Based on this strategy, the impervious surfaces inside Beijing’s sixth ring road in 1997, 2002, 2007, 2013, and 2017 were extracted. As the development of Beijing has a special regional feature, the changes of impervious surfaces within the sixth ring road were assessed. The findings are as follows: (1) the textural features can significantly improve the classification accuracy of land cover in urban areas, especially for the impervious surface with high albedo. (2) Impervious surfaces within the sixth ring road expanded dramatically from 1997 to 2017, had three expanding periods: 1997–2002, 2002–2007, and 2013–2017, and only shrank in 2007–2013. There are different possible major driving factors for each period. (3) The region between the fifth and sixth ring roads in Beijing underwent the most significant changes in the two decades. (4) The inner three regions are relatively highly urbanized areas compared to the outer two regions. Urbanization processes in the interior regions tend to be completed compared to the exterior regions.

Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN

The dominant object detection approaches treat each dataset separately and fit towards a specific domain, which cannot adapt to other domains without extensive retraining. In this paper, we address the problem of designing a universal object detection model that exploits diverse category granularity from multiple domains and predict all kinds of categories in one system. Existing works treat this problem by integrating multiple detection branches upon one shared backbone network. However, this paradigm overlooks the crucial semantic correlations between multiple domains, such as categories hierarchy, visual similarity, and linguistic relationship. To address these drawbacks, we present a novel universal object detector called Universal-RCNN that incorporates graph transfer learning for propagating relevant semantic information across multiple datasets to reach semantic coherency. Specifically, we first generate a global semantic pool by integrating all high-level semantic representation of all the categories. Then an Intra-Domain Reasoning Module learns and propagates the sparse graph representation within one dataset guided by a spatial-aware GCN. Finally, an Inter-Domain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts globally. Extensive experiments demonstrate that the proposed method significantly outperforms multiple-branch models and achieves the state-of-the-art results on multiple object detection benchmarks (mAP: 49.1% on COCO).

Application of 3-D Sensor Tracking Imaging in Detailed Feature Extraction of Motion Damage Action

In order to study the theoretical significance and practical value of the application of three-dimensional sensor tracking imaging in motion damage action detail feature extraction, the key technologies such as accurate segmentation of moving targets, regional feature extraction, target description and robust tracking are studied and discussed. The results show that the robustness of particle filter tracking algorithm in complex scenes can be effectively improved by using the separable feature information of the current tracking scene to track the target. From this it can be seen that the Bandelet transform based on geometric flow divides the image into regions, counts the histogram of the intensity distribution of the strip wave in all directions, and extracts the statistical features. Meanwhile, histogram features are normalized in order to maintain the robustness and selectivity of features.