An image-based shape analysis approach and its application to young women's waist-hip-leg position.

Digital image processing has been widely used for researches in the fashion industry. This study presented a method to classify women's waist-hip-leg position based on body images. 135 healthy female students were selected as the experimental subjects, and then photo and 3D body measuring methods were used to obtain 40 shape parameters. Through factor analysis, five factors were extracted for the waist-hip position and eight factors for the leg position. The waist-hip and leg were separately classified into four categories after clustering analysis with optimised factors. The distribution of waist-hip-leg shape was analysed by combining the waist-hip and leg classification results. The results showed that 12.31% of the subjects had prominent abdomens, flat buttocks, and round and thin legs. The landmarks and parameters were automatically extracted for waist-hip-leg shape identification. The image-based shape analysis approach (ISA) was finally verified with an accuracy rate of over 90% with 30 new subjects.Practitioner summary: This study will propose an image-based shape analysis approach (ISA) to realise the quick automatic shape identification of the waist-hip-leg position based on body images. In addition, the method can be applied to pants' pattern alteration for different body types by analysing the relationship between the waist-hip-leg shape and pants.

Semantic segmentation of UAV remote sensing images based on edge feature fusing and multi-level upsampling integrated with Deeplabv3.

Deeplabv3+ currently is the most representative semantic segmentation model. However, Deeplabv3+ tends to ignore targets of small size and usually fails to identify precise segmentation boundaries in the UAV remote sensing image segmentation task. To handle these problems, this paper proposes a semantic segmentation algorithm of UAV remote sensing images based on edge feature fusing and multi-level upsampling integrated with Deeplabv3+ (EMNet). EMNet uses MobileNetV2 as its backbone and adds an edge detection branch in the encoder to provide edge information for semantic segmentation. In the decoder, a multi-level upsampling method is designed to retain high-level semantic information (e.g., the target's location and boundary information). The experimental results show that the mIoU and mPA of EMNet improved over Deeplabv3+ by 7.11% and 6.93% on the dataset UAVid, and by 0.52% and 0.22% on the dataset ISPRS Vaihingen.

Students' knowledge of and conservation attitude toward the black-necked crane (Grus nigricollis) in Guizhou, China: insights for conservation.

BACKGROUND: The persistence of threatened and protected wildlife depends not only on habitat suitability but also remarkably on local communities' acceptance. The black-necked crane (Grus nigricollis) is a flagship species for conservation on the plateau in western China. However, the human dimension has been completely ignored in the decision-making process for conservation. METHODS: In this study, a questionnaire survey aiming to assess knowledge of and conservation attitude toward this bird was carried out among 1042 students of 7th and 9th grade from 12 schools in Weining county, Guizhou province, which has a large wintering population in an urban wetland. Logistic regression was used in the generalized linear model to identify the determinants that significantly affect students' knowledge of and conservation attitude toward this species. RESULTS: Most students have positive attitudes toward conservation, which is significantly affected by awareness, knowledge of this bird and grade. However, they have somewhat limited knowledge of this bird's biology and ecosystem services (nature's contributions to people). Knowledge was found to link with observation of the bird and grade, while observation related to the experience of visiting the wetland. Social media is the most cited resource to obtain knowledge on this bird. CONCLUSION: It is suggested that local conservation experts could help introduce more information on the black-necked crane in the schools and help conduct outdoor education activities in and around the wetland. Traditional knowledge and culture could also be incorporated into the conservation awareness enhancing program. This study focuses on the human dimension for conserving the black-necked crane in China, showing significant implications in the design and application of effective measurements to improve students' perception and attitude toward its conservation. Future assessments should include other local populations, such as farmers, fishers, and urban citizens.

Research on Spatiotemporal Association between Tourism and Transportation Based on CGS Model.

Tourism and transportation generally have an inseparable association. However, there are still many limitations in the existing research on it. For example, most scholars only adopt one single model method, which fails to consider geospatial elements. Moreover, some researchers simply use socioeconomic data for analysis and research and ignore the solid spatial characteristics between tourism and transportation, which leads to deviations in the results. To solve these problems, this article proposed a spatiotemporal association model by comprehensively using coupling coordination degree, gravity center model, and spatial coincidence degree. Based on the tourism economic and attraction spatial data, and the transportation and its network spatial data, the association between tourism and transportation can be revealed by the proposed model. This study conducted a quantitative analysis on the tourism and transportation industry in Jiangxi Province, China, from 2005 to 2019, and the results show that: (1) the coupling coordination degree of tourism and transportation increases year by year; (2) the change in gravity center of tourism and transportation is subtle. The mean value of spatial overlap is 80.33 km, while the mean value of inter-annual variation consistency is 0.56; (3) the spatial coincidence degree of tourism and transportation in Jiangxi Province indicates a steady upward trend and reaches 0.78 in 2019; and (4) based on the evolution trend in the coupling coordination degree, gravity center coupling model, and spatial coincidence degree of tourism and transportation, it can be seen that the slopes of their trend functions are similar and consistent-the slopes are 0.0239, 0.0253, and 0.0319, respectively-and the standard deviation of the slopes of the three is only 0.000018.

Automatic morphological filtering algorithm for airborne lidar data in urban areas.

Filtering is a key step for most airborne lidar post-applications in urban areas. To solve the problems of complex parameter settings and low filtering accuracy in complicated urban environments, an automatic morphological filter was proposed. In this paper, the optimal maximum filtering window can be determined automatically by applying a series of morphological top-hat operations. Meanwhile, the thresholds for filtering were calculated adaptively according to the gradient changes. Seven publicly available data sets provided by the International Society for Photogrammetry and Remote Sensing were used to evaluate the performance. Experimental results show that the proposed method achieved an average total error of 4.07% and an average kappa coefficient of 90.90%, which are the best performances when compared to some other filtering methods.

Combinational reasoning of quantitative fuzzy topological relations for simple fuzzy regions.

In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS) and Artificial Intelligence (AI). Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1) 23 different topological relations between a simple crisp region and a simple fuzzy region; (2) 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models.