Search Results (6)

Urban shrinkage has gradually become an issue of world-concerning social matter. As urbanization progresses, some Chinese cities are experiencing population loss and economic decline. Our study attempts to correct and integrate DMSP/OLS and NPP/VIIRS data to complete the identification and measurement of shrinking cities in China’s Yangtze River Economic Belt (YREB). We identified 36 shrinking cities and 644 shrinking counties on the municipal and county scales. Based on this approach, we established the average urban shrinkage intensity index and the urban shrinkage frequency index, attempting to find out the causes of shrinking cities for different shrinkage characteristics, city types and shrinkage frequencies. The results show that (1) the shrinking cities are mainly concentrated in the Yangtze River Delta city cluster, the midstream city cluster and the Chengdu–Chongqing economic circle. (2) Most shrinking cities have a moderate frequency of shrinking, dominated by low–low clusters. Resource-based, heavy industrial, small and medium-sized cities are more inclined to shrink. (3) The single economic structure, the difficulty of industrial transformation and the lack of linkage among county-level cities are possible reasons for the urban shrinkage in the YREB. Exploring the causes of urban shrinkage from a more micro perspective will be an inevitable task for sustainable development in YREB and even in China. Full article

Multifocal glasses are a new type of lens that can fit both nearsighted and farsighted vision on the same lens. This property allows the glass to have various curvatures in distinct regions within the glass during the grinding process. However, when the curvature varies irregularly, the glass is prone to optical deformation during imaging. Most of the previous studies on imaging deformation focus on the deformation correction of optical lenses. Consequently, this research uses an automatic deformation defect detection system for multifocal glasses to replace professional assessors. To quantify the grade of deformation of curved multifocal glasses, we first digitally imaged a pattern of concentric circles through a test glass to generate an imaged image of the glass. Second, we preprocess the image to enhance the clarity of the concentric circles’ appearance. A centroid-radius model is used to represent the form variation properties of every circle in the processed image. Third, the deviation of the centroid radius for detecting deformation defects is found by a slight deviation control scheme, and we gain a difference image indicating the detected deformed regions after comparing it with the norm pattern. Fourth, based on the deformation measure and occurrence location of multifocal glasses, we build fuzzy membership functions and inference regulations to quantify the deformation’s severity. Finally, a mixed model incorporating a network-based fuzzy inference and a genetic algorithm is applied to determine a quality grade for the deformation severity of detected defects. Testing outcomes show that the proposed methods attain a 94% accuracy rate of the quality levels for deformation severity, an 81% recall rate of deformation defects, and an 11% false positive rate for multifocal glass detection. This research contributes solutions to the problems of imaging deformation inspection and provides computer-aided systems for determining quality levels that meet the demands of inspection and quality control. Full article

In the past ten years, multimodal image registration technology has been continuously developed, and a large number of researchers have paid attention to the problem of infrared and visible image registration. Due to the differences in grayscale distribution, resolution and viewpoint between two images, most of the existing infrared and visible image registration methods are still insufficient in accuracy. To solve such problems, we propose a new robust and accurate infrared and visible image registration method. For the purpose of generating more robust feature descriptors, we propose to generate feature descriptors using a concentric-circle-based feature-description algorithm. The method enhances the description of the main direction of feature points by introducing centroids, and, at the same time, uses concentric circles to ensure the rotation invariance of feature descriptors. To match feature points quickly and accurately, we propose a multi-level feature-matching algorithm using improved offset consistency for matching feature points. We redesigned the matching algorithm based on the offset consistency principle. The comparison experiments with several other state-of-the-art registration methods in CVC and homemade datasets show that our proposed method has significant advantages in both feature-point localization accuracy and correct matching rate. Full article

Spectroradiometers exhibit the smallest aberration and the optimum response at the field-of-view (FOV) center. The aberration increases and the response deteriorates at positions further away from the FOV center, which leads to nonuniformity in the spectroradiometer FOV. In this study, a concentric-circles method for correcting the spectroradiometer FOV nonuniformity was developed. The calibration experiment for FOV nonuniformity was conducted by establishing the experimental platform. The nonuniformity correction coefficients were obtained and then used to fit the correction function curve within the whole FOV, allowing for correction of measurement targets with an arbitrary shape. The radiation intensity of the blackbody at different temperatures was obtained by measurement, and the nonuniformity coefficient was used to correct it. After correction, the error was within 1.84% for the spectrally integrated radiant intensity in the non-absorption band. Using this correction method, efficient calibration of spectroradiometer nonuniformity can be achieved, thereby enhancing the measurement accuracy of the spectroradiometer. Full article

Environmental changes in national parks are generally subject to constant observation. A particular case is parks located in mountains, which are more vulnerable to climate change and the binding of pollutants in mountain ranges as orographic barriers. The effectiveness of forest soil monitoring networks based on a systematic grid with a predetermined density has not been analysed so far. This study’s analysis was conducted in the Stolowe Mountains National Park (SMNP), SW Poland, using total Pb concentration data obtained from an initial network of 403 circle plots with centroids arranged in a regular 400 × 400 m square grid. The number and distribution of monitoring plots were analysed using geostatistical tools in terms of the accuracy and correctness of soil parameters obtained from spatial distribution imaging. The analysis also aimed at reducing the number of monitoring plots taking into account the economic and logistic aspects of the monitoring investigations in order to improve sampling efficiency in subsequent studies in the SMNP. The concept of the evaluation and modification of the monitoring network presented in this paper is an original solution and included first the reduction and then the extension of plot numbers. Two variants of reduced monitoring networks, constructed using the proposed procedure, allowed us to develop the correct geostatistical models, which were characterised by a slightly worse mean standardised error (MSE) and root mean squared error (RMSE) compared to errors from the original, regular monitoring network. Based on the new geostatistical models, the prediction of Pb concentration in soils in the reduced grids changed the spatial proportions of areas in different pollution classes to a limited extent compared to the original network. Full article

Coded targets have been demarcated as control points in various vision measurement tasks such as camera calibration, 3D reconstruction, pose estimation, etc. By employing coded targets, matching corresponding image points in multi images can be automatically realized which greatly improves the efficiency and accuracy of the measurement. Although the coded targets are well applied, particularly in the industrial vision system, the design of coded targets and its detection algorithms have encountered difficulties, especially under the conditions of poor illumination and flat viewing angle. This paper presents a novel concentric circular coded target (CCCT), and its positioning and identifying algorithms. The eccentricity error has been corrected based on a practical error-compensation model. Adaptive brightness adjustment has been employed to address the problems of poor illumination such as overexposure and underexposure. The robust recognition is realized by perspective correction based on four vertices of the background area in the CCCT local image. The simulation results indicate that the eccentricity errors of the larger and smaller circles at a large viewing angle of 70° are reduced by 95% and 77% after correction by the proposed method. The result of the wing deformation experiment demonstrates that the error of the vision method based on the corrected center is reduced by up to 18.54% compared with the vision method based on only the ellipse center when the wing is loaded with a weight of 6 kg. The proposed design is highly applicable, and its detection algorithms can achieve accurate positioning and robust identification even in challenging environments. Full article