scholarly journals A Measurement of Visual Complexity for Heterogeneity in the Built Environment Based on Fractal Dimension and Its Application in Two Gardens

2021 ◽  
Vol 5 (4) ◽  
pp. 278
Author(s):  
Lan Ma ◽  
Shaoying He ◽  
Mingzhen Lu
Keyword(s):  
Fractal Dimension ◽  
Built Environment ◽  
Evaluation System ◽  
Visual Complexity ◽  
Visual Image ◽  
Human Visual Perception ◽  
Canny Edge Detection ◽  
Image Preprocessing ◽  
Canny Edge ◽  
Visual Characteristics

In this study, a fractal dimension-based method has been developed to compute the visual complexity of the heterogeneity in the built environment. The built environment is a very complex combination, structurally consisting of both natural and artificial elements. Its fractal dimension computation is often disturbed by the homogenous visual redundancy, which is textured but needs less attention to process, so that it leads to a pseudo-evaluation of visual complexity in the built environment. Based on human visual perception, the study developed a method: fractal dimension of heterogeneity in the built environment, which includes Potts segmentation and Canny edge detection as image preprocessing procedure and fractal dimension as computation procedure. This proposed method effectively extracts perceptually meaningful edge structures in the visual image and computes its visual complexity which is consistent with human visual characteristics. In addition, an evaluation system combining the proposed method and the traditional method has been established to classify and assess the visual complexity of the scenario more comprehensively. Two different gardens had been computed and analyzed to demonstrate that the proposed method and the evaluation system provide a robust and accurate way to measure the visual complexity in the built environment.

scholarly journals Determination of the Fractal Dimension of the Fracture Network System Using Image Processing Technique

2019 ◽  
Vol 3 (2) ◽  
pp. 17 ◽  
Author(s):  
Rouhollah Basirat ◽  
Kamran Goshtasbi ◽  
Morteza Ahmadi
Keyword(s):  
Image Processing ◽  
Fractal Dimension ◽  
Edge Detection ◽  
Fracture Network ◽  
Detection Algorithm ◽  
Network System ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
Canny Edge ◽  
Canny Edge Detection Algorithm

Fractal dimension (FD) is a critical parameter in the characterization of a rock fracture network system. This parameter represents the distribution pattern of fractures in rock media. Moreover, it can be used for the modeling of fracture networks when the spatial distribution of fractures is described by the distribution of power law. The main objective of this research is to propose an automatic method to determine the rock mass FD in MATLAB using digital image processing techniques. This method not only accelerates analysis and reduces human error, but also eliminates the access limitation to a rock face. In the proposed method, the intensity of image brightness is corrected using the histogram equalization process and applying smoothing filters to the image followed by revealing the edges using the Canny edge detection algorithm. In the next step, FD is calculated in the program using the box-counting method, which is applied randomly to the pixels detected as fractures. This algorithm was implemented in different geological images to calculate their FDs. The FD of the images was determined using a simple Canny edge detection algorithm, a manual calculation method, and an indirect approach based on spectral decay rate. The results showed that the proposed method is a reliable and fast approach for calculating FD in fractured geological media.

scholarly journals Scale Characteristics and Optimization of Park Green Space in Megacities Based on the Fractal Measurement Model: A Case Study of Beijing, Shanghai, Guangzhou, and Shenzhen

2021 ◽  
Vol 13 (15) ◽  
pp. 8554
Author(s):  
Zhen Li ◽  
Wanmin Zhao ◽  
Miaoyao Nie
Keyword(s):  
Fractal Dimension ◽  
Evaluation System ◽  
Goodness Of Fit ◽  
Green Space ◽  
Fractal Theory ◽  
Measurement Model ◽  
Scale Structure ◽  
Qualitative Description ◽  
Urban Spatial Structure ◽  
Factors Affecting

This paper applies fractal theory to research of green space in megacity parks due to the lack of a sufficient qualitative description of the scale structure of park green space, a quantifiable evaluation system, and operable planning methods in traditional studies. Taking Beijing, Shanghai, Guangzhou, and Shenzhen as examples, GIS spatial analysis technology and the Zipf model are used to calculate the fractal dimension (q), the goodness of fit (R2), and the degree of difference (C) to deeply interpret the connotation of indicators and conduct a comparative analysis between cities to reveal fractal characteristics and laws. The research results show that (1) the fractal dimension is related to the complexity of the park green space system; (2) the fractal dimension characterizes the hierarchical iteration of the park green space to a certain extent and reflects the internal order of the scale distribution; (3) the scale distribution of green space in megacity parks deviates from the ideal pyramid configuration; and (4) there are various factors affecting the scale structure of park green space, such as natural base conditions, urban spatial structure, and the continuation of historical genes working together. On this basis, a series of targeted optimization strategies are proposed.

Application of Improved Canny Edge Detection Algorithm in Coal-Rock Interface Recognition

2012 ◽  
Vol 220-223 ◽  
pp. 1279-1283 ◽  
Author(s):  
Li Hong Dong ◽  
Peng Bing Zhao
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
Canny Edge ◽  
Rock Interface ◽  
Coal Rock ◽  
Image Edge ◽  
Canny Algorithm ◽  
Canny Edge Detection Algorithm

The coal-rock interface recognition is one of the critical automated technologies in the fully mechanized mining face. The poor working conditions underground result in the seriously polluted edge information of the coal-rock interface, which affects the positioning precision of the shearer drum. The Gaussian filter parameters and the high-low thresholds are difficult to select in the traditional Canny algorithm, which causes the information loss of gradual edge and the phenomenon of false edge. Consequently, this paper presents an improved Canny edge detection algorithm, which adopts the adaptive median filtering algorithm to calculate the thresholds of Canny algorithm according to the grayscale mean and variance mean. This algorithm can protect the image edge details better and can restrain the blurred image edge. Experimental results show that this algorithm has improved the edge extraction effect under the case of noise interference and improved the detection precision and accuracy of the coal-rock image effectively.

Canny edge detection enhancement by general auto-regression model and bi-dimensional maximum conditional entropy

Optik ◽  
2014 ◽  
Vol 125 (15) ◽  
pp. 3946-3953 ◽  
Author(s):  
Fei Hao ◽  
Jinfei Shi ◽  
Zhisheng Zhang ◽  
Ruwen Chen ◽  
Songqing Zhu
Keyword(s):  
Edge Detection ◽  
Regression Model ◽  
Conditional Entropy ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Detection Enhancement ◽  
Auto Regression
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