Toward a Computer Vision Perspective on the Visual Impact of Vegetation in Symmetries of Urban Environments

Abstract

Rapid urbanization is a worldwide critical environmental challenge. With this urban migration soaring, we need to live far more efficiently than we currently do by incorporating the natural world in new and innovative ways. There are a lot of researches on ecological, architectural or aesthetic points of view to address this issue. We present a novel approach to assess the visual impact of vegetation in urban street pedestrian view with the assistance of computer vision metrics. We statistically evaluate the correlations of the amount of vegetation with objective computer vision traits such as Fourier domain, color histogram, and estimated depth from monocular view. We show that increasing vegetation in urban street views breaks the orthogonal symmetries of urban blocks, enriches the color space with fractal-like symmetries and decreases the cues of projective geometry in depth. These uncovered statistical facts are applied to predict the requested amount of vegetation to make urban street views appear like natural images. Interestingly, these amounts are found in accordance with the ecosystemic approach for urban planning. Also, the study opens new questions for the understanding of the link between geometry and depth perception.

Multi-Phase Information Theory-Based Algorithm for Edge Detection of Aerial Images

“…Aerial imagery is diverse when it comes to texture as it has many kinds such as buildings, roads, lands, lakes, rivers, and so forth (Paravolidakis et al, 2018). Numerous applications are required to process grayscale and color images with the aforesaid areas, and detecting the edge of such images is required in many such applications (Aamir et al, 2019;Li & Wu, 2008;Paravolidakis et al, 2018;Samiei et al, 2018;). For instance, in image segmentation for boundary detection, coastlines extraction between sea and land regions in aerial images, detection of buildings, content-based image retrieval, and other machine vision routines (Stoian et al, 2019).…”

Algorithm Selection for Edge Detection in Satellite Images by Neutrosophic WASPAS Method

“…Satellite imagery has a wide variety of texture types that represent different target areas: roads, rivers, lakes, land, vegetation, buildings, and more [2]. Many applications for sustainable project management aim to distinguish these areas in grayscale and/or color images, and edge detection has been used as the pre-processing step before the other subsequent stages of data processing [2][3][4][5].…”

“…A particular algorithm responds differently to the given image content, and results are different for the chosen image. Edge detection algorithms have been used in boundary detection, in image segmentation as a pre-processing step, for example, to extract coastlines between regions of land and sea in aerial photos [2], in content-based image retrieval (CBIR) [10], building detection in low contrast satellite images [6] as well as others computer vision-based applications [4,5]. Edge detectors significantly reduce the amount of image data and provide the necessary structural information about objects and regions in the satellite image.…”