Learning visual features from figure-ground maps for urban morphology discovery

Wang, Jing; Huang, Weiming; Biljecki, Filip

doi:10.1016/j.compenvurbsys.2024.102076

Cited by 3 publications

(1 citation statement)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several quantification metrics have been proposed to describe the intrinsic shape or alignment characteristics with similar invariance, ensuring consistent depiction of shape or algnment features of the same object from different perspectives. These metrics have found wide applications in various domains such as image recognition [ 23 , 24 ] and road landscape feature analysis [ 25 ], yielding favorable outcomes. These studies have provided insights into methods for extracting alignment parameters from perspective views.…”

Section: Introductionmentioning

confidence: 99%

Exploring road safety using alignment perspective features in real driving images: A case study on mountain freeways

He,

Fu,

Wang

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Introduction While driving, drivers frequently adapt their driving behaviors according to their perception of the road’s alignment features. However, traditional two-dimensional alignment methods lack the ability to capture these features from the driver’s perspective. Method This study introduces a novel method for road alignment recognition, employing image recognition technology to extract alignment perspective features, namely alignment perspective skewness (APS) and alignment perspective kurtosis (APK), from in-real driving images. Subsequently, the K-means clustering algorithm is utilized for road segment classification based on APS and APK indicators. Various sliding step length for clustering are employed, with step length ranging from 100m to 400m. Furthermore, the accident rates for different segment clusters are analyzed to explore the relationship between alignment perspective features and traffic safety. A 150 km mountain road section of the Erlianhaote-Guangzhou freewway from Huaiji to Sihui is selected as a case study. Results The results demonstrate that using alignment perspective features as classification criteria produces favorable clustering outcomes, with superior clustering performance achieved using shorter segment lengths and fewer cluster centers. The road segment classification based on alignment perspective features reveals notable differences in accident rates across categories; while traditional two-dimensional parameters-based classification methods fail to capture these differences. The most significant differences in accident rates across categories are observed with segment length of 100m, with the significance gradually diminishing as segment length increases and disappearing entirely when the length exceeds 300m. Implication These findings validate the reliability of using alignment perspective features (APS and APK) for road alignment classification and road safety analysis, providing valuable insights for road safety management.

show abstract

Section: Introductionmentioning

confidence: 99%

Exploring road safety using alignment perspective features in real driving images: A case study on mountain freeways

He,

Fu,

Wang

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

Quantifying spatial patterns of urban building morphology in the China’s Guangdong-Hong Kong-Marco greater bay area

Wu,

Huang,

Wang

2024

International Journal of Digital Earth

View full text Add to dashboard Cite

Visual Feature Extraction and Tracking Method Based on Corner Flow Detection

Li,

Wang,

et al. 2024

IECE trans. intell. syst.

View full text Add to dashboard Cite

Frontend feature tracking based on vision is the process in which a robot captures images of its surrounding environment using a camera while in motion. Each frame of the image is then analyzed to extract feature points, which are subsequently matched between pairwise frames to estimate the robot’s pose changes by solving for the variations in these points. While feature matching methods that rely on descriptor-based approaches perform well in cases of significant lighting and texture variations, the addition of descriptors increases computational costs and introduces instability. Therefore, in this paper, a novel approach is proposed that combines sparse optical flow tracking with Shi-Tomasi corner detection, replacing the use of descriptors. This new method offers improved stability in situations of challenging lighting and texture variations while maintaining lower computational costs. Experimental results, validated using the OpenCV library on the Ubuntu operating system, provide evidence of the algorithm’s effectiveness and efficiency.

show abstract

Learning visual features from figure-ground maps for urban morphology discovery

Cited by 3 publications

References 49 publications

Exploring road safety using alignment perspective features in real driving images: A case study on mountain freeways

Exploring road safety using alignment perspective features in real driving images: A case study on mountain freeways

Quantifying spatial patterns of urban building morphology in the China’s Guangdong-Hong Kong-Marco greater bay area

Visual Feature Extraction and Tracking Method Based on Corner Flow Detection

Contact Info

Product

Resources

About