Efficient Computation of Greyscale Path
Openings

Schubert, Herman R.; Gronde, Jasper J. van de; Roerdink, Jos B. T. M.

doi:10.1515/mathm-2016-0010

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…The main differences compared to our implementation are that we chose to iterate over the grey values from high to low, and that we cannot assume that pixels outside the current upper level set do not participate. Algorithm 8 gives an overview of the most critical section of the algorithm, based on the version of Appleton and Talbot's algorithm presented by [12,26].…”

Section: Greyscale Graphsmentioning

confidence: 99%

“…This can be derived in more or less the same way as for normal path openings [12,26], except that now path weights do not need to be integers, resulting in an upper bound of O(min(|V |, |L|) |V |) (assuming that the grey levels can be sorted in time linear in |L| and that the graph is sparse). Here L is the set of grey levels in the image.…”

Section: Greyscale Graphsmentioning

confidence: 99%

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Efficient and Robust Path Openings Using the Scale-Invariant Rank Operator

2016

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Some basic properties of a slightly generalized version of the scale-invariant rank operator are given, and it is shown how this operator can be used to create a nearly scale-invariant generalization of path openings that is robust to noise. Efficient algorithms are given for sequences and directed acyclic graphs with binary values, as well as sequences with real (greyscale) values. An algorithm is also given for directed acyclic graphs with real weights. It is shown that the given algorithms might be extended even further by allowing for scores based on a totally ordered semigroup.

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Section: Greyscale Graphsmentioning

confidence: 99%

Section: Greyscale Graphsmentioning

confidence: 99%

Efficient and Robust Path Openings Using the Scale-Invariant Rank Operator

2016

Self Cite

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“…High-quality road information updates are important for intelligent urban planning, sustainable urban development, vehicle management, and traffic navigation [17,18]. These algorithms require a high resolution, road curvature, and boundary conditions [19][20][21]. With the availability of increasingly rich street-view map resources [22], which provide new perspectives and research methods, researchers can supplement the details of urban perception from a human perspective [23].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Visual Landscapes on Road Traffic Safety: An Assessment Using Remote Sensing and Deep Learning

Liu,

Gao,

Luo

et al. 2023

Remote Sensing

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Rapid global economic development, population growth, and increased motorization have resulted in significant issues in urban traffic safety. This study explores the intrinsic connections between road environments and driving safety by integrating multiple visual landscape elements. High-resolution remote sensing and street-view images were used as primary data sources to obtain the visual landscape features of an urban expressway. Deep learning semantic segmentation was employed to calculate visual landscape features, and a trend surface fitting model of road landscape features and driver fatigue was established based on experimental data from 30 drivers who completed driving tasks in random order. There were significant spatial variations in the visual landscape of the expressway from the city center to the urban periphery. Heart rate values fluctuated within a range of 0.2% with every 10% change in driving speed and landscape complexity. Specifically, as landscape complexity changed between 5.28 and 8.30, the heart rate fluctuated between 91 and 96. This suggests that a higher degree of landscape richness effectively mitigates increases in driver fatigue and exerts a positive impact on traffic safety. This study provides a reference for quantitative assessment research that combines urban road landscape features and traffic safety using multiple data sources. It may guide the implementation of traffic safety measures during road planning and construction.

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“…These stages include the road extraction stage based on traditional methods and the road extraction stage based on deep learning. The traditional method stage mainly uses template matching approaches, knowledge-driven approaches, object-oriented approaches, path morphology approaches [2], etc. Hu [3] proposed a road extraction algorithm that consists of two steps: detection and pruning.…”

Section: Introductionmentioning

confidence: 99%

C2S-RoadNet: Road Extraction Model with Depth-Wise Separable Convolution and Self-Attention

Yin,

Ren,

Yan

et al. 2023

Remote Sensing

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In order to effectively utilize acquired remote sensing imagery and improve the completeness of information extraction, we propose a new road extraction model called C2S-RoadNet. C2S-RoadNet was designed to enhance the feature extraction capability by combining depth-wise separable convolution with lightweight asymmetric self-attention based on encoder and decoder structures. C2S-RoadNet is able to establish long-distance dependencies and fully utilize global information, and it better extracts road information. Based on the lightweight asymmetric self-attention network, a multi-scale adaptive weight module was designed to aggregate information at different scales. The use of adaptive weights can fully harness features at different scales to improve the model’s extraction performance. The strengthening of backbone information plays an important role in the extraction of road main branch information, which can effectively improve the integrity of road information. Compared with existing deep learning algorithms based on encoder–decoder, experimental results on various public road datasets show that the C2S-RoadNet model can produce more complete road extraction, especially when faced with scenarios involving occluded roads or complex lighting conditions. On the Massachusetts road dataset, the PA, F1 score, and IoU reached 98%, 77%, and 72%, respectively. Furthermore, on the DeepGlobe dataset, the PA, F1 score, and IoU reached 98%, 78%, and 64%, respectively. The objective performance evaluation indicators also significantly improved on the LSRV dataset, and the PA, F1 score, and IoU reached 96%, 82%, and 71%, respectively.

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Efficient Computation of Greyscale Path Openings

Cited by 7 publications

References 17 publications

Efficient and Robust Path Openings Using the Scale-Invariant Rank Operator

Efficient and Robust Path Openings Using the Scale-Invariant Rank Operator

The Influence of Visual Landscapes on Road Traffic Safety: An Assessment Using Remote Sensing and Deep Learning

C2S-RoadNet: Road Extraction Model with Depth-Wise Separable Convolution and Self-Attention

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