Extraction of Road Centrelines and Edge Lines from High-Resolution Satellite Imagery using Density-Oriented Fuzzy C-Means and Mathematical Morphology

Salah, Mahmoud

doi:10.1007/s12524-022-01507-w

Cited by 8 publications

(7 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both the opening operation and closing operation are compound operations, in which the opening operation conducts the erosion and then dilation operation while the closing operation is on the contrary. Equations (30) and (31) define these two operations, 42 where I is the original image and S represents the structuring element. …”

Section: Methodsmentioning

confidence: 99%

Fabric defect detection using cartoon–texture image decomposition model and visual saliency method

Zhu

Xin

Deng

et al. 2023

Textile Research Journal

View full text Add to dashboard Cite

Fabric defect detection is of great importance in the modern textile industry. In actual production, subjective factors usually affect manual detection, which is prone to problems such as false and missed detection. With the development of computer vision, a large number of fabric defect automatic detection algorithms have been proposed. Traditional algorithms rely too heavily on setting parameters manually, while deep learning algorithms have expensive training and computing costs. Given these limitations, a new fabric defect detection method based on the latest cartoon texture image decomposition model, visual saliency algorithm, and mathematical morphology, is proposed in this article. A digital image acquisition system is also designed and constructed. Therefore, the self-made dataset used in the experiment is composed of self-collected images and network public images. To further evaluate the performance of the proposed method, this study conducted fabric defect detection experiments and comparison experiments based on the self-made dataset. The results show that this method can successfully detect fabric defects, having high detection accuracy and efficiency. Combining subjective vision and objective evaluation, comparison experiments prove that this method is superior to other common fabric defect detection methods and has the highest value of accuracy and F1-score. This research provides a new method and technical support for fabric defect detection and other fields.

show abstract

Section: Methodsmentioning

confidence: 99%

Fabric defect detection using cartoon–texture image decomposition model and visual saliency method

Zhu

Xin

Deng

et al. 2023

Textile Research Journal

View full text Add to dashboard Cite

show abstract

“…During the image processing process (including image segmentation, edge extraction, etc. ), clustering is often used as a preprocessing process, while the FCM algorithm (Algorithm 2) is the clustering algorithm used more [38][39][40][41]. The FCM algorithm was first developed by Dunn in 1973 [38], and it later was improved by Bezdek [39] and widely used in pattern recognition [40,41].…”

Section: Definition 6 ([29]mentioning

confidence: 99%

“…), clustering is often used as a preprocessing process, while the FCM algorithm (Algorithm 2) is the clustering algorithm used more [38][39][40][41]. The FCM algorithm was first developed by Dunn in 1973 [38], and it later was improved by Bezdek [39] and widely used in pattern recognition [40,41]. Unlike K-means hard clustering (in the K-means algorithm, each element can only belong to one cluster), the FCM algorithm is a soft clustering idea, which believes that all objects in the cluster object set belong to a cluster with different membership degrees.…”

Section: Definition 6 ([29]mentioning

confidence: 99%

Overlap Functions-Based Fuzzy Mathematical Morphological Operators and Their Applications in Image Edge Extraction

Zhang

Liu

2023

Fractal Fract

View full text Add to dashboard Cite

As special aggregation functions, overlap functions have been widely used in the soft computing field. In this work, with the aid of overlap functions, two new groups of fuzzy mathematical morphology (FMM) operators were proposed and applied to image processing, and they obtained better results than existing algorithms. First, based on overlap functions and structuring elements, the first group of new FMM operators (called OSFMM operators) was proposed, and their properties were systematically analyzed. With the implementation of OSFMM operators and the fuzzy C-means (FCM) algorithm, a new image edge extraction algorithm (called the OS-FCM algorithm) was proposed. Then, the second group of new FMM operators (called ORFMM operators) was proposed based on overlap functions and fuzzy relations. Another new image edge extraction algorithm (called OR-FCM algorithm) was proposed by using ORFMM operators and FCM algorithm. Finally, through the edge segmentation experiments of multiple standard images, the actual segmentation effects of the above-mentioned two algorithms and relevant algorithms were compared. The acquired results demonstrate that the image edge extraction algorithms proposed in this work can extract the complete edge of foreground objects on the basis of introducing the least noise.

show abstract

“…Road extraction from high-resolution remote sensing images involves two main aspects: (1) the extraction of road regions [4][5][6] and (2) the extraction of road centerlines [7][8][9][10]. The road centerline is typically extracted from the road region using algorithms, such as morphological refinement, in digital image processing technology [11]. This process is straightforward and can yield satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

Road-MobileSeg: Lightweight and Accurate Road Extraction Model from Remote Sensing Images for Mobile Devices

Qu,

Wu,

et al. 2024

Sensors

View full text Add to dashboard Cite

Current road extraction models from remote sensing images based on deep learning are computationally demanding and memory-intensive because of their high model complexity, making them impractical for mobile devices. This study aimed to develop a lightweight and accurate road extraction model, called Road-MobileSeg, to address the problem of automatically extracting roads from remote sensing images on mobile devices. The Road-MobileFormer was designed as the backbone structure of Road-MobileSeg. In the Road-MobileFormer, the Coordinate Attention Module was incorporated to encode both channel relationships and long-range dependencies with precise position information for the purpose of enhancing the accuracy of road extraction. Additionally, the Micro Token Pyramid Module was introduced to decrease the number of parameters and computations required by the model, rendering it more lightweight. Moreover, three model structures, namely Road-MobileSeg-Tiny, Road-MobileSeg-Small, and Road-MobileSeg-Base, which share a common foundational structure but differ in the quantity of parameters and computations, were developed. These models varied in complexity and were available for use on mobile devices with different memory capacities and computing power. The experimental results demonstrate that the proposed models outperform the compared typical models in terms of accuracy, lightweight structure, and latency and achieve high accuracy and low latency on mobile devices. This indicates that the models that integrate with the Coordinate Attention Module and the Micro Token Pyramid Module surpass the limitations of current research and are suitable for road extraction from remote sensing images on mobile devices.

show abstract

Extraction of Road Centrelines and Edge Lines from High-Resolution Satellite Imagery using Density-Oriented Fuzzy C-Means and Mathematical Morphology

Cited by 8 publications

References 50 publications

Fabric defect detection using cartoon–texture image decomposition model and visual saliency method

Fabric defect detection using cartoon–texture image decomposition model and visual saliency method

Overlap Functions-Based Fuzzy Mathematical Morphological Operators and Their Applications in Image Edge Extraction

Road-MobileSeg: Lightweight and Accurate Road Extraction Model from Remote Sensing Images for Mobile Devices

Contact Info

Product

Resources

About