Automatic image segmentation based on label propagation

Belizario, Ivar Vargas; Linares, Oscar Cuadros; Neto, João Dias de Toledo Arruda

doi:10.1049/ipr2.12242

Cited by 9 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method initiates with a preliminary segmentation via superpixels, formulates three strategies for label propagation, and leverages color information within the images to distill features, culminating in an autonomous segmentation process. This approach not only reduces the duration required for segmenting images but also elevates the quality of the segmentation 7 . Furthermore, Shen et al brought forth an innovative approach for partitioning daily traffic flows into distinct time slots through clustering.…”

Section: Related Workmentioning

confidence: 99%

Augmented reality navigation method based on image segmentation and sensor tracking registration technology

Zhang,

Zhu,

Chen

et al. 2024

Sci Rep

View full text Add to dashboard Cite

With the rapid development of modern science and technology, navigation technology provides great convenience for people's life, but the problem of inaccurate localization in complex environments has always been a challenge that navigation technology needs to be solved urgently. To address this challenge, this paper proposes an augmented reality navigation method that combines image segmentation and multi-sensor fusion tracking registration. The method optimizes the image processing process through the GA-OTSU-Canny algorithm and combines high-precision multi-sensor information in order to achieve accurate tracking of positioning and guidance in complex environments. Experimental results show that the GA-OTSU-Canny algorithm has a faster image edge segmentation rate, and the fastest start speed is only 1.8 s, and the fastest intersection selection time is 1.2 s. The navigation system combining the image segmentation and sensor tracking and registration techniques has a highly efficient performance in real-world navigation, and its building recognition rates are all above 99%. The augmented reality navigation system not only improves the navigation accuracy in high-rise and urban canyon environments, but also significantly outperforms traditional navigation solutions in terms of navigation startup time and target building recognition accuracy. In summary, this research not only provides a new framework for the theoretical integration of image processing and multi-sensor data, but also brings innovative technical solutions for the development and application of practical navigation systems.

show abstract

Section: Related Workmentioning

confidence: 99%

Augmented reality navigation method based on image segmentation and sensor tracking registration technology

Zhang,

Zhu,

Chen

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In the processing and analysis of digital images, image segmentation (IS) is a key part [ 2 ]. However, effective image segmentation is not easy to realize in adaptive image processing due to insufficient a priori information and imaging noise [ 3 ]. In addition, traditional image segmentation methods usually use manual threshold selection or region growing-based methods, which require a lot of manual intervention and are prone to errors and omissions [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

MCMC algorithm based on Markov random field in image segmentation

Wang,

2024

PLoS ONE

View full text Add to dashboard Cite

In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies.

show abstract

“…Researchers often utilize superpixel segmentation as a preprocessing step for image segmentation tasks. For instance, Belizario et al [24] employed superpixels for pre-segmentation, derived feature matrices based on color information, and proposed an automatic image segmentation method based on weighted recursive label propagation (WRLP), which quantifies the similarity between superpixels by utilizing edge weights. Xiong and Yan [25] developed a novel superpixel merging technique to address the oversegmentation problem in single-frame video sequence images and employed support vector machines (SVMs) for spectral-based superpixel classification.…”

Section: Introductionmentioning

confidence: 99%

Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation

Mu,

Ou,

Chen

et al. 2024

Drones

View full text Add to dashboard Cite

Given the escalating frequency and severity of global forest fires, it is imperative to develop advanced detection and segmentation technologies to mitigate their impact. To address the challenges of these technologies, the development of deep learning-based forest fire surveillance has significantly accelerated. Nevertheless, the integration of graph convolutional networks (GCNs) in forest fire detection remains relatively underexplored. In this context, we introduce a novel superpixel-based graph convolutional network (SCGCN) for forest fire image segmentation. Our proposed method utilizes superpixels to transform images into a graph structure, thereby reinterpreting the image segmentation challenge as a node classification task. Additionally, we transition the spatial graph convolution operation to a GraphSAGE graph convolution mechanism, mitigating the class imbalance issue and enhancing the network’s versatility. We incorporate an innovative loss function to contend with the inconsistencies in pixel dimensions within superpixel clusters. The efficacy of our technique is validated on two different forest fire datasets, demonstrating superior performance compared to four alternative segmentation methodologies.

show abstract

Automatic image segmentation based on label propagation

Cited by 9 publications

References 42 publications

Augmented reality navigation method based on image segmentation and sensor tracking registration technology

Augmented reality navigation method based on image segmentation and sensor tracking registration technology

MCMC algorithm based on Markov random field in image segmentation

Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation

Contact Info

Product

Resources

About