A fast level set image segmentation driven by a new region descriptor

Birane, Abdelkader; Hamami, Latifa

doi:10.1049/ipr2.12036

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…Then the color space of the image needs to be transformed to extract its color space channels. Then, the GA-OTSU method and Canny method are used for image thresholding and Canny edge extraction, respectively 15 . The images after threshold segmentation and edge extraction are fused, and the closed operation is performed on them, and then the processed images are obtained.…”

Section: Ar Navigation Methods Based On Is-tech and Sensor T And R-techmentioning

confidence: 99%

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

Zhang,

Zhu,

Chen

et al. 2024

Sci Rep

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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.

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Section: Ar Navigation Methods Based On Is-tech and Sensor T And R-techmentioning

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

“…[28–30]. In recent years, there are also many improved level set methods for image segmentation, such as the fast level set image segmentation method based on the new region description [31], variational level set image segmentation model combined with kernel distance function [32], level set image segmentation method based on cloud model as priori contour [33]. There are some other improvements and methods applied to different images [34–38].…”

Section: Related Workmentioning

confidence: 99%

AXanthoceras sorbifoliumcrack segmentation method based on an improved level set

Zhang

Chen

et al. 2023

IET Image Processing

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The dehiscence of the Xanthoceras sorbifolium (X. sorbifolium) may lead to seeds jump out and economic loss. The shape and the degree of the crack will provide the relevant elements for the study of the X. sorbifolium dehiscence and duly picked. Therefore, an improved level set method is proposed for X. sorbifolium crack segmentation. The problems of intensity inhomogeneity and so on, which pose challenges for accurate crack segmentation. The local Gaussian distribution fitting method has a good segmentation effect, but it is sensitive to the initial contour and does not use gradient information, which affects the accurate location of the edge. Aiming at the above problems and the scene of crack segmentation, this paper firstly adopts histogram threshold method to obtain the initial contour automatically. Secondly, the energy function is constructed by combining local and edge information. Finally, the double‐well potential function is used to reduce the oscillation and distortion of the method. In this paper, the experiment results show that the average boundary precision is 88.25% and average segmentation time of each image is 12.7s of the proposed method. After comprehensive analysis of IoU and boundary recall, the method in this paper achieves better results.

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“…Birane Abdelkader et al have developed a new level set function to implement a fast and robust active contour model. The model was built by combining the sbgfrls model and the Legendre polynomial, and instead of using the average intensity of the region, the level set function was regularized using a Gaussian filtering process [ 9 ]. Shamsi Koshki Asma et al proposed a new local region based active contour model, the local self-weighted active contour model.…”

Section: Introductionmentioning

confidence: 99%

Research on improved level set image segmentation method

et al. 2023

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Aiming at the shortcomings of the traditional level set model which only has good robustness to the weak boundary and strong noise of the original target image, this paper proposes an improved algorithm based on the no-weight initialization level set model, introducing bilateral filters and using implicit surface level sets to extract and segment the original target image object more accurately, clearly and intuitively in the evolution process. The experimental simulation results show that, compared with the traditional non-reinitialized level set model segmentation method, the improved method can more accurately extract the edge contours of the target image object, and has better edge contour extraction effect, and the original target noise reduction effect of the improved model is better than that of the model before the improvement. The original target image object edge contour takes less time to extract than the conventional non-reinitialized level set model before the improvement.

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A fast level set image segmentation driven by a new region descriptor

Cited by 5 publications

References 29 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

AXanthoceras sorbifoliumcrack segmentation method based on an improved level set

Research on improved level set image segmentation method

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