A Fast Image Segmentation Algorithm Based on Saliency Map and Neutrosophic Set Theory

Song, Shuli; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

doi:10.1109/jphot.2020.3026973

Cited by 10 publications

(3 citation statements)

References 38 publications

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“…In addition, sports learning researchers mainly analyze the ideal feedback timing, feedback form, and feedback frequency in feedback training to provide an ideal operation scheme for the digitization of sports training [16]. is algorithm is developed according to the concept of change detection, which is different from the other change detection methods [17]. e video segmentation algorithm does not use the difference between the two consecutive images to judge motion.…”

Section: Related Workmentioning

confidence: 99%

A Sports Digital Training System Based on Middle and Bottom Visual Information

Luo¹

2022

Mobile Information Systems

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Physical exercise has become increasingly popular in China in recent years, and the number of individuals participating in physical activities is growing year after year. Therefore, they are facing the problem of a serious shortage of sports resources, especially in the central and western regions where a complete physical education teaching system has not been established yet. The introduction of sports digital training alleviates this problem to a great extent. This approach uses advanced computer technology for digital training, collects people’s sports information, and formulates suitable sports programs for different groups. The system can collect people’s action videos at the same time by two cameras, analyze the athletes’ actions from the side and face by using the software, and compare the professional athletes’ actions with the collected video action for in-depth analysis. The sports digital training system proposed in this study focuses on collecting the bottom visual information of sports. The visual information in the middle and low levels is mainly used to describe the content of video segments, i.e., key primitives, including motion trajectory, motion action, and tracking motion, and establishes a sports digital training system based on the bottom visual information. In addition to the completion of basic functions of a sports digital training system, the system also adds the functions of video segmentation, action freezing, speed calculation, table display, local locking, and tracking, which can accurately collect the bottom visual information of people’s sports and realizes the digital sports training.

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Section: Related Workmentioning

confidence: 99%

A Sports Digital Training System Based on Middle and Bottom Visual Information

Luo¹

2022

Mobile Information Systems

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“…Yuan [2] proposed a new image segmentation method based on INS, and the experimental results showed that it achieved higher PSNR values and performed better than the k-means clustering algorithm. Song [3] proposed a fast image segmentation method that combines a saliency map with NS (SMNS) to achieve higher accuracy. The SMNS method can effectively solve the issues of under-segmentation and over-segmentation, and performs well in the presence of salt and pepper noise, Gaussian white noise, and mixed noise.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Global and Adaptive Thresholding for Biometric Images Based on Neutrosophic Overset and Underset Approaches

Dhatchinamoorthy,

Devarasan

2023

Symmetry

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The study introduces a new threshold method based on a neutrosophic set. The proposal applies the neutrosophic overset and underset concepts for thresholding the image. The global threshold method and the adaptive threshold method were used as the two types of thresholding methods in this article. Images could be symmetrical or asymmetrical in professional disciplines; the government maintains facial image databases as symmetrical. General-purpose images do not need to be symmetrical. Therefore, it is essential to know how thresholding functions in both scenarios. Since the article focuses on biometric image data, face and fingerprint data were considered for the analysis. The proposal provides six techniques for the global threshold method based on neutrosophic membership, indicating neutrosophic TF overset (NOTF), neutrosophic TI overset (NOTI), neutrosophic TIF overset (NOTIF), neutrosophic TF underset (NUTF), neutrosophic TI underset (NUTI), neutrosophic TIF underset (NUTIF); similarly, in this study, the researchers generated six novel approaches for the adaptive method. These techniques involved an investigation using biometric data, such as fingerprints and facial images. The achievement was 98% accurate for facial image data and 100% accurate for fingerprint data.

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“…In general, the perception of the fuzzy-based approaches is generalized by the neutrosophic set. It includes the fuzzy set and intuitionistic fuzzy set [ 29 ]. In the NS domain, Guo and Cheng introduced fuzzy c-means clustering, and the indeterminacy of the image is calculated by the entropy.…”

Section: Introductionmentioning

confidence: 99%

NeDSeM: Neutrosophy Domain-Based Segmentation Method for Malignant Melanoma Images

Bian

Pan

Zhang

et al. 2022

Entropy

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Skin lesion segmentation is the first and indispensable step of malignant melanoma recognition and diagnosis. At present, most of the existing skin lesions segmentation techniques often used traditional methods like optimum thresholding, etc., and deep learning methods like U-net, etc. However, the edges of skin lesions in malignant melanoma images are gradually changed in color, and this change is nonlinear. The existing methods can not effectively distinguish banded edges between lesion areas and healthy skin areas well. Aiming at the uncertainty and fuzziness of banded edges, the neutrosophic set theory is used in this paper which is better than fuzzy theory to deal with banded edge segmentation. Therefore, we proposed a neutrosophy domain-based segmentation method that contains six steps. Firstly, an image is converted into three channels and the pixel matrix of each channel is obtained. Secondly, the pixel matrixes are converted into Neutrosophic Set domain by using the neutrosophic set conversion method to express the uncertainty and fuzziness of banded edges of malignant melanoma images. Thirdly, a new Neutrosophic Entropy model is proposed to combine the three memberships according to some rules by using the transformations in the neutrosophic space to comprehensively express three memberships and highlight the banded edges of the images. Fourthly, the feature augment method is established by the difference of three components. Fifthly, the dilation is used on the neutrosophic entropy matrixes to fill in the noise region. Finally, the image that is represented by transformed matrix is segmented by the Hierarchical Gaussian Mixture Model clustering method to obtain the banded edge of the image. Qualitative and quantitative experiments are performed on malignant melanoma image dataset to evaluate the performance of the NeDSeM method. Compared with some state-of-the-art methods, our method has achieved good results in terms of performance and accuracy.

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A Fast Image Segmentation Algorithm Based on Saliency Map and Neutrosophic Set Theory

Cited by 10 publications

References 38 publications

A Sports Digital Training System Based on Middle and Bottom Visual Information

A Sports Digital Training System Based on Middle and Bottom Visual Information

An Analysis of Global and Adaptive Thresholding for Biometric Images Based on Neutrosophic Overset and Underset Approaches

NeDSeM: Neutrosophy Domain-Based Segmentation Method for Malignant Melanoma Images

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