Teeth Category Classification via Hu Moment Invariant and Extreme Learning Machine

Li, Zhi; Guo, Ting; Bao, Fangzhou; Payne, Rodney

doi:10.2991/cmsa-18.2018.51

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…ELM generates prediction output with a 0.0426 low error rate. Combining the Hu moment invariant (HMI) method with ELM, [ 47 ] devised and implemented a unique classification technique for CBCT images of teeth. The results showed that the devised methodology is better compared to a statistically significant ANN.…”

Section: Resultsmentioning

confidence: 99%

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

et al. 2022

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Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field.

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Section: Resultsmentioning

confidence: 99%

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

et al. 2022

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Research on Low-Resolution Pattern Coding Recognition Method Based on Hu-DBN

Zhang

Xiao

et al. 2020

西北工业大学学报

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The feature image code represented by the two-dimensional code is the key reference for global positioning in the visual navigation of mobile robots. Although reducing the acquired low-resolution image helps to reduce the real-time performance of the algorithm, the acquired feature image is more susceptible to motion blur-based interference and affects the accuracy of recognition, which causes the positioning failure of the whole multi-intelligence, in which the body control system is invalid. In this paper, an optimized low-resolution feature image code recognition method is proposed. In the preprocessing part, the characteristic image is converted into the characteristic signal matrix of Hu invariant moments, and then the characteristic image is added to the characteristic signal matrix as a characteristic component, and then the Hu-DBN neural network signal classifier is used to construct the signal matrix so as to achieve accurate recognition of low-resolution custom image signature images under high motion tolerance conditions. It not only avoids the problem of classical pattern recognition relying on model experience and poor adaptability of the scene, but also avoids the problem of high computational complexity and recognition efficiency of directly deep learning methods such as YOLO. The deployment of the mobile robot instance deployment test shows that the average recognition rate is of 96.3% at a resolution of 640×480@Pixs and motion speed of 0.5 m/s, which proves the effectiveness of the present method.

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The Classification System uses a Support Vector Machine and a Decision Tree Method Based on X-Ray Images for Spinal Abnormalities

Ali,

Jusman

et al. 2023

2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS)

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Teeth Category Classification via Hu Moment Invariant and Extreme Learning Machine

Cited by 3 publications

References 34 publications

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

Research on Low-Resolution Pattern Coding Recognition Method Based on Hu-DBN

The Classification System uses a Support Vector Machine and a Decision Tree Method Based on X-Ray Images for Spinal Abnormalities

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