A structured light image quality evaluation method based on no-reference quality assessment

Ji, Wenzhao; Xue, Quan; Sun, Xingming; Sun, Peng

doi:10.1088/1742-6596/1914/1/012005

Cited by 2 publications

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“…In order to evaluate the quality of English teaching in schools and study the correlation between teaching quality and English knowledge points, we have constructed a combined evaluation system of the decision tree and rule association analysis of knowledge points. This paper expounds that this method is efficient in detecting large error data in fringe images [ 1 ] and uses artificial intelligence technology to evaluate the quality of surgery in medicine [ 2 ]. It expounds the anatomical analysis of the components of kidney stones by the deep learning method [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Correlation Analysis between the Evaluation Score of English Teaching Quality and the Knowledge Points

2022

Computational Intelligence and Neuroscience

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As one of the three main courses from primary school to senior high school, improving the quality of English teaching in and out of class has become the top priority of colleges and universities. English knowledge points are complex, and domestic scholars have studied vocabulary knowledge and grammatical awareness from various perspectives, but there is still a lack of research on the correlation between vocabulary knowledge, grammatical awareness, and cloze test scores of senior high school students. Therefore, this paper carries out empirical research from the depth of English vocabulary, English grammar, reading comprehension, cloze test, and composition, aiming at exploring the relationship between teaching quality and English knowledge points. Classroom teaching quality evaluation is the basic content of education quality evaluation, which not only needs to evaluate the effect of class hours but also needs a student's long-term learning effect. In order to improve the quality of classroom English teaching, enrich the content of classroom English, and ultimately make the quality of students' learning to a higher level, the combination model of the teaching quality evaluation index is established by combining the decision tree with the knowledge point rule association method and the evaluation results are verified by association rule analysis. This paper selects the effective indicators that affect the evaluation of English teaching quality, determines the weight of the indicators by using the analytic hierarchy process, effectively constructs the combination model of the decision tree and rule association method, and establishes the evaluation model of students' English learning ability in the classroom. Taking students as the main object and combining them with the requirements of digital teaching, the evaluation index system is formed, the index weight is determined by using the analytic hierarchy process, and the classroom teaching quality evaluation model based on the decision tree and English knowledge point correlation analysis is constructed to truly reflect the teaching level of teachers, and the correlation analysis is carried out between English teachers' own quality and students' learning effects and knowledge points. By testing the model performance of English vocabulary depth, reading comprehension, grammar, writing, and other knowledge points, we can well evaluate and analyze students' mastery of English learning and the correlation of English knowledge points.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Correlation Analysis between the Evaluation Score of English Teaching Quality and the Knowledge Points

2022

Computational Intelligence and Neuroscience

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Research on Automatic Error Data Recognition Method for Structured Light System Based on Residual Neural Network

et al. 2023

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In a structured light system, the positioning accuracy of the stripe is one of the determinants of measurement accuracy. However, the quality of the structured light stripe is reduced by noise, object shape, color, etc. The positioning accuracy of the low-quality stripe center will be decreased, and the large error will be introduced into measurement results, which can only be recognized by a human. To address this problem, this paper proposes a method to identify data with relatively large errors in 3D measurement results by evaluating the quality of the grayscale distribution of stripes. In this method, the undegraded and degraded stripe images are captured. Then, the residual neural network is trained using the grayscale distribution of the two types of stripes. The captured stripes are classified by the trained model. Finally, the data corresponding to the degraded stripes, which correspond to large errors in the data, can be identified according to the classified results. The experiment shows that the algorithm proposed in this paper can effectively identify the data with large errors automatically.

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A structured light image quality evaluation method based on no-reference quality assessment

Cited by 2 publications

References 1 publication

Deep Learning-Based Correlation Analysis between the Evaluation Score of English Teaching Quality and the Knowledge Points

Deep Learning-Based Correlation Analysis between the Evaluation Score of English Teaching Quality and the Knowledge Points

Research on Automatic Error Data Recognition Method for Structured Light System Based on Residual Neural Network

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