Pattern Recognition of Construction Bidding System Based on Image Processing

Zhang, Xianzhe; Zhou, Sgheng; Fang, Jun; Ni, Yanling

doi:10.32604/csse.2020.35.247

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…In recent years, with the rapid development of neural networks, methods of deep learning made major breakthroughs in the field of image recognition [27,28], speech recognition [29] and natural language processing [30,31]. In NER, the appearance of BiLSTM-CRF [32] opened the prelude of the deep learning era of NER.…”

Section: Recognition Methods Based On Deep Learningmentioning

confidence: 99%

A review of Chinese named entity recognition

Cheng

Liu

et al. 2021

KSII TIIS

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Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

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Section: Recognition Methods Based On Deep Learningmentioning

confidence: 99%

A review of Chinese named entity recognition

Cheng

Liu

et al. 2021

KSII TIIS

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“…The phase calculation [5][6][7] of the collected fringe image can realize reconstruction of the three-dimensional contour of the object. However, due to the influence of instrument design, gamma nonlinear distortion [8] between the projector and camera will produce measurement phase errors, and the collected grating fringes will not be an ideal cosine function, but a function with certain distortion. To improve the accuracy of phase calculation requires compensation for the phase error caused by the gamma nonlinear distortion.…”

Section: Prefacementioning

confidence: 99%

Phase Error Compensation of Three-Dimensional Reconstruction Combined with Hilbert Transform

Zhang¹,

Shen²,

Wu³

2021

Computers, Materials &Amp; Continua

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Nonlinear response is an important factor affecting the accuracy of three-dimensional image measurement based on the fringe structured light method. A phase compensation algorithm combined with a Hilbert transform is proposed to reduce the phase error caused by the nonlinear response of a digital projector in the three-dimensional measurement system of fringe structured light. According to the analysis of the influence of Gamma distortion on the phase calculation, the algorithm establishes the relationship model between phase error and harmonic coefficient, introduces phase shift to the signal, and keeps the signal amplitude constant while filtering out the DC component. The phase error is converted to the transform domain, and compared with the numeric value in the space domain. The algorithm is combined with a spiral phase function to optimize the Hilbert transform, so as to eliminate external noise, enhance the image quality, and get an accurate phase value. Experimental results show that the proposed method can effectively improve the accuracy and speed of phase measurement. By performing phase error compensation for free-form surface objects, the phase error is reduced by about 26%, and about 27% of the image reconstruction time is saved, which further demonstrates the feasibility and effectiveness of the method.

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Confidential information protection method of commercial information physical system based on edge computing

Zhang

2020

Neural Comput & Applic

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Pattern Recognition of Construction Bidding System Based on Image Processing

Cited by 5 publications

References 9 publications

A review of Chinese named entity recognition

A review of Chinese named entity recognition

Phase Error Compensation of Three-Dimensional Reconstruction Combined with Hilbert Transform

Confidential information protection method of commercial information physical system based on edge computing

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