Feature recognition and strength estimation of chain links by 3D inspections

Sachan, Ankit; Cheok, Wei Wen Evan; Choo, Yoo Sang

doi:10.1016/j.oceaneng.2021.108785

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…If you want to obtain the sample set of knee bending motion visual sensor of ball motion frame image, you need to first obtain the knee bending motion trigger data from the massive frame images of ball motion continuous video stream and first disperse the continuous video stream according to the time features to obtain the ball motion frame image. After that, the ball motion frame image is classified according to the cluster, the dynamic difference and logical judgment are completed in the cluster, the dynamic active region image is obtained, and these regions are converted into time address/frame data [11,12].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

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Knee Bending Motion Extraction Algorithm for Ball Motion Image Using Vision Sensor

Yuan

Cao

et al. 2022

Wireless Communications and Mobile Computing

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To improve the extraction accuracy of knee bending motion in ball motion image and reduce the extraction distance error and time consumption, a knee bending motion extraction algorithm using visual sensor is proposed. The visual sensor model is constructed based on the ball motion frame image, the trigger data is output through differential and logical judgment, and these data are normalized to generate the visual sensor sample set of the ball motion frame image. The sample set is used as the input of the convolution neural network (CNN) and the sample basis of the motion energy model. The CNN extracts the features of the sample set in the convolution layer, the motion energy model is combined with the local binary pattern to extract the features of the sample set, the weighted summation method is used to fuse the two features, and the Softmax classifier is used to classify and extract the knee bending motion. The results show that the proposed algorithm has good ball motion image collection effect, the knee bending motion extraction accuracy is always maintained at about 98%, the distance error is low, and the time consumption of ball motion feature extraction is only 2.65 s, which has high application value in the field of sports.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Taking a as the central pixel, set the pixel as the threshold, compare the threshold with the gray value of adjacent pixels, and obtain the n-bit binary number. The value is calculated by Equation (11).…”

Section: Feature Extraction Based On Cnnmentioning

confidence: 99%

Knee Bending Motion Extraction Algorithm for Ball Motion Image Using Vision Sensor

Yuan

Cao

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…where x and y are the values of joint coordinates, and a ′ and b ′ are the values of the previous joint coordinates, which are the estimated angles. In addition, several contour features with good discrimination are extracted from each frame: contour area s, contour centroid ordinate R, maximum contour width f, contour height f p n ,(N−1) , and contour aspect ratio Z. e values extracted from the same feature in different frames can be expressed as time series [14]. Finally, we get 10 gait time series, which can well represent the features contained in gait video.…”

Section: Athlete Gait Feature Acquisition Based On Multisourcementioning

confidence: 99%

Athlete Gait Feature Recognition Method Based on Multisource Sensing Information

Xue

2022

Security and Communication Networks

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Aiming at the problem of low accuracy of two-dimensional gait recognition at present, a gait feature recognition method based on multisource sensing information is proposed. The multisource sensing information is combined to collect the athlete’s gait characteristics, collect the single frame gait image sequence of the human lower limbs during the movement, and extract the human body’s three-dimensional feature data during human walking by using the body structure and multisource sensing information, so as to realize the separation of the athlete’s gait image background. Finally, it is confirmed by experiments that the recognition rate of athlete gait feature recognition method based on multisource sensing information is significantly improved.

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“…Grey system is not the same as fuzzy mathematics. It attaches importance to the objects with clear extension and unclear extension, with larger cross-section and stronger permeability [13]. Deep belief neural network classification algorithm and Big Data analysis modeling first determine the mathematical relationship between many factors in the gray system, then extract and classify the differentiated English features of the text, and then match and analyze the information stored in the known database [14].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Liu

2021

Computational Intelligence and Neuroscience

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Realizing accurate recognition of Chinese and English information is a major difficulty in English feature recognition. Based on this difficulty, this paper studies the English feature recognition model based on deep belief network classification algorithm and Big Data analysis. First, the basic framework based on deep belief network classification algorithm and Big Data analysis is proposed. Combined with the Big Data analysis training model, the English feature information is processed. Through the recognition of different English text features, the recognition and matching of English features are realized. Then the errors of deep belief network classification algorithm and Big Data analysis are evaluated. Second, this paper describes the quantitative evaluation of deep belief network classification algorithm and Big Data analysis in this system. In the evaluation, the language feature evaluation method is used to improve the evaluation function. At the same time, the deep belief network classification algorithm and Big Data analysis are used to self-study the model, and the English feature recognition method with strong applicability is established. Finally, the effectiveness of the recognition system is verified by the experiment.

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Feature recognition and strength estimation of chain links by 3D inspections

Cited by 4 publications

References 8 publications

Knee Bending Motion Extraction Algorithm for Ball Motion Image Using Vision Sensor

Knee Bending Motion Extraction Algorithm for Ball Motion Image Using Vision Sensor

Athlete Gait Feature Recognition Method Based on Multisource Sensing Information

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

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